Image Classifier Project.html

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

<title>Image Classifier Project</title>

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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; }

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<style type="text/css">
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<h1 id="Developing-an-AI-application">Developing an AI application<a class="anchor-link" href="#Developing-an-AI-application">&#182;</a></h1><p>Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.</p>
<p>In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using <a href="http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html">this dataset</a> of 102 flower categories, you can see a few examples below.</p>
<p><img src='assets/Flowers.png' width=500px></p>
<p>The project is broken down into multiple steps:</p>
<ul>
<li>Load and preprocess the image dataset</li>
<li>Train the image classifier on your dataset</li>
<li>Use the trained classifier to predict image content</li>
</ul>
<p>We'll lead you through each part which you'll implement in Python.</p>
<p>When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.</p>
<p>First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">models</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">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span><span class="n">transforms</span>
<span class="kn">from</span> <span class="nn">workspace_utils</span> <span class="k">import</span> <span class="n">active_session</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">OrderedDict</span>
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<h2 id="Load-the-data">Load the data<a class="anchor-link" href="#Load-the-data">&#182;</a></h2><p>Here you'll use <code>torchvision</code> to load the data (<a href="http://pytorch.org/docs/0.3.0/torchvision/index.html">documentation</a>). The data should be included alongside this notebook, otherwise you can <a href="https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz">download it here</a>. The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.</p>
<p>The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.</p>
<p>The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;flowers&#39;</span>
<span class="n">train_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/train&#39;</span>
<span class="n">valid_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/valid&#39;</span>
<span class="n">test_dir</span> <span class="o">=</span> <span class="n">data_dir</span> <span class="o">+</span> <span class="s1">&#39;/test&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Define your transforms for the training, validation, and testing sets</span>
<span class="n">data_transforms</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;train&#39;</span> <span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="n">size</span> <span class="o">=</span> <span class="mi">224</span><span class="p">),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="p">]),</span>
    
    <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="n">size</span> <span class="o">=</span> <span class="mi">224</span><span class="p">),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="p">]),</span>
    
    <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="n">size</span> <span class="o">=</span> <span class="mi">224</span><span class="p">),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
        <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="p">])</span>
<span class="p">}</span>

<span class="c1"># TODO: Load the datasets with ImageFolder</span>
<span class="n">image_datasets</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">train_dir</span><span class="p">,</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">])</span>
<span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">valid_dir</span><span class="p">,</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">])</span>
<span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="n">test_dir</span><span class="p">,</span> <span class="n">transform</span> <span class="o">=</span> <span class="n">data_transforms</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">])</span>

<span class="c1"># TODO: Using the image datasets and the trainforms, define the dataloaders</span>
<span class="c1">#dataloaders = </span>
<span class="n">train_loaders</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">valid_loaders</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">],</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">test_loaders</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">],</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>                                                     
                                                     
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">class_to_idx</span><span class="p">)</span>
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<pre>102</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">train_loaders</span><span class="p">))</span>
<span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span>
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<pre>tensor([[[ 2.0263,  2.0092,  2.0092,  ..., -0.5767, -0.6452, -0.6965],
         [ 2.0092,  1.9920,  1.9578,  ..., -0.6281, -0.6794, -0.7137],
         [ 1.9749,  1.9578,  1.9235,  ..., -0.6281, -0.6623, -0.6794],
         ...,
         [-0.7993, -0.8678, -0.9192,  ..., -2.0323, -2.0494, -2.0494],
         [-1.0390, -1.0219, -0.9877,  ..., -2.0494, -2.0665, -2.0665],
         [-1.0048, -0.9363, -0.8849,  ..., -2.0494, -2.0665, -2.0837]],

        [[ 2.0434,  2.0609,  2.0609,  ..., -0.3550, -0.4251, -0.4426],
         [ 2.0609,  2.0609,  2.0434,  ..., -0.4076, -0.4426, -0.4601],
         [ 2.0259,  2.0434,  2.0259,  ..., -0.4076, -0.4426, -0.4601],
         ...,
         [-0.4426, -0.5126, -0.5651,  ..., -1.7556, -1.7731, -1.7731],
         [-0.7227, -0.7052, -0.6877,  ..., -1.7731, -1.7906, -1.7906],
         [-0.7227, -0.6702, -0.6352,  ..., -1.7731, -1.7906, -1.8081]],

        [[-0.0964, -0.0964, -0.0615,  ..., -1.8044, -1.8044, -1.8044],
         [-0.1138, -0.1138, -0.1138,  ..., -1.8044, -1.8044, -1.8044],
         [-0.1312, -0.1138, -0.1138,  ..., -1.8044, -1.8044, -1.8044],
         ...,
         [-1.7522, -1.7696, -1.7870,  ..., -1.8044, -1.8044, -1.8044],
         [-1.7696, -1.6999, -1.6476,  ..., -1.8044, -1.8044, -1.8044],
         [-1.5430, -1.4210, -1.3339,  ..., -1.8044, -1.8044, -1.8044]]])</pre>
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<h3 id="Label-mapping">Label mapping<a class="anchor-link" href="#Label-mapping">&#182;</a></h3><p>You'll also need to load in a mapping from category label to category name. You can find this in the file <code>cat_to_name.json</code>. It's a JSON object which you can read in with the <a href="https://docs.python.org/2/library/json.html"><code>json</code> module</a>. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.</p>

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

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;cat_to_name.json&#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">f</span><span class="p">:</span>
    <span class="n">cat_to_name</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
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<h1 id="Building-and-training-the-classifier">Building and training the classifier<a class="anchor-link" href="#Building-and-training-the-classifier">&#182;</a></h1><p>Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from <code>torchvision.models</code> to get the image features. Build and train a new feed-forward classifier using those features.</p>
<p>We're going to leave this part up to you. Refer to <a href="https://review.udacity.com/#!/rubrics/1663/view">the rubric</a> for guidance on successfully completing this section. Things you'll need to do:</p>
<ul>
<li>Load a <a href="http://pytorch.org/docs/master/torchvision/models.html">pre-trained network</a> (If you need a starting point, the VGG networks work great and are straightforward to use)</li>
<li>Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout</li>
<li>Train the classifier layers using backpropagation using the pre-trained network to get the features</li>
<li>Track the loss and accuracy on the validation set to determine the best hyperparameters</li>
</ul>
<p>We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!</p>
<p>When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.</p>
<p>One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to
GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.</p>
<p><font color='red'>**Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again.</font></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Build and train your network</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>

<span class="c1">#when a model is loaded in pytorch, by default requires_grad is TRUE for all parameters</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
    <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span>
    
<span class="n">dropout</span> <span class="o">=</span> <span class="mf">0.5</span>

<span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([</span>
    <span class="p">(</span><span class="s1">&#39;dropout1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)),</span>
    <span class="p">(</span><span class="s1">&#39;hidden_layer1&#39;</span><span class="p">,</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">)),</span>
    <span class="p">(</span><span class="s1">&#39;relu1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()),</span>
    <span class="p">(</span><span class="s1">&#39;dropout2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span><span class="mi">102</span><span class="p">)),</span>
    <span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span><span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="p">]))</span>

<span class="n">model</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>

<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
    <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
    
<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">)</span>
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<pre>/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
  nn.init.kaiming_normal(m.weight.data)
Downloading: &#34;https://download.pytorch.org/models/densenet121-a639ec97.pth&#34; to /root/.torch/models/densenet121-a639ec97.pth
100%|██████████| 32342954/32342954 [00:00&lt;00:00, 62250907.37it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Do validation on the test set</span>
<span class="k">def</span> <span class="nf">evaluate_</span><span class="p">(</span><span class="n">data_loaders</span><span class="p">):</span>    
    <span class="n">correct_cnt</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span> 
    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda:0&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">for</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="n">data_loaders</span><span class="p">:</span>
            
            <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
            
            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
            <span class="n">ps</span><span class="p">,</span> <span class="n">predicted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
            
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            
            <span class="n">total</span> <span class="o">+=</span> <span class="n">labels</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
            <span class="n">correct_cnt</span> <span class="o">+=</span> <span class="p">(</span><span class="n">predicted</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

            <span class="n">accuracy</span> <span class="o">=</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">correct_cnt</span> <span class="o">/</span> <span class="n">total</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">loss</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span>
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<pre>DenseNet(
  (features): Sequential(
    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu0): ReLU(inplace)
    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (denseblock1): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition1): _Transition(
      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock2): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition2): _Transition(
      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock3): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer17): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer18): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer19): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer20): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer21): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer22): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer23): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer24): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition3): _Transition(
      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock4): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (classifier): Sequential(
    (dropout1): Dropout(p=0.1)
    (hidden_layer1): Linear(in_features=1024, out_features=512, bias=True)
    (relu1): ReLU()
    (dropout2): Linear(in_features=512, out_features=102, bias=True)
    (output): LogSoftmax()
  )
)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">epochs</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">print_every</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">steps</span> <span class="o">=</span> <span class="mi">0</span> 
<span class="n">accuracy_train</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>


<span class="k">with</span> <span class="n">active_session</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">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
        
        <span class="k">for</span> <span class="n">inputs</span><span class="p">,</span><span class="n">labels</span> <span class="ow">in</span> <span class="nb">iter</span><span class="p">(</span><span class="n">train_loaders</span><span class="p">):</span>
            <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
            <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>

            <span class="n">steps</span> <span class="o">+=</span> <span class="mi">1</span> 
            
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span><span class="n">labels</span><span class="p">)</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>    
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
            <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
            <span class="n">correct_cnts</span> <span class="o">=</span> <span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">data</span> <span class="o">==</span> <span class="n">ps</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
            <span class="n">accuracy_train</span> <span class="o">+=</span> <span class="n">correct_cnts</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
            
            <span class="k">if</span> <span class="n">steps</span> <span class="o">%</span> <span class="n">print_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
                <span class="n">validation_accuracy</span><span class="p">,</span> <span class="n">validation_loss</span> <span class="o">=</span> <span class="n">evaluate_</span><span class="p">(</span><span class="n">valid_loaders</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">/</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="n">epochs</span><span class="p">),</span>
                     <span class="s2">&quot;Training Loss: </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">running_loss</span> <span class="o">/</span> <span class="n">print_every</span><span class="p">),</span>
                      <span class="s2">&quot;Training 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">accuracy_train</span><span class="o">/</span> <span class="n">print_every</span><span class="p">)</span><span class="o">*</span><span class="mi">100</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="s2">&quot;Validation Loss: </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_loss</span><span class="p">))</span>
                      
                <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
                <span class="n">accuracy_train</span> <span class="o">=</span> <span class="mi">0</span>
                <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
    
</pre></div>

    </div>
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<pre>Epoch 1/20:  Training Loss: 4.536 Training Accuracy: 3.281 Validation Accuracy: 6.250 Validation Loss: 4.211
Epoch 1/20:  Training Loss: 4.176 Training Accuracy: 11.250 Validation Accuracy: 14.062 Validation Loss: 4.060
Epoch 1/20:  Training Loss: 3.816 Training Accuracy: 22.656 Validation Accuracy: 28.125 Validation Loss: 3.673
Epoch 1/20:  Training Loss: 3.381 Training Accuracy: 33.750 Validation Accuracy: 32.812 Validation Loss: 3.189
Epoch 1/20:  Training Loss: 3.041 Training Accuracy: 38.125 Validation Accuracy: 42.188 Validation Loss: 2.684
Epoch 1/20:  Training Loss: 2.532 Training Accuracy: 48.906 Validation Accuracy: 46.875 Validation Loss: 2.181
Epoch 1/20:  Training Loss: 2.340 Training Accuracy: 48.906 Validation Accuracy: 71.875 Validation Loss: 1.566
Epoch 1/20:  Training Loss: 2.012 Training Accuracy: 59.688 Validation Accuracy: 70.312 Validation Loss: 1.589
Epoch 1/20:  Training Loss: 1.833 Training Accuracy: 63.281 Validation Accuracy: 71.875 Validation Loss: 1.357
Epoch 1/20:  Training Loss: 1.603 Training Accuracy: 63.594 Validation Accuracy: 71.875 Validation Loss: 1.355
Epoch 2/20:  Training Loss: 1.500 Training Accuracy: 66.198 Validation Accuracy: 85.938 Validation Loss: 0.947
Epoch 2/20:  Training Loss: 1.387 Training Accuracy: 69.062 Validation Accuracy: 75.000 Validation Loss: 1.022
Epoch 2/20:  Training Loss: 1.133 Training Accuracy: 74.219 Validation Accuracy: 84.375 Validation Loss: 0.734
Epoch 2/20:  Training Loss: 1.181 Training Accuracy: 73.906 Validation Accuracy: 82.812 Validation Loss: 0.782
Epoch 2/20:  Training Loss: 1.023 Training Accuracy: 77.344 Validation Accuracy: 85.938 Validation Loss: 0.738
Epoch 2/20:  Training Loss: 1.032 Training Accuracy: 76.562 Validation Accuracy: 84.375 Validation Loss: 0.688
Epoch 2/20:  Training Loss: 0.977 Training Accuracy: 77.656 Validation Accuracy: 84.375 Validation Loss: 0.702
Epoch 2/20:  Training Loss: 0.949 Training Accuracy: 77.812 Validation Accuracy: 79.688 Validation Loss: 0.791
Epoch 2/20:  Training Loss: 0.980 Training Accuracy: 78.438 Validation Accuracy: 85.938 Validation Loss: 0.594
Epoch 2/20:  Training Loss: 0.934 Training Accuracy: 75.938 Validation Accuracy: 87.500 Validation Loss: 0.559
Epoch 3/20:  Training Loss: 0.869 Training Accuracy: 80.677 Validation Accuracy: 89.062 Validation Loss: 0.459
Epoch 3/20:  Training Loss: 0.822 Training Accuracy: 80.312 Validation Accuracy: 84.375 Validation Loss: 0.553
Epoch 3/20:  Training Loss: 0.676 Training Accuracy: 84.062 Validation Accuracy: 87.500 Validation Loss: 0.527
Epoch 3/20:  Training Loss: 0.776 Training Accuracy: 80.938 Validation Accuracy: 87.500 Validation Loss: 0.385
Epoch 3/20:  Training Loss: 0.714 Training Accuracy: 84.375 Validation Accuracy: 90.625 Validation Loss: 0.446
Epoch 3/20:  Training Loss: 0.702 Training Accuracy: 82.812 Validation Accuracy: 90.625 Validation Loss: 0.360
Epoch 3/20:  Training Loss: 0.695 Training Accuracy: 82.344 Validation Accuracy: 95.312 Validation Loss: 0.271
Epoch 3/20:  Training Loss: 0.644 Training Accuracy: 83.750 Validation Accuracy: 92.188 Validation Loss: 0.432
Epoch 3/20:  Training Loss: 0.719 Training Accuracy: 82.656 Validation Accuracy: 79.688 Validation Loss: 0.638
Epoch 3/20:  Training Loss: 0.656 Training Accuracy: 82.812 Validation Accuracy: 93.750 Validation Loss: 0.297
Epoch 4/20:  Training Loss: 0.636 Training Accuracy: 84.115 Validation Accuracy: 93.750 Validation Loss: 0.351
Epoch 4/20:  Training Loss: 0.588 Training Accuracy: 85.156 Validation Accuracy: 90.625 Validation Loss: 0.342
Epoch 4/20:  Training Loss: 0.647 Training Accuracy: 82.656 Validation Accuracy: 95.312 Validation Loss: 0.425
Epoch 4/20:  Training Loss: 0.598 Training Accuracy: 84.531 Validation Accuracy: 87.500 Validation Loss: 0.469
Epoch 4/20:  Training Loss: 0.549 Training Accuracy: 86.250 Validation Accuracy: 92.188 Validation Loss: 0.236
Epoch 4/20:  Training Loss: 0.595 Training Accuracy: 84.688 Validation Accuracy: 92.188 Validation Loss: 0.328
Epoch 4/20:  Training Loss: 0.556 Training Accuracy: 86.719 Validation Accuracy: 93.750 Validation Loss: 0.241
Epoch 4/20:  Training Loss: 0.580 Training Accuracy: 84.531 Validation Accuracy: 87.500 Validation Loss: 0.440
Epoch 4/20:  Training Loss: 0.558 Training Accuracy: 84.688 Validation Accuracy: 89.062 Validation Loss: 0.296
Epoch 4/20:  Training Loss: 0.556 Training Accuracy: 85.000 Validation Accuracy: 90.625 Validation Loss: 0.311
Epoch 4/20:  Training Loss: 0.661 Training Accuracy: 82.500 Validation Accuracy: 89.062 Validation Loss: 0.293
Epoch 5/20:  Training Loss: 0.487 Training Accuracy: 87.344 Validation Accuracy: 90.625 Validation Loss: 0.319
Epoch 5/20:  Training Loss: 0.523 Training Accuracy: 85.156 Validation Accuracy: 98.438 Validation Loss: 0.173
Epoch 5/20:  Training Loss: 0.539 Training Accuracy: 87.188 Validation Accuracy: 89.062 Validation Loss: 0.448
Epoch 5/20:  Training Loss: 0.492 Training Accuracy: 87.969 Validation Accuracy: 87.500 Validation Loss: 0.380
Epoch 5/20:  Training Loss: 0.549 Training Accuracy: 87.812 Validation Accuracy: 87.500 Validation Loss: 0.340
Epoch 5/20:  Training Loss: 0.447 Training Accuracy: 88.750 Validation Accuracy: 92.188 Validation Loss: 0.192
Epoch 5/20:  Training Loss: 0.493 Training Accuracy: 87.344 Validation Accuracy: 96.875 Validation Loss: 0.215
Epoch 5/20:  Training Loss: 0.510 Training Accuracy: 86.250 Validation Accuracy: 93.750 Validation Loss: 0.258
Epoch 5/20:  Training Loss: 0.559 Training Accuracy: 83.906 Validation Accuracy: 93.750 Validation Loss: 0.204
Epoch 5/20:  Training Loss: 0.548 Training Accuracy: 85.000 Validation Accuracy: 90.625 Validation Loss: 0.367
Epoch 6/20:  Training Loss: 0.441 Training Accuracy: 88.646 Validation Accuracy: 84.375 Validation Loss: 0.682
Epoch 6/20:  Training Loss: 0.499 Training Accuracy: 87.344 Validation Accuracy: 89.062 Validation Loss: 0.650
Epoch 6/20:  Training Loss: 0.479 Training Accuracy: 86.562 Validation Accuracy: 96.875 Validation Loss: 0.476
Epoch 6/20:  Training Loss: 0.506 Training Accuracy: 86.719 Validation Accuracy: 92.188 Validation Loss: 0.238
Epoch 6/20:  Training Loss: 0.519 Training Accuracy: 85.625 Validation Accuracy: 89.062 Validation Loss: 0.374
Epoch 6/20:  Training Loss: 0.426 Training Accuracy: 88.281 Validation Accuracy: 82.812 Validation Loss: 0.508
Epoch 6/20:  Training Loss: 0.463 Training Accuracy: 87.812 Validation Accuracy: 96.875 Validation Loss: 0.154
Epoch 6/20:  Training Loss: 0.410 Training Accuracy: 90.156 Validation Accuracy: 92.188 Validation Loss: 0.236
Epoch 6/20:  Training Loss: 0.431 Training Accuracy: 87.969 Validation Accuracy: 95.312 Validation Loss: 0.123
Epoch 6/20:  Training Loss: 0.523 Training Accuracy: 87.031 Validation Accuracy: 93.750 Validation Loss: 0.261
Epoch 7/20:  Training Loss: 0.467 Training Accuracy: 87.344 Validation Accuracy: 95.312 Validation Loss: 0.254
Epoch 7/20:  Training Loss: 0.496 Training Accuracy: 85.781 Validation Accuracy: 100.000 Validation Loss: 0.083
Epoch 7/20:  Training Loss: 0.369 Training Accuracy: 90.000 Validation Accuracy: 95.312 Validation Loss: 0.210
Epoch 7/20:  Training Loss: 0.447 Training Accuracy: 87.500 Validation Accuracy: 84.375 Validation Loss: 0.313
Epoch 7/20:  Training Loss: 0.417 Training Accuracy: 88.750 Validation Accuracy: 95.312 Validation Loss: 0.166
Epoch 7/20:  Training Loss: 0.406 Training Accuracy: 90.469 Validation Accuracy: 90.625 Validation Loss: 0.264
Epoch 7/20:  Training Loss: 0.429 Training Accuracy: 89.531 Validation Accuracy: 92.188 Validation Loss: 0.218
Epoch 7/20:  Training Loss: 0.386 Training Accuracy: 89.062 Validation Accuracy: 96.875 Validation Loss: 0.162
Epoch 7/20:  Training Loss: 0.355 Training Accuracy: 90.312 Validation Accuracy: 96.875 Validation Loss: 0.151
Epoch 7/20:  Training Loss: 0.493 Training Accuracy: 86.250 Validation Accuracy: 92.188 Validation Loss: 0.175
Epoch 7/20:  Training Loss: 0.513 Training Accuracy: 87.188 Validation Accuracy: 95.312 Validation Loss: 0.193
Epoch 8/20:  Training Loss: 0.404 Training Accuracy: 88.698 Validation Accuracy: 95.312 Validation Loss: 0.227
Epoch 8/20:  Training Loss: 0.409 Training Accuracy: 88.906 Validation Accuracy: 87.500 Validation Loss: 0.594
Epoch 8/20:  Training Loss: 0.437 Training Accuracy: 87.656 Validation Accuracy: 95.312 Validation Loss: 0.189
Epoch 8/20:  Training Loss: 0.356 Training Accuracy: 91.250 Validation Accuracy: 90.625 Validation Loss: 0.387
Epoch 8/20:  Training Loss: 0.353 Training Accuracy: 91.250 Validation Accuracy: 90.625 Validation Loss: 0.306
Epoch 8/20:  Training Loss: 0.393 Training Accuracy: 89.219 Validation Accuracy: 95.312 Validation Loss: 0.232
Epoch 8/20:  Training Loss: 0.441 Training Accuracy: 88.125 Validation Accuracy: 98.438 Validation Loss: 0.088
Epoch 8/20:  Training Loss: 0.417 Training Accuracy: 87.812 Validation Accuracy: 93.750 Validation Loss: 0.298
Epoch 8/20:  Training Loss: 0.323 Training Accuracy: 91.562 Validation Accuracy: 95.312 Validation Loss: 0.191
Epoch 8/20:  Training Loss: 0.430 Training Accuracy: 88.438 Validation Accuracy: 96.875 Validation Loss: 0.096
Epoch 9/20:  Training Loss: 0.424 Training Accuracy: 88.125 Validation Accuracy: 92.188 Validation Loss: 0.190
Epoch 9/20:  Training Loss: 0.345 Training Accuracy: 90.156 Validation Accuracy: 93.750 Validation Loss: 0.243
Epoch 9/20:  Training Loss: 0.427 Training Accuracy: 87.500 Validation Accuracy: 92.188 Validation Loss: 0.376
Epoch 9/20:  Training Loss: 0.371 Training Accuracy: 89.844 Validation Accuracy: 92.188 Validation Loss: 0.243
Epoch 9/20:  Training Loss: 0.365 Training Accuracy: 89.531 Validation Accuracy: 98.438 Validation Loss: 0.091
Epoch 9/20:  Training Loss: 0.372 Training Accuracy: 90.781 Validation Accuracy: 90.625 Validation Loss: 0.198
Epoch 9/20:  Training Loss: 0.354 Training Accuracy: 90.312 Validation Accuracy: 90.625 Validation Loss: 0.265
Epoch 9/20:  Training Loss: 0.402 Training Accuracy: 88.906 Validation Accuracy: 93.750 Validation Loss: 0.240
Epoch 9/20:  Training Loss: 0.371 Training Accuracy: 90.312 Validation Accuracy: 92.188 Validation Loss: 0.281
Epoch 9/20:  Training Loss: 0.395 Training Accuracy: 90.000 Validation Accuracy: 98.438 Validation Loss: 0.080
Epoch 10/20:  Training Loss: 0.387 Training Accuracy: 89.583 Validation Accuracy: 95.312 Validation Loss: 0.242
Epoch 10/20:  Training Loss: 0.402 Training Accuracy: 89.375 Validation Accuracy: 89.062 Validation Loss: 0.308
Epoch 10/20:  Training Loss: 0.324 Training Accuracy: 90.469 Validation Accuracy: 90.625 Validation Loss: 0.187
Epoch 10/20:  Training Loss: 0.346 Training Accuracy: 89.531 Validation Accuracy: 96.875 Validation Loss: 0.099
Epoch 10/20:  Training Loss: 0.396 Training Accuracy: 89.844 Validation Accuracy: 95.312 Validation Loss: 0.167
Epoch 10/20:  Training Loss: 0.398 Training Accuracy: 89.844 Validation Accuracy: 98.438 Validation Loss: 0.107
Epoch 10/20:  Training Loss: 0.363 Training Accuracy: 90.000 Validation Accuracy: 95.312 Validation Loss: 0.228
Epoch 10/20:  Training Loss: 0.293 Training Accuracy: 92.031 Validation Accuracy: 96.875 Validation Loss: 0.078
Epoch 10/20:  Training Loss: 0.396 Training Accuracy: 89.062 Validation Accuracy: 95.312 Validation Loss: 0.108
Epoch 10/20:  Training Loss: 0.428 Training Accuracy: 88.281 Validation Accuracy: 90.625 Validation Loss: 0.303
Epoch 10/20:  Training Loss: 0.403 Training Accuracy: 89.375 Validation Accuracy: 93.750 Validation Loss: 0.179
Epoch 11/20:  Training Loss: 0.336 Training Accuracy: 91.094 Validation Accuracy: 93.750 Validation Loss: 0.186
Epoch 11/20:  Training Loss: 0.384 Training Accuracy: 89.062 Validation Accuracy: 96.875 Validation Loss: 0.142
Epoch 11/20:  Training Loss: 0.401 Training Accuracy: 87.969 Validation Accuracy: 93.750 Validation Loss: 0.274
Epoch 11/20:  Training Loss: 0.385 Training Accuracy: 90.312 Validation Accuracy: 98.438 Validation Loss: 0.082
Epoch 11/20:  Training Loss: 0.369 Training Accuracy: 90.156 Validation Accuracy: 93.750 Validation Loss: 0.188
Epoch 11/20:  Training Loss: 0.350 Training Accuracy: 88.750 Validation Accuracy: 100.000 Validation Loss: 0.039
Epoch 11/20:  Training Loss: 0.391 Training Accuracy: 89.531 Validation Accuracy: 95.312 Validation Loss: 0.146
Epoch 11/20:  Training Loss: 0.337 Training Accuracy: 90.625 Validation Accuracy: 93.750 Validation Loss: 0.179
Epoch 11/20:  Training Loss: 0.383 Training Accuracy: 90.000 Validation Accuracy: 90.625 Validation Loss: 0.304
Epoch 11/20:  Training Loss: 0.343 Training Accuracy: 91.250 Validation Accuracy: 95.312 Validation Loss: 0.143
Epoch 12/20:  Training Loss: 0.466 Training Accuracy: 88.333 Validation Accuracy: 93.750 Validation Loss: 0.160
Epoch 12/20:  Training Loss: 0.341 Training Accuracy: 91.094 Validation Accuracy: 93.750 Validation Loss: 0.244
Epoch 12/20:  Training Loss: 0.350 Training Accuracy: 89.844 Validation Accuracy: 93.750 Validation Loss: 0.194
Epoch 12/20:  Training Loss: 0.324 Training Accuracy: 92.344 Validation Accuracy: 95.312 Validation Loss: 0.171
Epoch 12/20:  Training Loss: 0.339 Training Accuracy: 90.156 Validation Accuracy: 90.625 Validation Loss: 0.244
Epoch 12/20:  Training Loss: 0.359 Training Accuracy: 89.844 Validation Accuracy: 93.750 Validation Loss: 0.203
Epoch 12/20:  Training Loss: 0.435 Training Accuracy: 89.062 Validation Accuracy: 92.188 Validation Loss: 0.269
Epoch 12/20:  Training Loss: 0.314 Training Accuracy: 91.562 Validation Accuracy: 96.875 Validation Loss: 0.155
Epoch 12/20:  Training Loss: 0.389 Training Accuracy: 87.656 Validation Accuracy: 95.312 Validation Loss: 0.158
Epoch 12/20:  Training Loss: 0.365 Training Accuracy: 90.469 Validation Accuracy: 96.875 Validation Loss: 0.183
Epoch 13/20:  Training Loss: 0.364 Training Accuracy: 90.000 Validation Accuracy: 93.750 Validation Loss: 0.159
Epoch 13/20:  Training Loss: 0.359 Training Accuracy: 91.250 Validation Accuracy: 100.000 Validation Loss: 0.036
Epoch 13/20:  Training Loss: 0.356 Training Accuracy: 91.094 Validation Accuracy: 95.312 Validation Loss: 0.193
Epoch 13/20:  Training Loss: 0.271 Training Accuracy: 92.812 Validation Accuracy: 92.188 Validation Loss: 0.336
Epoch 13/20:  Training Loss: 0.335 Training Accuracy: 91.094 Validation Accuracy: 89.062 Validation Loss: 0.312
Epoch 13/20:  Training Loss: 0.324 Training Accuracy: 90.469 Validation Accuracy: 96.875 Validation Loss: 0.108
Epoch 13/20:  Training Loss: 0.355 Training Accuracy: 90.938 Validation Accuracy: 93.750 Validation Loss: 0.215
Epoch 13/20:  Training Loss: 0.316 Training Accuracy: 91.406 Validation Accuracy: 96.875 Validation Loss: 0.518
Epoch 13/20:  Training Loss: 0.362 Training Accuracy: 91.094 Validation Accuracy: 95.312 Validation Loss: 0.217
Epoch 13/20:  Training Loss: 0.351 Training Accuracy: 90.625 Validation Accuracy: 95.312 Validation Loss: 0.205
Epoch 14/20:  Training Loss: 0.362 Training Accuracy: 88.594 Validation Accuracy: 93.750 Validation Loss: 0.143
Epoch 14/20:  Training Loss: 0.334 Training Accuracy: 90.156 Validation Accuracy: 92.188 Validation Loss: 0.195
Epoch 14/20:  Training Loss: 0.345 Training Accuracy: 89.844 Validation Accuracy: 92.188 Validation Loss: 0.209
Epoch 14/20:  Training Loss: 0.298 Training Accuracy: 91.562 Validation Accuracy: 87.500 Validation Loss: 0.767
Epoch 14/20:  Training Loss: 0.332 Training Accuracy: 90.625 Validation Accuracy: 93.750 Validation Loss: 0.185
Epoch 14/20:  Training Loss: 0.312 Training Accuracy: 91.875 Validation Accuracy: 95.312 Validation Loss: 0.150
Epoch 14/20:  Training Loss: 0.323 Training Accuracy: 90.625 Validation Accuracy: 96.875 Validation Loss: 0.133
Epoch 14/20:  Training Loss: 0.259 Training Accuracy: 93.281 Validation Accuracy: 95.312 Validation Loss: 0.138
Epoch 14/20:  Training Loss: 0.383 Training Accuracy: 89.844 Validation Accuracy: 92.188 Validation Loss: 0.153
Epoch 14/20:  Training Loss: 0.369 Training Accuracy: 90.469 Validation Accuracy: 96.875 Validation Loss: 0.212
Epoch 14/20:  Training Loss: 0.319 Training Accuracy: 90.312 Validation Accuracy: 95.312 Validation Loss: 0.189
Epoch 15/20:  Training Loss: 0.282 Training Accuracy: 92.708 Validation Accuracy: 93.750 Validation Loss: 0.156
Epoch 15/20:  Training Loss: 0.291 Training Accuracy: 91.406 Validation Accuracy: 96.875 Validation Loss: 0.122
Epoch 15/20:  Training Loss: 0.299 Training Accuracy: 91.250 Validation Accuracy: 95.312 Validation Loss: 0.565
Epoch 15/20:  Training Loss: 0.296 Training Accuracy: 93.125 Validation Accuracy: 93.750 Validation Loss: 0.214
Epoch 15/20:  Training Loss: 0.298 Training Accuracy: 91.562 Validation Accuracy: 89.062 Validation Loss: 0.352
Epoch 15/20:  Training Loss: 0.324 Training Accuracy: 90.469 Validation Accuracy: 96.875 Validation Loss: 0.199
Epoch 15/20:  Training Loss: 0.305 Training Accuracy: 90.469 Validation Accuracy: 93.750 Validation Loss: 0.521
Epoch 15/20:  Training Loss: 0.389 Training Accuracy: 88.594 Validation Accuracy: 95.312 Validation Loss: 0.152
Epoch 15/20:  Training Loss: 0.373 Training Accuracy: 89.688 Validation Accuracy: 96.875 Validation Loss: 0.109
Epoch 15/20:  Training Loss: 0.400 Training Accuracy: 89.531 Validation Accuracy: 93.750 Validation Loss: 0.216
Epoch 16/20:  Training Loss: 0.360 Training Accuracy: 90.677 Validation Accuracy: 92.188 Validation Loss: 0.178
Epoch 16/20:  Training Loss: 0.277 Training Accuracy: 92.344 Validation Accuracy: 95.312 Validation Loss: 0.152
Epoch 16/20:  Training Loss: 0.299 Training Accuracy: 91.875 Validation Accuracy: 95.312 Validation Loss: 0.154
Epoch 16/20:  Training Loss: 0.336 Training Accuracy: 89.688 Validation Accuracy: 92.188 Validation Loss: 0.194
Epoch 16/20:  Training Loss: 0.265 Training Accuracy: 92.656 Validation Accuracy: 96.875 Validation Loss: 0.100
Epoch 16/20:  Training Loss: 0.358 Training Accuracy: 90.000 Validation Accuracy: 98.438 Validation Loss: 0.069
Epoch 16/20:  Training Loss: 0.405 Training Accuracy: 89.375 Validation Accuracy: 92.188 Validation Loss: 0.649
Epoch 16/20:  Training Loss: 0.307 Training Accuracy: 91.250 Validation Accuracy: 95.312 Validation Loss: 0.160
Epoch 16/20:  Training Loss: 0.277 Training Accuracy: 92.344 Validation Accuracy: 87.500 Validation Loss: 0.354
Epoch 16/20:  Training Loss: 0.311 Training Accuracy: 91.562 Validation Accuracy: 93.750 Validation Loss: 0.227
Epoch 17/20:  Training Loss: 0.371 Training Accuracy: 89.948 Validation Accuracy: 90.625 Validation Loss: 0.335
Epoch 17/20:  Training Loss: 0.396 Training Accuracy: 88.594 Validation Accuracy: 90.625 Validation Loss: 0.309
Epoch 17/20:  Training Loss: 0.261 Training Accuracy: 92.969 Validation Accuracy: 100.000 Validation Loss: 0.062
Epoch 17/20:  Training Loss: 0.323 Training Accuracy: 90.938 Validation Accuracy: 96.875 Validation Loss: 0.116
Epoch 17/20:  Training Loss: 0.329 Training Accuracy: 90.469 Validation Accuracy: 85.938 Validation Loss: 0.328
Epoch 17/20:  Training Loss: 0.284 Training Accuracy: 91.875 Validation Accuracy: 92.188 Validation Loss: 0.649
Epoch 17/20:  Training Loss: 0.266 Training Accuracy: 93.125 Validation Accuracy: 95.312 Validation Loss: 0.093
Epoch 17/20:  Training Loss: 0.270 Training Accuracy: 92.656 Validation Accuracy: 90.625 Validation Loss: 0.198
Epoch 17/20:  Training Loss: 0.366 Training Accuracy: 90.000 Validation Accuracy: 92.188 Validation Loss: 0.140
Epoch 17/20:  Training Loss: 0.348 Training Accuracy: 90.469 Validation Accuracy: 95.312 Validation Loss: 0.176
Epoch 17/20:  Training Loss: 0.297 Training Accuracy: 90.312 Validation Accuracy: 96.875 Validation Loss: 0.091
Epoch 18/20:  Training Loss: 0.362 Training Accuracy: 89.844 Validation Accuracy: 95.312 Validation Loss: 0.171
Epoch 18/20:  Training Loss: 0.345 Training Accuracy: 90.938 Validation Accuracy: 95.312 Validation Loss: 0.165
Epoch 18/20:  Training Loss: 0.299 Training Accuracy: 91.406 Validation Accuracy: 93.750 Validation Loss: 0.175
Epoch 18/20:  Training Loss: 0.308 Training Accuracy: 90.781 Validation Accuracy: 90.625 Validation Loss: 0.268
Epoch 18/20:  Training Loss: 0.302 Training Accuracy: 91.719 Validation Accuracy: 93.750 Validation Loss: 0.151
Epoch 18/20:  Training Loss: 0.316 Training Accuracy: 91.406 Validation Accuracy: 98.438 Validation Loss: 0.083
Epoch 18/20:  Training Loss: 0.322 Training Accuracy: 90.625 Validation Accuracy: 93.750 Validation Loss: 0.602
Epoch 18/20:  Training Loss: 0.311 Training Accuracy: 91.406 Validation Accuracy: 89.062 Validation Loss: 0.267
Epoch 18/20:  Training Loss: 0.296 Training Accuracy: 91.406 Validation Accuracy: 96.875 Validation Loss: 0.065
Epoch 18/20:  Training Loss: 0.305 Training Accuracy: 90.156 Validation Accuracy: 100.000 Validation Loss: 0.018
Epoch 19/20:  Training Loss: 0.342 Training Accuracy: 90.885 Validation Accuracy: 87.500 Validation Loss: 0.338
Epoch 19/20:  Training Loss: 0.306 Training Accuracy: 92.344 Validation Accuracy: 96.875 Validation Loss: 0.110
Epoch 19/20:  Training Loss: 0.283 Training Accuracy: 91.250 Validation Accuracy: 98.438 Validation Loss: 0.065
Epoch 19/20:  Training Loss: 0.252 Training Accuracy: 93.125 Validation Accuracy: 95.312 Validation Loss: 0.138
Epoch 19/20:  Training Loss: 0.232 Training Accuracy: 93.750 Validation Accuracy: 93.750 Validation Loss: 0.244
Epoch 19/20:  Training Loss: 0.298 Training Accuracy: 92.500 Validation Accuracy: 100.000 Validation Loss: 0.030
Epoch 19/20:  Training Loss: 0.280 Training Accuracy: 91.875 Validation Accuracy: 100.000 Validation Loss: 0.035
Epoch 19/20:  Training Loss: 0.280 Training Accuracy: 91.875 Validation Accuracy: 95.312 Validation Loss: 0.190
Epoch 19/20:  Training Loss: 0.252 Training Accuracy: 93.750 Validation Accuracy: 90.625 Validation Loss: 0.264
Epoch 19/20:  Training Loss: 0.319 Training Accuracy: 90.781 Validation Accuracy: 98.438 Validation Loss: 0.154
Epoch 20/20:  Training Loss: 0.344 Training Accuracy: 92.031 Validation Accuracy: 90.625 Validation Loss: 0.227
Epoch 20/20:  Training Loss: 0.287 Training Accuracy: 92.500 Validation Accuracy: 96.875 Validation Loss: 0.115
Epoch 20/20:  Training Loss: 0.317 Training Accuracy: 90.938 Validation Accuracy: 98.438 Validation Loss: 0.049
Epoch 20/20:  Training Loss: 0.236 Training Accuracy: 93.594 Validation Accuracy: 95.312 Validation Loss: 0.133
Epoch 20/20:  Training Loss: 0.243 Training Accuracy: 92.500 Validation Accuracy: 93.750 Validation Loss: 0.133
Epoch 20/20:  Training Loss: 0.211 Training Accuracy: 93.594 Validation Accuracy: 98.438 Validation Loss: 0.096
Epoch 20/20:  Training Loss: 0.260 Training Accuracy: 91.562 Validation Accuracy: 96.875 Validation Loss: 0.068
Epoch 20/20:  Training Loss: 0.282 Training Accuracy: 91.875 Validation Accuracy: 95.312 Validation Loss: 0.185
Epoch 20/20:  Training Loss: 0.352 Training Accuracy: 90.469 Validation Accuracy: 98.438 Validation Loss: 0.077
Epoch 20/20:  Training Loss: 0.328 Training Accuracy: 90.312 Validation Accuracy: 93.750 Validation Loss: 0.244
Epoch 20/20:  Training Loss: 0.301 Training Accuracy: 91.562 Validation Accuracy: 96.875 Validation Loss: 0.073
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<h2 id="Testing-your-network">Testing your network<a class="anchor-link" href="#Testing-your-network">&#182;</a></h2><p>It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate_</span><span class="p">(</span><span class="n">valid_loaders</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final Accuracy of the network on validation Data = &quot;</span><span class="p">,</span><span class="n">validation_accuracy</span><span class="p">,</span><span class="s2">&quot;%&quot;</span><span class="p">)</span>
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<pre>Final Accuracy of the network on validation Data =  (98.4375, 0.08297161012887955) %
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">evaluate_</span><span class="p">(</span><span class="n">test_loaders</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy of the network on test Data = &quot;</span><span class="p">,</span> <span class="n">test_accuracy</span><span class="p">,</span> <span class="s2">&quot;%&quot;</span><span class="p">)</span>
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<pre>Accuracy of the network on test Data =  (92.1875, 0.20310024917125702) %
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<h2 id="Save-the-checkpoint">Save the checkpoint<a class="anchor-link" href="#Save-the-checkpoint">&#182;</a></h2><p>Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: <code>image_datasets['train'].class_to_idx</code>. You can attach this to the model as an attribute which makes inference easier later on.</p>
<p><code>model.class_to_idx = image_datasets['train'].class_to_idx</code></p>
<p>Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, <code>optimizer.state_dict</code>. You'll likely want to use this trained model in the next part of the project, so best to save it now.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Save the checkpoint</span>
<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span> <span class="o">=</span> <span class="n">image_datasets</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">class_to_idx</span>
<span class="n">checkpoint</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;input_size&#39;</span> <span class="p">:</span> <span class="mi">224</span><span class="o">*</span><span class="mi">224</span><span class="o">*</span><span class="mi">3</span><span class="p">,</span>
    <span class="s1">&#39;outputs&#39;</span> <span class="p">:</span> <span class="mi">102</span><span class="p">,</span>
    <span class="s1">&#39;model&#39;</span> <span class="p">:</span> <span class="n">model</span><span class="p">,</span>
    <span class="s1">&#39;state_dict&#39;</span><span class="p">:</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
    <span class="s1">&#39;optimnizer_state_dict&#39;</span><span class="p">:</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">,</span>
    <span class="s1">&#39;class_to_idx&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span><span class="p">}</span>

<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="s1">&#39;checkpoint.pth&#39;</span><span class="p">)</span>
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<h2 id="Loading-the-checkpoint">Loading the checkpoint<a class="anchor-link" href="#Loading-the-checkpoint">&#182;</a></h2><p>At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Write a function that loads a checkpoint and rebuilds the model</span>
<span class="k">def</span> <span class="nf">model_load</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
    <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">]</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;state_dict&#39;</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">model_load</span><span class="p">(</span><span class="s1">&#39;checkpoint.pth&#39;</span><span class="p">)</span>
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<pre>DenseNet(
  (features): Sequential(
    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu0): ReLU(inplace)
    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (denseblock1): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition1): _Transition(
      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock2): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition2): _Transition(
      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock3): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer17): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer18): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer19): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer20): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer21): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer22): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer23): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer24): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (transition3): _Transition(
      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
    )
    (denseblock4): _DenseBlock(
      (denselayer1): _DenseLayer(
        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer2): _DenseLayer(
        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer3): _DenseLayer(
        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer4): _DenseLayer(
        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer5): _DenseLayer(
        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer6): _DenseLayer(
        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer7): _DenseLayer(
        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer8): _DenseLayer(
        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer9): _DenseLayer(
        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer10): _DenseLayer(
        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer11): _DenseLayer(
        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer12): _DenseLayer(
        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer13): _DenseLayer(
        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer14): _DenseLayer(
        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer15): _DenseLayer(
        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
      (denselayer16): _DenseLayer(
        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu1): ReLU(inplace)
        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu2): ReLU(inplace)
        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      )
    )
    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (classifier): Sequential(
    (dropout1): Dropout(p=0.1)
    (hidden_layer1): Linear(in_features=1024, out_features=512, bias=True)
    (relu1): ReLU()
    (dropout2): Linear(in_features=512, out_features=102, bias=True)
    (output): LogSoftmax()
  )
)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">validation_accuracy</span><span class="p">,</span> <span class="n">validation_loss</span> <span class="o">=</span> <span class="n">evaluate_</span><span class="p">(</span><span class="n">valid_loaders</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Validation Accuracy: &quot;</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="s2">&quot;Validation Loss: &quot;</span><span class="p">,</span><span class="n">validation_loss</span><span class="p">)</span>
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<pre>Validation Accuracy:  90.625
Validation Loss:  0.6692056655883789
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<h1 id="Inference-for-classification">Inference for classification<a class="anchor-link" href="#Inference-for-classification">&#182;</a></h1><p>Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called <code>predict</code> that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
</pre></div>
<p>First you'll need to handle processing the input image such that it can be used in your network.</p>
<h2 id="Image-Preprocessing">Image Preprocessing<a class="anchor-link" href="#Image-Preprocessing">&#182;</a></h2><p>You'll want to use <code>PIL</code> to load the image (<a href="https://pillow.readthedocs.io/en/latest/reference/Image.html">documentation</a>). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training.</p>
<p>First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>thumbnail</code></a> or <a href="http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail"><code>resize</code></a> methods. Then you'll need to crop out the center 224x224 portion of the image.</p>
<p>Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so <code>np_image = np.array(pil_image)</code>.</p>
<p>As before, the network expects the images to be normalized in a specific way. For the means, it's <code>[0.485, 0.456, 0.406]</code> and for the standard deviations <code>[0.229, 0.224, 0.225]</code>. You'll want to subtract the means from each color channel, then divide by the standard deviation.</p>
<p>And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html"><code>ndarray.transpose</code></a>. The color channel needs to be first and retain the order of the other two dimensions.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Scales, crops, and normalizes a PIL image for a PyTorch model,</span>
<span class="sd">        returns an Numpy array</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># TODO: Process a PIL image for use in a PyTorch model</span>
    
    <span class="c1">#Resizing </span>
    <span class="n">w</span><span class="p">,</span><span class="n">h</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span>
    
    <span class="k">if</span> <span class="n">w</span> <span class="o">&gt;</span> <span class="n">h</span><span class="p">:</span>
        <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="nb">round</span><span class="p">((</span><span class="n">w</span><span class="o">/</span><span class="n">h</span><span class="p">)</span><span class="o">*</span><span class="mi">256</span><span class="p">),</span><span class="mi">256</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">256</span><span class="p">,</span><span class="nb">round</span><span class="p">(</span><span class="mi">256</span><span class="o">/</span><span class="p">(</span><span class="n">w</span><span class="o">/</span><span class="n">h</span><span class="p">))))</span>
                       
    <span class="c1">#Cropping </span>
                             
    <span class="n">w1</span><span class="p">,</span><span class="n">h1</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">size</span>
    <span class="n">new_w</span> <span class="o">=</span> <span class="mi">224</span>
    <span class="n">new_h</span> <span class="o">=</span> <span class="mi">224</span> 
    
    <span class="n">left</span> <span class="o">=</span> <span class="p">(</span><span class="n">w1</span><span class="o">-</span><span class="n">new_w</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">right</span> <span class="o">=</span> <span class="p">(</span><span class="n">w1</span><span class="o">+</span><span class="n">new_w</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">top</span> <span class="o">=</span> <span class="p">(</span><span class="n">h1</span><span class="o">-</span><span class="n">new_h</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
    <span class="n">bottom</span> <span class="o">=</span> <span class="p">(</span><span class="n">h1</span><span class="o">+</span><span class="n">new_h</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
                             
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">crop</span><span class="p">((</span><span class="nb">round</span><span class="p">(</span><span class="n">left</span><span class="p">),</span><span class="nb">round</span><span class="p">(</span><span class="n">top</span><span class="p">),</span><span class="nb">round</span><span class="p">(</span><span class="n">right</span><span class="p">),</span><span class="nb">round</span><span class="p">(</span><span class="n">bottom</span><span class="p">)))</span>
                             
    <span class="c1">#normalizing, reordering dimensions </span>
    
    <span class="n">np_image</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">image</span><span class="p">)</span> <span class="o">/</span><span class="mi">225</span>
    <span class="n">np_image</span> <span class="o">=</span> <span class="p">(</span><span class="n">np_image</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="mf">0.485</span><span class="p">,</span><span class="mf">0.456</span><span class="p">,</span><span class="mf">0.406</span><span class="p">])</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="mf">0.229</span><span class="p">,</span><span class="mf">0.224</span><span class="p">,</span><span class="mf">0.225</span><span class="p">]))</span>
    <span class="n">np_image</span> <span class="o">=</span> <span class="n">np_image</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">2</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="k">return</span> <span class="n">np_image</span> <span class="c1"># -&gt; returning value = tensor </span>
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<p>To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your <code>process_image</code> function works, running the output through this function should return the original image (except for the cropped out portions).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Imshow for Tensor.&quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</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="c1"># PyTorch tensors assume the color channel is the first dimension</span>
    <span class="c1"># but matplotlib assumes is the third dimension</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">transpose</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">0</span><span class="p">))</span>
    
    <span class="c1"># Undo preprocessing</span>
    <span class="n">mean</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="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
    <span class="n">std</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="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">image</span> <span class="o">+</span> <span class="n">mean</span>
    
    <span class="c1"># Image needs to be clipped between 0 and 1 or it looks like noise when displayed</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">image</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">ax</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="k">return</span> <span class="n">ax</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;white&quot;</span><span class="p">)</span>
<span class="n">test_image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">test_dir</span> <span class="o">+</span> <span class="s2">&quot;/37/image_03734.jpg&quot;</span><span class="p">)</span>
<span class="n">modified_img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">test_image</span><span class="p">)</span>
<span class="c1"># print(&quot;Type: &quot;, type(test_image))</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">modified_img</span><span class="p">)</span>
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w2/EMGI/7S/bA6SI22pkpHmjAg7HRmaM9bK4dPPGK72/ML/+x3+8P/7I071xFwLqGCirHRQ7aGUCBvISr8PdcgujCgf5BuGPKApBYchJ6Q1rqc986miHfArS6FKxbzy9OYGaYaahbdWjd0+mJfzUqhzod09YLViDuqJNjeW04mUBKlB9LHWIk9vhpC2+WulQTNGEb55+w1up2u+9/3vcz8fKN5islSxixj4y4TyVcL553X13/Tem47/yu8+Dv23VfHI33/DeKuCb0BpldYa4Q5/8TNJlN04sd/taaVQW7foGqm/abfDrWHWgorqhiGkNdbWAARDC4YFatYCbOrTpiqIJJDMXBq1FJZluSSNRbyv7ZEqFQK4Czc40oONQPrX3LRZAzpaiW1PabvVTQN8SfzezxZWvS+01bPoFt6bIShYY9SBv/T0I4acyXSWW1IO9/f87z8ufPTBB/yPP/lfkW5bT935CNWs58rX2L6fvVOWhzTwZHfN5DmsaDOgbgBh61+rhLupqmgKgo5Z5NzzOGIreOUED2OXuNrtOT488HD3AAatVk6lYK3iHa+xWmnWYBjQlJEtKHHqsuDdwqsoSZWf+va3eZiPfPbiOS+ODzTvSs1sC/UuyTOX4/J3XyfJ50cdl5f+KLP3JeOtCv7ijcWC076mtx45vBcvckp4rWHvzMkpMYwDkoTWKtaUWgq11s6nF1IOPnizFqg/hnmjtopoPFjzhrpS6sLD4YH7+1PklXMsnGYWRSD0SV0vyukLpvUqHEcGpdGobh2k7Dek8R0lfqfdJff1GMZXJpaE8AcesRUMuTNoxpsxysj717foKgDZuNnvuT8euNpNNIFvf/Qx/+NP/lcAbZo7Cu4dhMuBXJgHNwE24DSrsh93ZM146UGLQLOK1RbKtv+p6qF48khO6Xxz7rRSzkpetRf2xDmvbq55cnvL558+Y5lPKDDmIdiYSw3FAnhtpGRbSti6UrBOwRQirMvTyNV+j46Zq/KEZ3cvuD8+IBIe0qoA3jzvb7bsb2uIrH9X4pRfJI9fPd6q4B+9cvRKIai5IVNxFy5OSvFAl1JoZgzjiFnjtJwCTde+zDzcU+9eg2gH2RRcfMsxR7YgzKR34M+sIpLDKq75Zus/tbNc18virFHXSD2AMUGywChUMxqQcgj5Zf2NIGfBt75ge8HM49TB69397f1evYZDloQ05ybtuJmuSE3Jg7IbB2opHI9H8jRSW+Hp++/xp9/7EzLhEZmFk28GQ8qIQbPGTkdU4tiDKkNKnWQlWDUeQZLuoWQRaq0xv1lRHbY6DGuNdMGrN/MgZpUSXpoo0zSxFlldP7nh6vqK0/FAKwVJGWuthzP9GlL3/rJirbGmrq2zp2TNBDRhGDKTOte251ROnE7zxvX/84Bw/+cVwNkTlEex+8vXfOlKvkns37LgH7xSpFeIrag3bBImEvFY7am+cRyRVmIRi5OtLzLOQNJKB0WEUuuF20os0JRIllkLa+K7zjAM3D4ZUR14eDhQzEhZ0ZQxb5xTaSvPu4OQRryfBjwLdWmkoSsjc3LSLtPnSipzD2yiP9QAJblAnZyXlUA89IvH2RXHOI20eeHpeMPTYU+bC+OYkSQcjodIq4nz7O45/8/P/Sz/639/l+cvnvPTH3+babejmvHJD77PvCy02tiliZun70XNAb3c2AFzrFq/DoccMfyYE1dXe5ZlobUokc3DiCfYTVO/A6F119usoUDOgbHMpVDNyMNAKWVLNQ77ifl0Yv/khiFlHu7vOR0O1GLktJZLd6zAoqpPNBGQj1NqYRwm0pjRaaS4cTrMfPb558w947Ba/D/v+GGF/quwAi/BxJdP8wWh73Lufc04oBYW/8ca3GtdgrKDNN/q1r0H15GeU1qZOd4/MOaB6+urqORSBbqw97hNVEk9to4cNdRmYFBrRVUYp4GcMqOM1DIHrbY2mlfMEuMwkp+OLF6pVjExqjnNo0x0pd2KR9oxaL6CZqFKWKug6nar3klDF3o7YmHrSLP1h/RaTf7qoSK4CktZ+Ojp+7ynO/xh5mbaQ0qB0OeB07Lw4Ucf8T7w7NlzvDRudtdwLFgTxiHz0x98jAgMw4iKsCwLx8PMsswsy4K1ythTqUNKoezKAtYwNxZ6vOyN/X7H7mqP4ZRSSCkHk1I0CqumIWL0joukIZbgUgplPgWrr1bqWEk5sSwFycpwvceS4McTS6mkZSGLUFrlUCui4TGYNWqtPCwz+6wcDvd89uI5noTm3j2+6FCzWnxYw7avbvl/mHGZLXjTeCz0r/n8GYLZMk0rZyGtxLTXjLeL6rswaQ60mJXuGmCcdcFvzZA8oQ5eKjpMKEJbCi1duNEiJBVqC0ubeuGOeGQH6lIpdaEulTwkEoJrgpR7XXks1OOphKCLB7tPnRWBkLPjgJthHgpGg0RAs4a1iL/XB7FGvW6+ggTbIlvDk1eNN9kUcd+u++7uBd/8+JZ6quScONZCq1Gl9v5771NruNm310/YjzvuXrxg2l3RLBa/+UU4RCymNGaeXE3hwqvy2ac/4HQ4oJoYc8ZODU0Zd6Oe5gDMMA61cDod8J7Db80QSVztJkopnOaZ6WrXnRpHTMg5o6I9bSe4KG1Ztv4FPoSyrhpWOqkiblirlFqp6zyKMEwTKsKUhWMtIfQaEKB7eHcr6Qq+OoD3F+Xivyor8KXH/ArYz3rINc5/jB6/erxVwX+S9gHSALmXEWoS0pBwnGqOaaW2glqFtiA+kLLizfEUiyp8nIQ3B+vxpycGnVAC/ScbZZlpbcGsIZ5QV5oMXYAgTYrKwOk0c78caW7o2NM/nU4m3cWnOWkNzlPX4jUyFS2BVAuX1jvuLD0daEDroKC/viFCKL5LclG3FnFGmjreaifkGHNdmG72PBwjTXfz3hN204jmHFQCh6UumAhP3n+PVhuDDIBTlgXJA61nWIacScOEqQS5ajnxwccfsZ8mDi/u+PzTTxmSsJQCZkx5CCXnHVxaDEuwLCe+9VN/ifvjkeNyjBx8rfi8MObhrEitkZKgOjLPS/At3Gi1gChlmfHBuLm9Zi4z9/f3TNXxZtiyRF+BrFityG5Es3K8O/H8/o5qlaQBDl7KTxfjCGX6LF+2tdicsFWO5ExrxnnMtu4h46WsuYY5XiPE7Vi+4iL9u5zxIunrZK0NOV/o2V/cTri9d3nQeE8E3mDw3zZlN1ySFIl5gA2wiom2SPfVEpz81vDayCoM4wjipDyCF6yWsLSdJ68mwQUAJCuao5uOm9FqAHI5RUmpphwMwmZMYybpQME4zkceMz7Wjjm9XMd8a/klHVqVXmmWOliZ+++LWXD++2MOXOP8SB8ZglgBjzMc6zncHzHQvH+2tBrAmxsffviNyEqk6GyUhhzWPFIZPZsRymcjSxHubzMjuXM6BSA4jDkUrRun+cT+6ooXz55xOLzg9voJVhveegqwh1h0HMCBZ8+e8Y2PP+LFwwO73Z5aK5988mdM40hKyjhkhpw7QJjZ7aYAI3OkC5sZrRl5EJZ54enTp7y4u0MkwrdpjFZnrXsc5uGdnZYZ80hxllo78CsX0ytdfjrGxCrsL1ngC+hpe1oX6Sbh/OVHhnkNCbn4/aVWWSX1Ugu89LGXFsRrx2bkJapRVw/0y8YPLfh/+qd/yr/8l/+SH/zgB6gq/+Af/AP+8T/+x/y7f/fv+K3f+i0++OADAP7Fv/gXr63Ld7NOcOmdctzwFiwsVJgthB4slENztNnWIidcze5am2ClYUv42pZqoFISfe7WvnKO9vSZRucYjS40mhRNTlkqQwqlYrUy10IaVxZXv25WQV21rXZh9i4koNVQdzJRjOTesQLpXCMlbIRdOPvy0s9+rrWX3LpglY6PdCtUPYgwp7sHntw8QXMiAAYjqVCWOVpdddO/Loyhx9PeSU/TNHVOBex2GZL0MMcRjcKbw+nI/vqa090du/2eMs8sp7mX3p7ZbiKheE6nI7VWcs6bUJobx9MBHKZx4Ob6qqf+Eq0Z4zhSehiSc2YNjyCYmO89fQ+/u8fNogHLkCPNNyQYM+bOsN9xv5xAJTzBjZQTE7wKrPd0qF3O+4Xv/DK9YrXsX5Srx+Ka/eydXY5zcZQ//tp2wFeI/eX5X+H2X+JHm7v/hpDkhxb8lBK/9mu/xl/9q3+V+/t7fvmXf5m//tf/OgD/5J/8E/7ZP/tnbzyG+NpuK1B53HsPt1h8lfipgDejzQuWh1j8GgJ2Oi4spTCfZuxUQuA0wVzwsUb9fifzGBGH19rQJOAJZSCnC3dfKh20RnCePbzgVGckhzYNzpdtDMMA6oylORWLbjXNNsOQLOoHtJNtLEr6zy7cS8rZHwm/bE96NQwrUNhbA2xWammVcZrIYwiYim/XqHquuI/6B2c3TSxliTZlGuw3dzuXc3onRYVLQ2uVlAKJH6YpSqNzph4OW9zqIptgJdEtZXd3d8fu+gp3J48DrTWmcUCAUgp39/fsphEHxnGitYVxGHu6M9qjLfPCbp+orTEMA8cW1j6UQ+/fl5XDslC9RdHX6nlsONDFPK+uuTwWNafnwEV6XQabslpJFKsyPj+YV6zt7rFt3Y5igs8BwcvVlz18ONdhfcEFfP24cCl8pYe/AUD8oQX/448/3hpo3tzc8DM/8zN88sknf65jmLfNJUQ6l87X7jpdsAh3TTWhJGggKgFeVef5w11UnDlkCyOvZrS6YG0BlFESpRZwo7XSzzFGeykDQRFJqEDqXXlSnri9Dnzg+eEFixWqP+7kulKDq1n01MPwXpJq7heNOfzccqRb+xV0XPWAdpDNnbBSfib6ZI3eg0mDnjzPC4u1jVgEzjyfuL19fwtDvrBU+sk1hdJsFvTXYNQF6UX751SEZpVaLTCCDuJZY8vXh3eTMKBadMRZrT70OUyR9z8dDtw+fcpco1GnSNy1u21ptePxFJcpwjAMVIRpt2OZ5/77SNuhym6/596M2hpTitTskAcWMe4PD8xl4dBqt9gXjvqlzy0XrzcQNs6jBDjc3HomoHt76/Eea5Du0z+ecXPHJNicrOHrRYi2xXmrB9Hn7bJ8eD3v6u28bqyufsh7gM76KhrsxfgLifG/+93v8od/+If8tb/21/iv//W/8pu/+Zv89m//Nt/5znf4tV/7NZ4+ffrK71VpGI3oKwZdt27CMKiCa7fAA/ura3a7XXTjLYKgjDqgHm21EytzG5pXmjfEnVrniOHnhbosJA0izRoXxfyHdTKvmBtJlHGcAjQS48XhDqulO74R66t4bxjiiK0CF9ZOxaKCzYPvn4CBnlIy0K4MxjxEARHhORTrbbm7zRBgn0b2+30QZFpFq3WiTVzNlEdub56EENUWJckCa0XdSo5yj4xJ6gQpM8O1g4btzP+vtSLSwTokuPEE2SlpYkiZ26fvcffw0Ntm62Os4sLdTBog4DKfggCk2q15lC1rUqyEZ3c6zaSU4z4Exu7+r/Gc9q6+g2YMNgXWzBD3rbFnKRXbyBah0FYK9To2lJ5e0CXRwm1ImSkFm7FYo7RGtWBjPmqGvAlw/yFy+dverPVMc8reU9MqkXKzdbVIJ3Y+RhJ+2BG3bDT/8o1qfuQuuw8PD/zqr/4q/+pf/Stubm74h//wH/If/sN/4Hd+53f4+OOP+bf/9t++9ruGRXslWf863hPlosKYM8MwhOVImZSH3isvure6CtM4MuYhFkjXp07Evec4qoN6rV4w1ezscXi816x2vKGdUz94B0z0DArxuHp9866IRxkdeFZvvCsChIREzO/RQmrUzFUeuRknbvfXPNlfcbO74nracz3u2A8T+zyyywNTijTalAf2045RExkhowwpM6REWZZed3AG2dZMQMR/cnZV+7Vv8aBsUwXu2/dxth6HvhZQibDf7ym1rgcJ/ORiHlYTJN1y1RKpuros5/fRzQ8WFWqNMMx6c86V679plNX60l1vWTkRttV7wOqus971drZzWP2yaDlZot//zbTnatpxNcbfMQ8MKa8f4yJOu4j1414fhQwr4cPZGrXkvgYu53xVF+uaehk/+HNRCzZd99LFvGL8SBa/lMKv/uqv8nf/7t/lb/2tvwXAhx9+uL3/9//+3+dXfuVXXvt9U8dS4AXWeoM6iwKXgcSUR3IeWGoj5ZE0DHiv2dbJXTgfAAAgAElEQVQxaJ47nDLPnE5HDg8PtG61mjlLawyi+NJY5pmyhOC36uQhXGXtiHfxKN7BracPB8bdCKnHmb0FtdsZOTaxvklEPEhrhrZYbANCVthJVBaiiZ0bc88oSFLGlNmN49ZI0joBo/nKEQj3UFV7I4sQ2rZvfMBTSmu96CQyHrn/FFmX0tnSec84OPQe+h1PCcRiUxjUdkFjVZJotBcTga7QvFvBuRRkfe9irGFK1CHERhgvnj/nm7urbR8B6SCIm/UwKxTH2gdeklBKifbrQt9nIYBJUdA8YAhDL7eutbG72pPTgDKTOvffeyn0WhRkL0mSSAjkpJnbMdp+BZbhLN5IxwNLLdRWo624yOaNPT6SX6Js28HFnQnYk0iaOUl4lbW/t5aeJ1GQuD7z9bq/2thOe6G834AK/PCC7+7863/9r/mZn/kZ/uk//afb7//sz/5si/3/43/8j69sq70OyYmUhHEY4yE1o7rjpUZs7wSTbgggyTwaOqSc0N6eaVRl2tJOiWo1kOpy7uBjrdJ6n/u1IKZZpVlCrdKKsbRCqUu00MZxMqUWWo1+/EIsyrbGa32io7f+Od/e1j50zUgEx32XR1Ie2IlEfbhqcNkRkgeRSVvEx0k1cAJZLUAI5LkxiUAeMDd2UwBqx9MprFKz4Kyn1MGouMZxzB0o861f4ZrGA0FaCIcAKWdqb4kll+7x2m/AAyw8zXMUQNUWQrYd4WwTkwrNWi+UMp49e8bt0/fZ7fbMp1O44SnhBsOQ2e12XF1d95bc0ZRFJDola0qdkh0K4fbp02BzDgPTNHEshXmeAxMZBsxqzGHq4YpbYDE9W/C4wYowamLKmSlHqOECYsp+mkhJOSwnall6RqQDuBcexupbbEAiofTUYZLETjIp9U1dDGp/H+KZp03ZRuHYjz6+phj/v/yX/8Lv/M7v8PM///P8vb/394BI3f3u7/4u/+2//TcAfuqnfopf//Vff+0xXAUkYzmxplWKOFYTsxmkwrXM7PLEqB31b061eLiDDJBGdExMaR9c+Vpjgw07nWN4C5aXiKO9N76RaCZIiX10llY4lRMyBIFolEJrwrzMHOcDaTRUfdvhxU2iMac4ohGTNyGEr8eVyZ3qTsGpGrv1WDJyEsak0FrvzbECSLHQ1zJi2bRLj9NX91A0ugQbnMqMmG38c0G23gSroDoaYZUZucfJwXRM1FJ7z3+DTpy5tIrBg2frJYgIpRlLCZdck/b9B2Q7p0pCaLjrhh+oJk6HIyA8vbkh314hwIsXL7CmDMNA80Zd4j7UQ/gaFhuBLMtGr005Md5e8bAcSbuRKqBZWUphN01oEpbTA2LCKEqWUDwnKzRW76HfHyG4zcNDPJXCpIJ0Jb9hCOYMwOQJ8Sg7nr1Go6W1acwFhO9AcmMQIAs1Q5MKtZF7YZZChGwSz624MXvbFCjQQe/HYcH6dEJtn7skA9umMK+scb8YP7Tg/+Iv/iJ/9Ed/9IXfv6mX/uVovfXTRqEUIY8DnlPsgWfGXTmxdHaYoN1KKLk6VYIhlklIUob9Dq2VhrMshdbapnWjMEaiDLQHUuZGrR7dfMtMtQIivbQ2uvWeTic0WXTsVQc1ZG3v1e9DJYQwWZeNnhReOQrNC3WJ/fyM8GJmOYVH0HvjkTJKT8d0y6bdvY+pCT/Ee6wbJQhOqZU8DB3/6k0+jO6+60aEEhUSiWEYApBcyubSpo7IX0TDAFtO3j0qJCGq3A6HQ2Altlot+jf7xArR8ML9zIU3D9ptKZyOR1SDvNNabL1VysK035E0hQDvxnhmmrrybr0HYjANcx642V9Fya97dPM5VYKpIQwuNA+AeMxD7LTUKpHnObvH6rAbJoYc5cjmAbBGR6BCqdFHYBpGBk14b/ediN2QylrD0RfECkybRFii3diE3rSthdjeo9vzoCGCxSwyKQTzVC6Zey/J8LYhyyty9ecW7l8+3m4jjtWqCSAhkIpHQws5V9wt7cR9nVGHQTNjzowdFESF5rlvkpGRnBl2E/vSmE9zL8ZQskYuH/pcdn+0mbGUmcN8xKSFsBajFaWUhqqwG3LE0diamTlnA4gHYD3G85WrL70tVIqSXfPG0RaW0tC5kLLFJpYamMWgmSSxL4CqgqbYD24F42TtkBuPddm6166UU++ZiUCP6YogHKleFyBryeoZhEs5k1WZa/AX2BpURD27OxGa+NpYxDnNywVR5zGwtS6682t5rLQMHh7u2E07apkRoreCaLTNWnyOqj6R3iKbaMTRWZcpD7FuvMf70pt+EiCt1RrC1SxAtRSo+VpyrUSzFOEsJNe7PbthZOh9+2prLK2xtLKtq+urK6xU7p6/2DIAFWdpkXAWYl9F7ccUCdpsUjbPMKIEJylMFmlRcT97Fqs1Xz29l1OHl3O8bqezLuT1PX/8HF433m6zzc54806WgB5/ezTedPFA/c1jS6oaD3OQxPVuz5yE3a6S8xDg2DBGBkAz4zQF6afUrld6bMsZha+1UqxyWoIEhEJOSkqJIWemNMYOP2MiJaNaQaxsG2hcgOHQFZYl7dRRCYKhguWoO6jqlG6SvRqKMUhDRRmkkqRvKqLaOwXHSYa+PRjA4XhEVFmWyu3tLeM4xU7DpYRF6oKtm7B1y7CSbAgr/EiYt6oieZz2Egk3F9kyK8fj3LsTnSsJNi5Gf5bQLd+6KLswJI3NUG6ubzbPZWv3vSoRiIo9OR97VdjWGrQAG8fdHhGJjUldOhvwxFJL77wESRLjNJJUmU+Ril0Zequ9UYRxHGPPhU4dLq3R+nH248gwxC5NdS60pbCcZtQjVNKmvXAoMYigFkBSkwjpBo2ekSmFV6hNtvmqPZ5vZhT8LPhcyP4rxOYRyUu2Tz9OAXydqP6POrwjmitnOkY8HhfD1w3tLCwFNRDk3TAwpYymRDktVCn4OMEEMo6b4FoeEHOstIh/RWL7ZaGz0pxlWSglwLucUvT3m0b2ukfTEHu3Jaf6iZNZ5LYtcty+Wvn+IF2F1pw1zHYVyGvuFqrEjrxJJGhJLpFb9/BwBg1uv6HgHbH36Hwjw0ithQqcjgfUO8+AlQgUKUrMMbGzwMvZ8pt5T2euNNwIM4TgyWvHG+gVbEooHxch5wEz5/Nnz8h5pNWlZ2MCzHxcZdabcl5YK+nXZ0IAdaVuQp00LL65kTQ/Ch+QsGLDMPQMQCjv0iql1a7EEp9+9jnP7l6wv76itULxqB8Yr3bRoLUo034HrXJYTn1eAoBUiWcvfe9F3zby1FC6mvp5IvyqrfUe/20LFwZVdpqgb9QiCYYk5CTkoXdzqj1NinHcEPxo3OKw+XPnrutdcV6a7+29eEabbl0Vu1wo3C8Zb7dIZ11o3QUE70U2Fzl4c7wY2aNv+26cuL2+iZhrnJjnmVILZSl4i1hcL44rhDcx5CGWewrqrluhWuu56N7Xbz+x203BDdCBlAZAcTEKylwXhIiLzc+eQ1REnRtXdNuJSOqAXWT1tzSLxP2uD1oJ3kFGGIaRcRgRaVuMPO2mjUuwYiIUJ2tmWRacICqxxulrnniL8VcXUi421lind93nK7yDVmNrcc2ZpInihXGcKLVyOJxw78w/znwBazUqANmWaoz+i7Ufn7szTEHFlZQY0hjue29xtuIazYy0Hq8rj1ZKTzMGl2BuC9///FOm3URrxmcvnqEI2SKWt97ubG4FcWfcjdxME/enI/NyiloPiVSeLZWSZgYfsRTHH3IK3rYTrn9ZsNqYa2GxSja6Yo6msdlD0BGhApJhHBO5F0tFDYpRW2ADs6yp1A42imw7OAUG8bL19i3TswK8EcLQlcElRfjNUf7bF/xeSnuGLX1bxE5YnyklnkzXTDIEcKOhAGQYmaZdgDY9nTPPJ9jttq4xivSmjGyL3jrXvtZYTLupC/s4MqbMqFH/ndIYu+Akx2oDzRRzGoJpMPrUYRSJrblakHNqDdaempDQ2MyigbS+6UbvGBN4+9nlTDhJekUfgUsMQyYRKa9dnpBhx9W45/7FA7WUXpbrpAt3eAXq1hAw3PvUdxo+9wNwesvw3uJMBayDabU2kJ7BwHn+4o7j8RS8ihKNOcLShOCeQ4n1vI8XX22VPE1M+33v6d96+JWiNmNVDgT33nvsO6TUMyfRRTePAyrw3T/7Hi+OD+S6hKBDsCwVdjdXPP3g/ajXP0UDziRKWwrlcEJaLPwpD+zSwE4SqTmosW67JSKUpVCI3x3nE6fDkXmeGVKmbp2jOuZL7PuHELjCaumzhhcGLLVRm9G6YVoFdVXUq6cecu6Pgfk1vbuGcC3WUO6l4asCqWuQ/+PciMOsE2GaxQJW6TxwNmBDVcPSDyO7NIIFmCOqpJTJQ0YtMYwjOSfmZemdXwaSRklmVMNFMUrrE5ySxlbNeWCadp02G39St9RAgI4aFirlTLGG9WuMdBN9b75wyW+vrraW0jvJjCn4WmMaeXIV1j/33v1renLNPAwpkzWHB1Acp4GmaAG2Wvwcgnm8P3I6nqK1l3nfiCJFf3ldW06zxeG1VoRwV1OvlFuth3ZwstYWVXStMY6xN14egim5zEsH92zbaVgdTqUy7ia81kfPVl5+IXHecRrjuL1MGsL1ly40q/sfrdaGni/vW6R1/OLh8MCzuztS0nD3k2Kce+5Fhx/nyc0TjvJAPS0MOXN/f08rhQxhPPLA1TAxpagAXFm+dOwgpQSi1BatwXZXct51KJ0ZeeJESNkaolGgpRrgpIrQME7zwrJENiaeiq927hynr/sUviwoq03sLrxqZL3GlJgkI+7MnVZsApbkC0Sll8dbBve6VVePzrbdTEV9NyEY9Pxyq9QWlEdfQUGsC7UitLBCBstcKBZFOaKCmHVkFaAfexi51gErFXHdauubNUqZUc+gBZUSAE41qIq03thTVlAKxNcdYIRswjDtwgqs6LwmrnTgOksvttF+D77ty7emrmLLZ6eUhtXalUOJRiNdWezyGE0q8XMb79bo8AJDztFqygIYtSUs6jTsGVLeFE10tg2UO2lGs2C19MrF6LgLic+evaD1TkNrF1fzGgrzGK2zM50m7BBbkGl0O+7eBSrk3UD1xpqdcM5tyS7XaSjuqRuGXkMhzrSbOB6OfPK975E19T0RhUxiTJlpHFEUMcEOC5J37HSg5nCfd2ng3jyUvWZ2eSRLUL/XegO3tYIybRuoDCmQ/9aM62HH6XjkVGc0K8XnABhl3cAlzpX6HNamPBwqhxOUFh4NEt2YoXt7kYu9cNUfT8gGo3ZP2DQ6Gk9p4DpNER60EqXMVqmlkYcvF723C+6tOepeEx9r6pyminxlaNzDPFO9Mkhi7O21PSmOhiso4XLmnLBq1GWO1NTKYCOAKuuxUhIFdQqRLjK8N6sI5F1TMK0kxTbcDrQSrbhTj8DMPaoF2xmqaGZM40gW3einSbq17oj92i+vh2db/B1ub6j83ZjwnGm1UK3SeuVhaUHCkU5qiSaWCTdjGHwraSZHrLybdtHHzqK1Vm3pzH+P0wbVuINXq5tp1hinHa6Jh8MDPbAktsNOjDnCo4Mq3sMgv8jbXwSjCLGw18aYm1vbY1X6OROxpdpm8rr1SylhPSz49LNPub25Za9EqKPdc1ONNl/9q8f7A1MamIaBQRLnbEZimEYS0lN2jaY9a+Gpg4frxqO29TGQFEnTdb8GWyv3CIq15EweM0MKclWxhVahlIX5uFBrbyPR0bs1Nr/A6Tbr/wVjvWE053lZvbmV8rs2Rc1dTYzpy4t03rLFl42fLnIWptbruLV1wAllcaMHR31iVg3butCHgHmLNEujN1Rc490WlrXhsQuLKGVZwqoSIJcLYBYVZ4Srhih5HMnjSJ1ju67UVbOrMObEftizG4YovGFgzLFXnwhsrbq9K4BeAFpqpbnFwrNYQDnHMVR1I9do0k5h9Z4bNJZlIedEKSGoqhrbShFuM+u9d9d8rSrMEuFQbWFxzXsOvW93RS+lLctC0tzdRWeplWmaEBV248TVfs/VNDIfj/EY+4rcmnGcwf0tz69dua+UYesIvXhcq/Zr1R6SVWvbbspOeDF3d3eYGdfX11HLEDts9R17FrJrX1aCZsWWiqF985XMkRMusLRKcdk6Dh2tkiWqBvfjiEqAjnVZkE5VTkNGh7iupRbmslCitnSj8SISnZ4EzDO2NKzG9t9G6wmTLqGrMfLHgOjWVUu2KWQLA/pQF6T1StIUpCI89hjYacZI58Ki14y3K/gxQ1gzrHeFsfW1GVa9y7r1qqzYOaUtcxAevDANI6rONOTe0SeT09hdZNssTauBvLk7rkZ1gl5bK5oSOYXwbDltt74QE4hRKYjD7f6G6FYzksbMbhoZkpI1qJxDRH09PxOo70ppDUJMT+NYtBU7lYXaorZcOzFkypF7HnIQU0qnjeaeDahWtpoAcfpuPdKPEYCbC9G9RmAaJ2ovay21bTHoalmMKKShA1Qpp9hoY/VHBKb9jnEY2HXuurfWd+9h8yC8U1EFObusPasQHIqeduznAKHMM4JsO9ysSkQ7hlN7F+VSK6flxPvf+Ebsdajj1tK7lr7lloQ3lXIi7QKRV5HAEFJid3VFehh58XBPcO+geOMwV0ZJpPmEPn2f6ymwFKuV4+EBswAVSQkTZy4zzdsGqGWJmoSlVCQp5o25ONqc7Im9ZqYRZmscaqE2P9OG1znaZOKln5sCWP/Rw7Ru7euahVm9qz7fY/pyX//tovol6JraEXj3QCut9ZLYVQaJZg8F666Q4XNhWQ5kUa73I/n2BhyeP/uMq/0Tpp6vfzSBHguvdernupkDHflfe/CbhYJNokx5ZJx25Ks9N2tDUJHoFbC+5qKIpjPMOuQdyuiivZbh1FY5lpmH+cSxLjQzakdpd3mMpiLrHvMS24TPrbB4C6+nGak/Ou1MP/fYQ2Bls9Va++5B4EmDFKPS8+UX17Pm3+UcctiaUZHYIGS3m7i9vdn6BliLbcZabaFQO4tMO4FI1sXcQ4OcE8Uax+ORp++9T3Oj1ih91tzvIy4mADKP1NcgAQDn7s5Pux1Dr+VfWoudmOrSW3CNTOMIsGUtht5abRgG8jCg48D+4YpjLUxkUs64wHE+sZ920UasLtzmWxQYxoH7XrVpJQwPKXoZNCd2Vb5YXtWMejxhbpyak6pztW4uOo6U1rhKhcN84s7bFjq1ta9jf35u3im7fuH++3Yi6YKdNUdvSdYCrljjYxp+vF39KxmZ64l2XLAhLEKzFnuu2+rdxIo0i9ZWJgH4mUS6R1OPl3vKZ6mV6xSuXWywWLfyRwhhxqyzraKgJFCofqrunuYhsR8mbnY35N0OGzNNI/cbm0d6b/YgtBbhAdBr/nvxjTnSQnsJAlmpZhyXE8+PBx7mI61XDTU3MkqyRrW1HiDArbn3ibdT3ziiOVfDjrUnXdQKeJ+qIImsVlgkXFpJCVrbshvrPnZn5yRcfetWo5mRxpFBlZub656yC4Zaq4/TZAnOcWdn9Jmu8T2RSZAA45ZlJo0j2jcaSZp6bl5AYq5yzpj6Fq60rkh3+z0AD4eZuTZOx1O00t5N7He7qAWolXVrs3meEYPlNIc3qTDlkW9++DE7cl9vxn2K3XyPx8OW5h1yprSKpw7GdfwpsjDG9fUV73/jAwzn/sUdy/GEexRxVTNqrwStNJoaZLgaJ57kKw555Eqc5/d3LPQwp+Mr1rMYj7IiGwEk/ql+Zmbamtvv/SKUKGIS/TEW/A+vb3mYMw/lGGiqRB196uwm14iHY1GHlWoSO9KaAJXor9YKJiFct+89ZdztmKYRsWD/tVIo89JBLOm7roQUO06lRTlva1hrUQ8wjkzjLmLblKiqmFrHDFZeeN+TL/btCvSZddcduvAGcl29YRVmq9yfHrifZxYPS7+xrUSobiy1Iqsr2gqHZWa22DBUEQYUzyEgOWeWeWYYol21W9u2CnOHWmqw3uKCArzarHx3w1flQFiTYOSd23mbGYeHh3gWOTM/HMIbq9EDgG7ltQt9pLD6PXXXXlPCMQ6HI3sNmmy8Z5uFDhC0181bFFWtz8scSp05nWZOpxPWjKspsBTcWY5HFuip0m4FLaogvYaXVcXQrNv8pBQdnMarJxxPJw7391w/eUIphWaNz++e4zhpSJsXU0/RvbfMC/O8cHNzjT655XlplFIwgx5NIQRJ69T5IvQW7bv9jrHBBx/d8NnDHZ8f7jl1w5H78+lxD9BJVsQBtWcx1mdk1vqOxbohhJLORKfXjbcq+INm3rt+wmgjD+UUZN2VRSbQ6HFoRy7NOx++C9TqHkUlVEJQntzucQ9wTFRjVxl3ltNMKQWVKNqJ9RpdeEydpZWVPLgSzR/xx817HE7f5rl1Hn3OmENrvWiERLO6cflXNdyWymxhuV8cD9R4injPMkSPvq4gUgCYtbXgn7dCpZGHMRp3EISglBK3T57w3Izdbhf98ZOc95hTZZzGRww+0XMtelQv2lYYJL56J4poeF95CuV3uL8Pz0sktux68pTT4dg3QOnH5rxYt9Cho+nNDDTmMefMaY6963a7HaUFW3PdTCU81iD4tFIgxXZd8xwx/W6/R6r1+L4EjyEpu2lHSik2TzXj8HBgkWg1JiJI1qAed8A3EZ2ZcxKeXj8BFR5OR0yUw/HAYTkx9VZvdPyheSh7c6GUhWUZmU8naq9erN24rDnK3T46+SyHIzYbVxLdi54MI8cy8+Htezx5esundy+4Ox6orQZInaSDgXZ+dgSvZcgDu3GHlUZZApxeWYTSMY3m9UuF+60K/jxH//RdGtn1CqvTMPKiHDiK46VgCk1hbSGmJmSLfHGlRaODtMMZGYfYQGPIA02E1jnrC0YVpywzqXeEqdFGN+q9m/dwoNf8C4grRuZUD4gPmEW6cY2P1/Zd0CnGnWk1t0DQtZzbd5n3TrFLYT5VMgEmanTx2ObDO/qNKIvX4LMTzTan4Yqb2yfshwmaU8rMkAeOy5FxNzCMCZGh53kTbS0D1agAyz1P3fp2U0lTj8N7RqPfu3p06Mmqkd60xvU0cXxxxzIvpDxhTWjVeDiczmkmgSFrD/edbErqTUrcg9aqHu8f7x/YX12hKbrmkjRcaAl8wswYNdPmEgClKqaJpTmjjtRTwVs0XEk5MewS4zggOLUuNK+wOPs8RBpu9e6EC8agdUMSWFI7VW6GCavGD+5esFhlScKxzpHCszUSEdBoqHEqBVlOPL/7PHYcXql8Bi7OiHClmethQMbGi4c7Cs4ClKmx241kjx2L8+0tHz15wvHhyDIvFKs8WGEm+jmwKmwzFneUilCjTNwUscgCtURwDJL8+Ap+WYJjPw4jY+dFX6URURiWmUODxSoprVYRYlnFSAKjxIYcydjKJaX3j2+tbHFlmWeaBS/frEXHm5wjdaThchez0JzNqItzpcJg/z9z7/Jq2Zac9/5ijDHnXGvtnZmnKo/OqfJRySAQ9xq7/gB1ZJAoYbAE7hjhhsGFQA0bjGVwQ13jhkEdu1s9YYzBGCEZ1PKjYZCEuhIGXa5xw0K2VFV5Tu7cj7XmHI+4jYgx5srzUtll3fSsyrMz995rrfkYMSLiiy++aIi4gAPef24N766Rbt+zcqBy3i6ORFvoOk+T0XrTzBwn4myDPCrm+STEEVLXaptKzjbGy0aBzza3Lzrivyzcf3LHslgD0TLP5JwBo8V2tZmhQ+/CkR4Rm3e8Aot6l1wvu4l4rdrVYbZtI6aJly/f5/z0xOtPXnNzOHHeVraaeXZ7S0DYttWjJA+aPJV4K1t1r5ldLGWaZ+bjMiKD/kUcJZc40Ud8pWQEnfvXdxymhaqNw+lo7L7RXWi1/Vorgc4NcaRbrBmnC2b2CNJklg1noCmn4xEe3ox0KAjQlFqMXdolyW5vTszT5CVGB6a7KE/XERScYRpNZ+JROK+rjQX32zKniTkkZl8DemhMjinUpwfKVr2j0Ctczl+41MoETF3ZUXqkZOSv0ITlS2zvhzb8n/7pn+bm5mbUnn/913+d169f88u//Mv88R//MR999BH/7J/9s89X2k2Rll0xx3PDNEXmIASZSEvgUlaqqWp7/bV7J1NGPYaJU5yZVKA0dNIheFC2zfT0yspWVtBKU2uDvQmmpV+DsrXKuRW2WvpAH2t6yBtxs24r4xZ4uIWQRJgkDVqmhWNCVHsAJRgQFkMgeD4cm9GPgwaWKflcug6wCTU01q2wlUqtldvTidPxSG2mCShNeXp8JMboAqOTj6X2Up7z7MEiI4nutb2xx6i9oz7UQWC6gCVeajQyjXjeGEaKcLq9ZV4WXn/yGhH40R/7hqUKIfDJx5+wrpdBWvpsyN+NxjCEnE30xOreMqIjy82bg39dF9A2g5vbGyiVx/sHnr945gNACs1z6uwTf42F6eDXEAjdwc5OpIrByEXlsgFCrhun58+ZpolL2YZB0yxiiSIUlNvjkR/90Y/4+JOPqXl761p3I1QHpY3o07BIMmtjrQVZoZZKjpk8z8zLAqhpNCzBZg8uM8vDA2/Oj6ytDLVgU5Rq4DMHbe9y5iRKbV3O44uP/y0e/9d+7dfG5ByA73znO/zkT/4kv/RLv8R3vvMdvvOd7/CP/tE/+szrbIM0oCV7HVu9ZGe5cc/FrEOuAzleoLUQVgKz19C1NlqoSHCdPVfOtRn2xrCypp1evupiH83prQyv13duA7xcYEECEcsZowQmiUMhBuzhzp6yJDH+fO8UFC8hRuuIMXyhCvSyjtpDbZ53B7rcs3V4SavWirpljsuBXerKRk6XUneDYpeaHqO9EANIm9IndHdBx90w2qgOVH+tuoBEN8p5nrm5OVn9OAXKVh11h7HrfC6q1METM74urLHjDrsnxpF8FZAY6JOPBbi5uWG7rKTZN0znJ3Q+iLqx1lZ3MpFvZqMDtOfMircmuzZjawNAc/iIIHEXaFHTgnj2zKTMS7G8vm+afRLhRRIAACAASURBVGleQx3XEZX4VJ/SKrlff2298mubXTPHlFIiaOB0ONoYuUsz8pmXpFMIHKbZ2I7a3B3165X9WXzB8ecS6v+H//Af+Bf/4l8A8Df+xt/gb//tv/25hn8pmdCMZqreWBGaYaEBYYtXHUrGVOGa4ZDUZKoTFppWqgssKiVv1LK5UINtBipAEKbjYbTVlmpGT1Oi7zlRAgeZmJswi43kts4+U521PcmkteRqY1UAtd+VwBC1LGr5KL1kg0lmCWXk+H1trjkjwGk5mNFLQHq7ZymmGTDZ3LnmO383vDRNvoHtqrkxJqOKOjJsMwOi97bL3gatbfRIBJlQar8gxL1/ddZanCbSwerSMUV7hp10YfHt2/ek/8WEZMeYrubAYhcwtcm6PskG2yCOxyOXvBnFdluZQuDlV7/KqhshBaKajsB5PfP0+LRTd4N5Z7DypajfEwfMNCbXQzAOwLpudk61WQXAsQCTFrdN9riceP/lS7768iWv7l7x5s09pgZnRm/7hsuf2ScbbiKmqJxiYq0m5VVwwlGIiMSRLgYRpsnSlEjg9ngcvIxLMRHUROD2cOL2eIAGq2ZK2enStnn+/9Cd94u/+IuICL/wC7/AL/zCL/Dq1auhtPvBBx/w8ccff+7rSs1QnMrYxSGEwXizejYEjZ5fGy2xg0YHIloaebOe6xqtIcdIMhdyy9ZksSSmCCkYxC5JWHOhFKPLSlMmNWmtQDBtumrdV0KPRKyS0DBxjOvup9BzWzVFFcV6s5tc6QqkSFaTbK6tsWoeYaS7X4x73RVhFpJE8DRDHURM0Up4teaB4HZtvVIyyYdRVG3Da6V5om+Z4coTtLaPAhPM+4k2Ss5+zf57dW8oUSBE4x0EMS18SiXnQnQyTp8ipB5V9RcP4RIP51v37GrRUZcxV6CTA87nJ/Cmph6Jqf+vVpMCF/f29jneuxEdzXcMBZxaa8ibqTMpoIEmtrElrCJwuVyspHs8cDzdEER4frrl2c0N8zLxp9/7Hv/ju3+CROv3KLntIKfu963Li9MaU0o8u7lhmmckBFKwzs3TdCA4fRixcWuCDU6RaGlQTIlnt8+I5yeaKqflyOlwYJkTtRgeVUsZu2wIcRCjvuj4oQ3/X/2rf8WHH37Iq1ev+Pa3v82P//iP/8CvDc6uCj7+KDdTuw1qoXRqDFcorbH0JhJfu5N3gz2tK1EbMiVSsHJWbtmn5VgomeZkPdnNFuaqlVw274MP7glkqJTaOqqjvNgwjv/48N3ufYHZ36tTfcUHNTYHlHLzsd8w0OSuzCqKbziByQ3g8nSmxoi6+uylZs5l4+XLlyNv7jMHg5UbTE0nOHPQ89zW9j5x6cKeXN1XvC23VnNZXf0mJuMhGBQ+QscenhsRSDDNQ9tW+vcRhtKwXtEnu5Ly9b0LIjSPZrQ2pmDNMLllosQe/4983y/L8l0fgQ2wbqtVWtQ08LQa1RfEtRWMIhyjD0fp97BZg00IgSVGDre3fPOv/BXSPFOxa2zV+CCvX7/m9X9/zd39G2sYc9nyEIVSri5qbHp+6Q1Cg9O0WKkOaBKMPOaAawdDo099v398hBh8hJmJsaQYoZnYzBzj0OIPnmp2DEV6uP8lxw9t+B9++CEAL1++5Fvf+ha///u/z8uXL4e+/ne/+9238v/rYw6R56cb0nHhsWUuNdO2sw2FAFLB5tCLQIhDLsuCGWFJidN0oijIFJEpMSVfOBIsXMXywJ7D5dyoKla393yp69Z3dd1WTdd/CpHoop+lLzz2sDxgD6zTCkIIVuf1zwvJwszsOV3tqjsCoWJexhd2VyMSsxqK2tjr4H0A6zlbfbpWJtcx6Is4iFA2K32tl8tgKfa+/F6CFF8c1s7a1W8bRV1KyhF12Jt8gm9efd6dcVCcalqtOnI+n72Tsn8K2EJ8e/0NEBHGM7R9oI3oZdt8M176gEzTXtAO/KkRppq2t+bD1Wr6/Qa6dwqrswh7VBGNfaheShVPh5r3UgTHEw6HAw9PZ948PnI+nyml8vTwYNUhLcZEnJMJrArktW+sdl0j38eMmtqIkxAkkdtmCtApIQEuW0WLRbbTMllqppVP7t9QUabDwuFwsAk87lBaM7pyKTKYhAME+XJ7H8cPZfgms9y4vb3l6emJ3/7t3+bv/t2/y0//9E/zG7/xG/zSL/0Sv/Ebv8HP/MzPfO7ra8mkApNr1EOFxb1lVdiK83QiNnfN5KDmkAiqtCSEQ+IYZlBroCBBoVHXSm5nGxKpag0/m7BtUBQ2xJRx/CZECx7I1SS7iwZIkJWuAmhTbjyMVgfuQmieelgnXWmwZiu9zCkwTZFawYSTr3K/oBxS4hSSP1S7/CgJaTLaKxvWh/5ssiEctdnCNRAyUkumaSEEYb1czPi3bai8BAcie6pk4TwoPoY6BOMseHdf80m/A8sIMgSS1Lv3mjZatg5GxfJPG3Lax5h5Qv+W5St9YGGsnjoFw3dikKGkGz0/qc1EN+2zbfN3x2Zt1C4BpmLuX5aEwsjvfeYPectjvUUVJjHqckyT9eK3xpSs604k8vHDHW9ef58//e6fXJFn7F5fS60phSDW6WgTkh3V0371CkR3FELLzUp6xYae6pphmsgK22YU4YPoqIqspbCJUs6FaT0zh0DSLvNlqZGpMpsSUoxGU0aiaUD8eYJ7r1694u/9vb8H2I77cz/3c/zUT/0U3/zmN/kH/+Af8G/+zb/h61//Ov/8n//zz319FuXjx3sOWsgLaFCiCtJM4lrUwvljWqyvfFrswlw0slC5e/PA89sXxBgGwLPVjRIqcVFisvx7y5W1uCE7EBPduzaaTVE5nqBVsk+PRQx970XXUipFu9e2EV6CQhQ7LVFkssmyYuC6G0/YowQPv5vCumUkmPcV11m3Sa3BpX2EXBphq6Q58ZX3vsKbxweLpqPVnkMUaMFVc6ahooNPntlyZnH9+iE46uF2b4pSNUGLbd2sRFYKRY0RJqVXBJy/H63phZhIMfLJ9185kWkk4G8vOo93O0+gqRl8U2VOcXjwWitzMk6CiI/5UuWwzGSX/m6qzMHGZc3TPGILiYFvfOMbzlcwlp9x7gsPjw883N9TcjaGZTHQMib7nSlFTstiKPoyg8D/81/+m40so0dLtsX1NFOi2AalQPX6PT2isQ2qesq4eQdmjJHQmk3+KbOlgM2akW5vbkwxebIJScXbqG1kvFh3pmYmhCl6K3OpBAo6KdN0MJ1/HRke0/zn2J33jW98g3/7b//tZ77/la98hV/7tV/7M19v/eiZkBNhmkgIqi5MWNSjF2uqoSqS7OYWF6soXrY5r2eWBaqaKOLaVjT1irvH4TEgs8KG5+2G/vauuqCKtAohOMutehnG1WxjIOXMw/lCK15q8lJUSNa6ayGrsYRLYTD2Wi934YtDglcUlHN1+W+EFO0eTCKIBmPZpUioDS3iyq5mrF1yulZrrgnOfivVjGR1eWgbUeXMQq4M3iOCTn7ZfHBE3rYBtgEuXmLUZJPsUsqWkSBc1o1cspfFZBCR8M/qHhqsy7Czx7V5I4nzH/KWjY/vJd1SK2FaSJNtZOAsxF4Tr5VpmWnN9QUVHp4eXYtAxiZRa+VyWbl/eEAQvvriBc9vn5l6swjny9mqI2oyX2FKVlbGcJYQ9wpBT196SN+bh4xc0x8uXiKVUaKtavX7nmxFF/ZoTV2XwCJFkwS3qT+lZGax6VKWTlpVYArG+EwOgnZ+ibRKHxMaooGBI9f4guPdtuX611YKIYt5Z4IhnFVoaqHuumWCJNPLC5HSzHgs5Db+dK4bSmM+zNRcbUQ2DDBJgo1sjgEfg2XAVGlKEsgo6lNpuuZ8SPZ1WiameWbSBaKw5cLj40qr6q0AzefE4Z47WCdh01G66iS2t55HkFHKUoynPzmNdw6BpnDZNi8l2jV0IKi2OnJew9gC2SsF1oo6m/E5VTd4aapK9SXY77/sDTZ+3dbBaFyArWzEELlcLsyzKeR2jf/z+Tx083pIbN59FOR8s7lS/1Xb8FPY7zNqIiTbuiLBVHiipwydlpumRFmNOrscFnzv9+s3oKvX8HPObKXy5s0bcslDoWi9rJxDZFvXMawlpsjzH/kREyLt0ZCfa/Wq0hip7Qs2XlVx7CSwNCbYtXTjF3/e1cHRrk/Qmm3QrenQj2ytUtUGqqjAzbwwYZyDFCNTiEwh+Md0WfiMSPCoxIVNnEgX0p8zuPfDHHZq3obYgNrJIgw99wYULeR8ZooNbZHWlGU52AQSKk0SlUpIgSlM1FBNbXcs8QEOI743Nkc+JTBEM9FGzZstomBquBLswU0CyzQR5JZt22g5s10aVAgT9sA94rVGVbuunRp7ddHD2P3sejqMlwM1UmnEFMmlEHIwcQlHl7U258IYYBUQiw4wIdJ9YZlI5BKjk5eCk1SEaZpsHp1Yfppz9pTDhD6maXKk3xayDaVYUVVOpxPbthlNF0b0MQ7d+/07rXnkv4L1RoipJdOM1GSyZ8KWNw6nE6RE7zGvTmjqkYoqaOwotjHxrHTWWC8X8pZ5uqwEVW4PR6bbZ6agPCXuPnnNtq6kmFjmhcPpgLhuvmJyZm62xuxs7er8bY5gH3ldqq3bIMGjSA9wvAiibpBeOrECiWCToopHReqM0BBGehEw/kgMtv6lwRyCVTxiQLXa3MNQ6WBqVesanUIwQDhFvmz05rs1/GA3gSTOxPJe+wIaA1OcSFHILVK0UYN5cquhWwkmazHdu2hz4m1kUhhlOfM06sw480wxuCCj6DA47Q+mGg8ghsia3YPKEyKY5n5KLDESVfm+PrDmYrs5nWff3vLq14Mm9ojQwDuPuwfuJeqbjTY02iYgAUorXNYLIiasWHImHKaBkutV22ZrplaDLzyrjdfRGoswwvjeO785vz0l8zzbauIWvSpQSx0b5zTPtNY4Pz25PcjVNfb0jD3Up2shqP+uDa+ITi8tpZpSsHvG4KIdh3gAby+1sHlvM+1tvHZthZw3TocDtMbNciSHxPnxiZI3bpbFIqRt4/F8puTM7c0NMUQOxwNpStAMrFU1WTM8kuopWhe6EGAKcIiJWSAEuBSr4kQHNvsTVhXmkLiZF25vblkOB1PqjcLh5kiujZIL25atd2NwKvYoiKojlamOHQQ6b8Slye1BIg522sQji5D+zzX8ZCIWLUDoht8KNUM6HbhJRySCtI1aV9ZmRBEatMsjh8ORMHkomwIxChkDdYoaQtzD5Oo14NYs9+pafD2ETlNiipFaKmXL5E29HKQ8PW2GerfqUl82GmmaJ2NiWT/kXurb7XCUxDSY1gCYN0itN7IYJ733xU8EZgx1rw74qTYL6wARK8cFVRf8UAc7d3mvzneHHWgT5zcEz2lbyaOU1XP6dV2h2QBK9XAy9OvySOFwOHA+X6yl18+55/bd0O3/MvC+fvQ4YIwDr83TBu+PT7ZYWze66/KUXr2LWiUhTYnL5cL9mzviy/eZQuTh4Q1BAs9PN9w/GB/C2q9NCWiKkcM8W5jnOECcJgQDbztZ6dMZ8jA6Q51MNfnmxFPIPF08ShBLV2MMpOXIcV64nRaby5ds1LdGYZ4SUS1339aNlquX5BiiKorhLsZ9sIXV1NLSOcUxk8Cm+SppmsY48ZS8P/8zV7Ef79TwD/MN5/JAofCYz96vXZHZ2HZznGnSkFzQolAbYvbHVioSzzwPxoSa5ghTRLIj6lceB4VWA1tWtBpuYBz6RoyWs80xMM0zOWwUNrZqFN6pwTEkTizoU+VB7o0VNkWSKMn7+oOr2UguxsRKVpHsI5p6Olib1+8jLCGyhDC02Uupjlm48YqVD0VAm3XKHZYDuYDpxFpvg/Fc1HM+M6LqTLY+0TbESKF67lpZ5plWqueZkaiJfM5WBKtKaYV+F2upTGkyxmAubC6yGZyM0g/z9rYJRPf0Aw/vZbEgkATTjZDRtyCdLxCMcKNBTPY72ZyBLjWOCJlqpU1tXM5na6X97vfH+0gIhGw8DWNS2uuKBjQmuDRitNmISERlGpoINpPPSnVVJ98fGiLV1ZcE3UAmuIkTL24OXNJGESPbTNPszLloaysIUY2ghcpIU0JLQysxxkhDvAMwUrJNFm4luxCtgBjeYDMdzFlYObfs6jtiGFb0Qa3/xxr+zeFknPy8UrZink9gPswkSY7iWrdVp3OW2kNp03PXw8K0zISUaDSThcoZIs6Ik5H/aWOvSfsN7blYLpWYzKuFIERVUhR0M+R+XmaOhwO5rKx55fH+ibML2Qs9xxNaMIAveEQRHZxprRjo4yCPtXVH17HrxJhArnUH5bRRWjHqrjjNtVpfgwlnmqHXamWiWgoxTWxrvkLYXXBDrBc9qPXilz67DmhOmw7BRDTXbbMhj2qjveZpYp4XTqcTrz7+eCcESS9e9eMayPCQXNz4mw/KaI1nx6OBabq/qvpo8DjNgxjTB6n28dg9tejnnKaZhzdvOE6LzTMUz8mbia0E1PHE/TxMzCJyOBysASiY5HdpJsSyzLO9v2NMHbC06wGNUFrlabWw/PZ4w/HmBhWTe0vROuasGmvzHIKH7z0Fqs2wk3VdrdEsRlrJ9r0tk7fzXnYNMtrBa96QWngKTqP2aC9JQouLmMZIUkU08FYjyaeOd6ur34Sb5YZlWng6n1HsxJdkghp5zZzzxZpvrvtz/BbGFsm1IM305wFoBnAMRZIrcYTOoOmtmTap1MC+nAuwDumsNBk6r9q4bBl5eGCOiZvlxE068Cwd+d75kfvL2UYypYQ2k3cSCcRi7Lrkobw4UNMf5Fob2lZyiLY5eN620Yw9uF3MC9JQ1/qziK86XdV2sKFL57XuWrMj6QPNHAbaa+nObRmpiAU/zSsPne1mwGGMkzf/VEqtXNaLN5b0EN/baUX6IBinjPozxqKRjgHMy2wlvFI60Q8FF/30ybMpOVjlktvNQLCe3zYRYlModZ+EU3WIbeZSWPPmuISMuXKC0WS1KVvOTGH2OQ52PVb8FaaYWEu9ylT8HgWQZG2wl61Qnh5pCqfjiSlNNtG5NSA4j9+ekYp1kpayWZlRbCBLrqt1cJIozTQML49nWnEqsoOBzcP8Ws05VjFevnFcJqvtezdiUmUCpIbeDPm5x7vN8YstzjnMpJPllY1mwFtprOtmCLqYrNUhzJwWY5r1mfKbrlbyoVmfNQaqFFXb+bsogud0JoJpbDzo4B8uQGmtuzEJIeFadEASzlvm7u6eeAOHaeb5fMt8uuW8XnjKK5eyQVNys/QhxD4zwEGytgN86rXd1WWzR+4Y3FMMdL9TdncDsrDFUN0m+8iryWvew8t7k7p6yay36LbWZa4sPKy12qixnG3z8BTBePaM3zuebris6zCi3uwzgEwHSYO3Mqsbu+A6itEin+PxaCw5sE2wVwCC6Qf0TZIrsc4w8lyGlxNVtFZe3D4jqg3VFL/OiG/E2qwrUW0ztmjOWKBWRFKkdXqyGocj2dzEzfkDll70tk2GsGsLLgJ6eSLXwnE5cJxnZrxpqk3OO3F8ydtxmQSispJp0qjNdBXzupLP9mf2Jps+cyI7RlF91apC8ogxTZOnTObZxKtG7UuhvXds+EEFLW2U4qyEEm1Wo2LkmWkZD+8wLRynhaAOmMXIJ2sBsRlmUdXAMB9X3EPO0V/tEWNHom14QxfS6Nuj7fMipuDLHNl86MZ523hd75lD5L3b58zHxSSx4kLWxtO28r1yh6qyRqOjVm2oVxm6CxGzHF/4Huqqos3adK8HUiCY/JgvaMS8etPmANsezuWSDc+4assFq1SkEOhS2ja3z4y6ajGvEwLZuyJDMCPt52ADN+NAvOtV2NoBu6GU6761s9hUGLyDZ6dnFiVUI77YyGmba+BLmmlenCm597l3tSPbjOwTU0qcHx9Z1wtJjPgyIhaF2IJ1NdYebdgI8xYbMU1ewuvjua1zU/28Y/AZAGK9F1Y8ryZs4mGj+XXDpPJ2ZisbeZ15Nh+I02z8e43+7GxCrgQhqvVV1Mlwl4f7R7bLihZFL5lFTKxFsaa1i+poL+6uKiHe3TcTg8047E9ilUaKIDHzZS7/3Ro+4gSEZmOYxGbGE4TYbGoLagtamjIRWbDQOKhwac0bWYQpRmhQiw2qqE5sQfCG346oywCSwEM5N/xRguqlKSzPIwY0NraiaN2IVWiPgeetcpgXppSYQuC4JI5fncm18P3yyJvHeyMATYktFzd6Bxsd/PrUSFRfUvh8PdsQcil0ssGSJqpafmhqOia+8fT0RBADAK2v2/XqvYuuNaceqw7STZfqenx8pOVipJ1gG3DJmWmePXqwEuH906N7GBOM6Bm+ef2rbN9z094sE0LgMJsQZvUUpTcYrcVLpsm83A5HGXpvnZOWzzbvNeideqUWDqcj2/lic+P68wTTzC97Db6TbzqbLolYPS4YvtKANCVevbkzuTE68WmQdel1CUUpMm6np0oV3S5oLmxxshHgWSx1a8bxOJ2OnJbFGIfY4I+1ZO4fL8wSzBjV1vam1dSf1TkmnraEICxqM/+i05NrLUzLZJWxQ2JNjRAqcPhC23vHOX7xFlzbFa12ilNNg02lccPe6krOhVXF8ubWuEihhkKUyJQitSjntnIuhRIbSbxWDwbkRIXaiSCW5Ko6d95bcZo2pquNsivEFKA46FYUcj5z2S68uLnl5nBkcgHLY4ocUyItEzchcXd5YFs3S0OiULPhGEHfDsU62DiWmO65b8aGVgiCBm+4UZs9YApDzcs7gc3VXltz0Kg20uxhs+yhtXp7Z6n5iplm3rL2UBzzyikmHh4fXW1IbLzVVQ5v2P8eVfUXa2sD+T8dT45L2HMupdHZcXE2+e9Si21mDZvI6+8RxdiSpqzTzMhVKLkQp0Ca0hAoqc7RvzhmNE2mtd+0DQ9PEJ9/Z/TnNE3UUnlz95rvffKxC4RMNMdESvNhJYOroDtzEKdqiVGqawi0FCgo57zxVDa26vX5FJAZ0+snmiKyA499i0o+o298b6xFGdFij7RUG0ULEmE+JQO5D4FCxpLG/0MNf9tWpNrDCa4LZyO0CqgPhYg+VWY2L/fmcvYSmbCR4Qhd2YYgbNrYsGEX6uUPzxs8FLTP7gtWVame/+MPoTviwWnHZrHl0jh7F1tKEUqlPN1z3laeH04cltmktQI808Dx9JxlStzlM495s08NuIpvvwsddupEGD833QG6pvvCaH5eXY2nh/YphJHHww68xRB91r3x49Vr6NnLQF2nDt3FM/phfHIzlsvl4t7Wyk9S9x783dd7msSVUWjjNB/HlRZvoFE3pC6DHlKk6+T1icE2BdiFSLyU11xqq2YTHTFcwgHP1JjmyUg5NB4fnxC8a7I2K59V478be3GmtsLruzseHx95uJzRFEkp2bRiCSY9Fmwac+/rGE05/XlgsVpQcxAVKzk+lo2nVqhYt+Nj2ciPmXmOnnpVVJrjzsasTDEgtRHShK79tvZF4Xc4KBpMHDYFZTnOLMfJNvhUjf6bq4uZfP7xTg2/looWGwCRQkeedwCM1sZEkBgTIRbKevFaeKSG6u2XOJBjC6M6d3bYliWL/ve+OD3sVUYYqO6tDJTag05wDbo49oexIxdtnPPqrb1eP61WwpmmwHFZyEE55zw2kV7aeSuVl93w+3f3LrceYHZATZ3pVscJxZQol8vQzBPMcKfJS1ZimIn49cWYTO2mGdZhfbz70YG5MRlHDSCJzg4bSjlXHr4fevVWioXdptHXuwN94lAInnKpA45pGPZbY75qHW+mWESI6gAjrbRlUYIm2yRisjq5TUwKTPPEVkw6rF0szLcGrwvnxwe2LdO5BqpdDUoshKe9pWSlb98qWx9qUu2lVrJYVl6ajS43PMCm7CBKnLyBWXuNni69ONZpF0693oj31eD3xpWGUkrEFEZE17S6Y/jsefbjnRp+U5thv22NWkwEIcU+qthWVPTcNsZASAf0CK8f3xj7DWUJkUZjpZIVa6nFwvLELpIh7j3N0O19bWaZ4cbaHLJR9pqrfRPUZJyyN8hYO6vpnTeCKb7kjfV+4y9MHxAlsooS1sYxRA7hlpvTge+3ez55emJtxqsP0erOnWfdDVYUqiPJ3a4aYrYpRs/M28q8zI7SQy6V6sYbxTnesoNVuRQXosAYc2Jpgxll9s92lV0UrRDmmTglXt/fW3gstuhytprzgOBG2N8jKytf2QZjZB0DJQtbtq7BlKYBeEqMhpKjhJhsLl0U8rqxhInsXZSlZlMoKoXzttrQ0cuFWgtzmtEAU7OybmuWh5+zaekd0w2aAufHJzRf+OT8xs5ZrRGmBcNdqlYD4CyTN/FWTK8hKtQWoFXX6+vXbUdGudfGY9mMRAWoYwWbNmpZiRWmw8IzW1Y0gQlYXPKtYRtdbZnskWHQHV1Iqmi0EWNpCiy3B+bTDMH6VkqunM8XtofKiw+/2PbeLbjnnoPWnEev5C5uKNDEFEamlIgyEUQ5HQ6sdeNpPVNUbRJstdFFecs+0ooRa/ZdVdkRYdNF2zn0O7Os88o9WNV9d+91aAFD41svCFqIVmhIg/unR26Pt6Ycowz+wc18YH7/yOHhntf3b2xuXm2jG6t0AUq/N9dOVMXfZ1iYD5e84iVUV+EVF6fo5CRgTwswT1Jqsdy71hHhxJS8RGYeNNfKzTxRamHdVps4Kzs+0mv216F9n1YLPrIc5XA4+BRdtaqDu06rdtjCj2oNqF0eunX1H2yj2rbNO+Js9qBqYyubTQ1uJkISonE32mpDSC81s24bXfVwfby3Mp4jvn04ZafztmGoDrxyBep5taAFk1Cr16Ha1V2wAkSjD7zcU0b7jej8kiYuxbZurJcVqYa/J3cEAZfP1k9FXp6mqSjLnDgeJ07H2cRUsLTofL9y9+YBaZHPEbQfx/+y4f/X//pf+eVf/uXx7z/6oz/i7//9v8/9/T3/+l//6yG39Q//Zc7wjgAAIABJREFU4T/kr/7Vv/q57xFiRJJSi42njljY0u+nYrPfai4kb0yJKXE6LFSKMdUkQjJ12lKq6eb1G++xe1eTbV1aV7gK1/oK7puA/6z5jt56OGZh+JguC6iYFxZ0tEu+eXpEJfIiHpnSTOjg4VqZ5sQHz57z3vHI9+7vefX6E3Kvm4ew93ZbnP72zRKLWuQqimlXhltyMa/uenIWx1ylM64d1/vAp5SMcxAMHFuigWnUyqNPuknTxMd3dwN/6T0FvRTa79tg6juQ2vGKaTIWWwfXSq3WY682Jjs6p7zW5p1pMgRDWi4saSafOzAaWbcNScLd/YPN2IvQggGuYU6QIpd1Y8sbTyXvCjrgoGwbm38bGnlGkuoYyvUfVHtxguBX2jooe5WS0YFidiPvm0W/NdOULKKqljY+XQpbq7RNOQAzwfsXrOmr5J3gFAneNuz9AhMcjxPHw0QMzRl/lW2r3L1+JG/WLv5lx/+y4f/4j/84v/mbv4mtlcpP/dRP8a1vfYtf//Vf5+/8nb/DL/7iL/6Z75GbDYGUEAbxo5TinOPocsFKK5az6FopRbnPZ7KXeNbLBYIJUvSctfdRd4pm99Z0b+WfL5/aAPoS1qa0GgZ62yU2JYBR2J1+6q9sAmGOaKusW6M+PVDryoub5xzno9XB1Wim0uA0JT547yWn+cCru9ec88rWur+x6MNqxIwFdn3O4Dz55mQXxPN3cY9lm1AHPa8Vccb25uUxa7TppUxhmmdSsjAakaE+pFf3rnsXGZFFeOt9EePbL8thzPHrvegKzmoLA7lIk82zq6UYrwLrD5AE27qyTBMPjw8spyMPl0cbgiKFGho5QBVFa3ZdhUqWxibqlFv3347I9YrIOJdr7ORTf0TVsBsVpgahClu5igL7erEbxMjVPnUvBHjx/AUR5enpgXXdyEVswnK2aTiHaTKyTi0mwqHO80+TKUVFs/olROZjYJkTUNkulW3LnB8zuTTKCoGpn9UXHv9bQv3f/d3f5Rvf+AYfffTR/9TrSlOk55t+C0XtQWjzC4+BKUW2mllLZiuF3LLPhIP7N/eGvE6TqZZizQt70Awj5uqP2j2VXvGAryN8p3t3khgS1EExBt1VPd9WUWdyGWq+bdat97htEJ6QOHFI1tEnjaGDP88HXj57zjLPnC8XPnm85+78wFBSATNO/Dx64OnIdl9o6pY+xEV8E7hmu1mfQh8YYarCpbjWP5bzy2iVNaGL82Xj8fy04y09AvHYtYf04ueJb7QhWEkqSGDbNuPg+4DTBtCsEQnfcECGNHffqLUph3khXzaCwP3DG+bDwiWfTQdxyzyWzZqMsE5FU0w2z10DVlLr1zdyv/1L83O2W3rdXehRnSWcTCqkBounIYcmbKpkfzO744PGtJtbX26qHA8HDpNFso/ejtsHjSZgCtEqNa1YmhFMCDRJ8tKvVZtiDByXmWm2+7ddMuenM+tqfSiqgkj8VKXl848vYfP+4Mdv/dZv8XM/93Pj3//yX/5Lfv7nf55f+ZVf4e7u7gtf1wUsizYqBnIVjKK4tcJWbDz0hcoqykUL55ZHDptiYJ4Sk0sN9Xp0Z6Z95thjsYGMG0LqhqR7FFCy0pqg1V4jYa847GGcPfaG8a9rULIYkHNujbvLEx8/3vGUzxTKUFENIaHrRjuv3E4z7z9/wY99+DU+evkjHCSQtNnkHnrv9b44u/H3Ft9Rz3Wtup4TDq6C9nKURQmxo8gugOFvZ57W2XkKPLt9Zo08rQOeHST1zkb26KN/piOkJhjpgGJHoKsj7bVLjPu1xGT9450aTFOKl+pyydRaePbshrWcWW4nsmyc65nHLXMpNpiiBaGI2thx34T7Z1x/lrsWM9de2+1G369vsHN7i7QwE5gUkhoo3I1KvvSPjL9DV3jOZKdVS4FQYRJhdsXd4tUoISDJOkBVbRxcCsKyTCzLRAqJslXOjxvrpVn64H9c0cKIZ19y/NCGv20b//E//kf+2l/7awD8rb/1t/h3/+7f8Zu/+Zt88MEH/NN/+k+/8LVdYacC2Y2+iiHyBSW3zKWunOvGU9t40mJ1UrXGkNoqL7/6VW5OR4II67paqKuf9vb7X6//vX8Tz9F2L6rawR/74W7w/S06864v7GbcgQhFlAuwodydH/j+/SvuLw9cyoZKIMSZxcUX62VFt8whRL723lf5iR/7i/zI7XtGQYXBskMYrDW6LLT2MNWnxfR8U3cwCHB8w+vuWBm1lOIbRjFxTgfuqi/Q0+k0RCvDFa7QW22H0V/dxBAj8zwjgol7eMVijCjDwu84JdI8gXQRkDZUflNKzGliXS8+iy/w8HTP7fMbwiQwKU8+RRh6z8A18Ulde6Fv7P0kezRkf4IaWi7NemmiG/ZMYBL/g8mfR/8cFaEEoRB2NZ2rJfQWPjA2F9hK5uHpkfvzo5UvUyRhKj5JXA6+FLLzG1AhLZMJZrrg6zQnjoeZFCFvhfPjynqu0CZajaiGgcO0PT78wuOHNvz/9J/+E3/5L/9l3n//fQDef/99FxQM/M2/+Tf5gz/4gy9+cY+ffVFLNCJHSBFJFgaW1jiXjcdyoaLgubW1rYJq42sffsh7771nApe17qHw1bX3/U8/z+p9MZtn23eC1naKZn8DvXrKY0d3j6gObhUXqJSUkCQ8rhc+vvuY12/ecNk2E04IkcO8MEcbAdU2E608TjN/4Uc+5Bsf/Sjv3T533ridT9e6GxuAm32vCIDsnrN/J1x5teC13mZKriUXepjTATgw739/byh4emsii3zmvx3wu8YR1nUl5zyUbmvzkVYORvbfl9AJPE7ddrWgUiuPT090Ka733nuPmMSXdCVOIOq9Ha5Q3Ddh6FGSXD90rpWFUR2NUUmC69lFJgkkLBWKSFc3A99MLnnj0ipFPkX04trg97Xdv1db4/F8Zs2mnrs54SiJE3YEcitegu09K8H6V8SUn0wcpXG5XIzfvxVUA60JYuoHtsmOytWXG/4PneP/1m/9Fn/9r//18e8+SAPg3//7f89P/MRPfPGLnaKpHSRRnMDhC0FMK866m8QbthqHhNVSA7x5/ZoJWO/uOUXhsVmjR+Piu5813IB5K5taanx1A12DL0YHBBVCC6jUEdISxACkiIUn1QEuXGChNUKFUKyJQ0PwFl0lLhHmwDlXLg+vuN1WTssNz04H5mn2OeZmyCUXG/YQAy+Pt7x3OPHq/o7v333CelmZxMRBooarXgNfXm5QOha9jHAvYgMkWnV1YBFCTGzryul4pGzZS02wlY1pmlmLiXxuOVuZbph7D5Ht86PnRoKSYmDbLq4TYIq3IaTefYCEaHiItlF6xNOMqiYzbtGF8QCaVJZnifAssG6FdbuYaMq47t4JaF4yaCA0kw/TqgQNfm7+GPHRkvL2Bhm0rw/7mq5Kp6vY+iy1UTybD9IGnVe70+A61wdrn3a/qj7RFmtMS6psotyqclMjuanNeUCZgRqNaFSq97OocrmcaRehXjI1ewQl0KjO5rQz6GezC5V9/vFDGf75fOZ3fud3+Mf/+B+P7/3qr/4qf/iHfwjARx999NbPPn08v7nhzd2deY2OEIdgyrMumGGKIzshBfCktEtKF159/xW1KktMXGphW1fCoV+4byxfch+6DFcPY/tsj1HLdTntt46+o/prTeTDGHsNq/kWGnO06CVE0KDc5yfutzOPlwM3pxM3xxNTtNnvScOQhS7ZvOPLZ885TjOv39yRL1bPbq5Ddw1IiYezI8ccob6PuuqLGwv9SzbprG3dwFWCn56erNQ3Tbx588BhOUB2ck8HP7R/1j7vrnc/9hTCiDvRrsUFNsSHl/Tv28ar4317TpzzBtWGnk6HxIuvvMf9+Q2VgohpEgbfwEMwozdsAPrADjQQXGrNM+ZRLQi9xHZ9P67DOOStQLlnWjaPUOlZeAdXe8Hoc1bV1WfoUHnGa/YRmGKCIKwlk1HH4u2elGJpbVDItXLZNnKD2IToGhOOTpm9XJ3Gl5u8HT+U4R+PR37v937vre/96q/+6g/8+ufPTtyeFh7XJ+6fnqiqbCWjTubQ5iQeCSA2waXQvClnRrUYW63ZPPdaG8cpIdXELN4+5EqbYg/VrgGZgHmw0BcPNkjzraYesTZZWjOPgm8QTSlboyNLGrHyTGvExJDq3mqlVsjrI49l5XQ5M6eJ47LYJjDbhJfkXXdSlWOIpNvn3LcHms/kG0hj/7tlQfTcMoyc1sLHwXFvdQzRSCH610Atza8F5jQRgwF9nUuvte2pMjoMyDZLY/wV1/LvhCL1kDWmZJ1+g4vf53SrqRVhKdo8WUv1+fJICMr7X3ufp/WBdJiouRLV6txLgDVdpWfNFGdsA7Jcd6b5oE2PFLshdm8+jMWN0y/O9/zxsx7O9xD6elX1NM+3j0/9oB/qa0uQZjX3oAbqBTGG4KbV25eDlRDnCaTY/IJmUbBiJdKA0Jo1VlVsSNzbtSkZa/nLjnfbpFNWclmpsRFPiZoz51yoLohpFEYf/xvsFtYG2YGiZXKPo/aQUpAxF7518OZLz8BDNHViBEoi+Bw5GaFaH9ZI0LfaeXdvu+/u0cuMpVlovW2VSTuppjdx2MPaauFyfiASmB8Dz0433B5PHI9HwnQwwUQ1BZpJhSVOnIv1xHMF3u0f3z2+j5fyc2quntM184wr4dN3pokQApfzhRCEeV548+YexYC2UoqXWK88HLaQqw/A6KXCLnbRDUi63oF39fUaep8NMDCSpjaXwKM1BN7/4H22siHzRLb+RGvpdes7zYvRpjdrSJl87DUqLnyzq94a56B7dh1RgI/DsHOT3nVnwLL0KPE6l/cbrZ828s/sBnvADT3mdMEVhSkEJscl1lpY/R2j+NTeOdFKZWuNqRnoeFoWluOJdmnc1TemNt3P6601rX9mhAvv2PAveWXTCzUqOVo5oyQrpZnciV1UF+JIybxcVsvdW25Mahc/qaGvtMrRBSttd3Xf51Hg3gzTBzFa6SZii9mELy1kjwhKIEjnzovLJtl5hdDHPNv7dx25Vk2Eoc/DK+5tAUdfrQzVaa8Vy9nywxseL2dO5yO3hxuOh8VShWDU5dq6TLbpC3Zvr59ahx2r2Gu5Jqyh2qjFFHdMDMS14BTmaWLbNpZp5vx0JsaEVmurxX9HxIdtdO/O1bjwHllgJxDCDiiGGHZjEdMa6BN+xO+jiLCtK7UVXrx4QRGXIMPSPcMKKkpEGyySLN9WaCGa0VdDwPuu3LXuOydCxh2CvgGAlZMVx3/Q0Ti0X5iF/CMluLa08Xe5DgHGlyA2WSmImkQbxtJLPvyi1Lw/N2caLscD5f4yqCcCnJYDx+VIqZm71TbtXqocu/Gnz+1Ljndq+GvdWNvFOqVCoIjNLDfnFQbBpo+mCjEQk6GbpVRqAY0ywvCAKfGkAEUCW6uI94SDd76FThKxDL4bfRSxv7tnuDYgA/EczJNAC5ZeeMV0J4AEN7XSVXyhlebzAyoQnY0X6EFm9XwVHwpRSub8kLl7fOK4zBzmyQZ5YNLhGvqgSLli7UkPAhhMxZ5fXiWhXSc+YBcmvqJrs9z85uaWbbXx1DsX345O2EHboP5CFyw1MHZ4SD+f0JHpsIO3OxOqN/FY1FaajS2/ublhWmY2zaahoLYxt6LkS2M7N2oNhM0wCqlqHp5Gd3PC2yXdfgc+/bXn8sPg6eG9X0j3pp8ydLl+07d/dPWBthZigJQcAFbTyZuDpZGlFFf0sXJi7A7kMFHfqKP0HumqjZtbNxMd2cP7HySj/+zxbrvzRhY1yK++LuKuEiqDNYrSfE5dZL1qalGXME40YrNmhyXKTtZz8GpKEW0+OtqnX0a1RW2hPlcEmL4Idn63bUi7H219oQf7oCaN5MRuqxK44Ga0xd48TDSNtDAwA1Ujn0QR7wJsZC1cLplwgcOUSCGyxAMpzhaoBjy0hgFewMixh8yYe6qmPnPOSdzGQzD2Yq2mZHw6HHn16pWp12hvWPL3xW5KrcXxhJ5UcAWSeFTjz0y8LU912ORwTrkUF8f0BqOcmeeZm9ubYUF1s+9v3szy9HQmrxtad0xCdWd99lXS810Tz+xJytvrro5XvW307Xp36O/XjV37pV7j91989O7EyQeZSG1MBCLedqwmFRcxYZPkoGhxx0Yzw89BudueoKzkNVs3Z5c2fys66c/q7Wf3ece7Nfy2q9D0EksKlSYWeHeLD2KbQJqsRznXbNNpN6FUa5CZxdhs1uXXiCSStz3W1ghhMsloLzNJq/5AO6LvS8RBojJ2fkYUJ36e5kUb4+460FVRJrFTVx8IIQJpNu51K32rU/qtF+z3hegdf/uCFHEsgAoEUwYSx7/d4+vVKVg4vof3vYzZKZxdZde4+c1BODvX58+f8/DwYCF+2zvuxibn/IjkLMCinca8G3sHzAc3oxuIb5zi0RZitN7q19sxiWfPn3G5XCi1cMkXnp6e2NbM5WkFxAHGfh0dWO3P6dr4+z7UdQR6LuwYg7+mu5zqxn0N+PbjOrqXqz9/ltn39whiClI2184iQelGnyLEQM4rQRtRbdhGmtPgHdgk5sCm1ZV7XK4bcy5vCwXY/fhBwv13m+OL9dBPHvpYNieOygmxRZQC0ggJiKZTkmsgb2YM4mOwCrA2ZUqB5qOFpnSkeX5Ziqu01EZeKyrCHCcO08RxtpBaxJR1JJgr1tZMdVas1/qTx3vydvHpNY3QAjHN5JIJESaEkCZS3dDezCEwueIrqtRsef7o6++7c9fcd7fSQqC77SlFtEKSSMA2suQCGiFae21TB8yU0aGneOuz1rFIzFgFqXZ9orAcDogIOWef4NqG4aI6fi8SUDW9Qo22Zb5l7LADev6MLSDxIpjntU6NMTGVYqKogcDdx3fc3d2Ry0YrllLEEJk1+qLu5bodmIPuH+zzd7/PEPOwencbHr3zHXT83g72GyWgpzA7On6dprReygueWsGICpufn0V6Jns9qXCYJy65kdUAPVE4xJnbNBHWjQasEV589QW1KetmeogSAiXYNhWLUtXmOo7z+VT1Sq+91Jcc79bj91AXHbXd3iONVl+AbVyIDY+o1kfed+LqVN8QmEJgLY35uFj4eLZmnvn2xIsX7xE0keJMDBNpORjg0xrSmiutjFjORmjj+WVTqig3z5/5YIpm4WeufPd73x/0WpqaNHg1PELE2GrrujIfJsIUCKWNEiSio9S05+Iy7k1XAkpe5ukgVYzBvfmVhxs1+zB2nI5tQP+MXi+3363NxnDdHm55enxiaB5Kn/6jV6VMX0lBRiTjHzyyjbd+IHteL2rRSafXtpqZ00QtmXlKvH79mtd3r+mKwTGahHry6KN3EO6Xq8Mir5d9u/pXt4fGnsP3fB4xQLWnc9b7f5UwjP9cW9DnudHdunrk0+94UOkFDgiQ5sRUG5dzZqvNZicqVwBzYD4eEI8CUBMjMVDYMJVrusGfYdd/5vFuhThSBC3W1qpqlFf1MLmriKv3RPvuqq0OFpTtupEgrhi7LGhr3K8bz7/ykg/ee27a4yn5JBKT51LnR9fqs+cGGcNJEab4aSUqdaNRa6tFjOBzSDOHKfD1v/QB9w8PfO/j7/L6/jW0jTlOrK1677tdp4raqCuTUu3rwa6i58RXj7O5RzHIwIFIX/yB3bPa63tKtHPI+pp9qw3Z3wcP01utHE8n09Sv1YYwunCn0yj9QYWx0jpwt7/pVTrU0yBhsAaviUQdTDwdF5vTJ/B0f+b1Jx+bsYdoPQRNfRCK17Z9qs/Y7K8MuX/2wGE+RWYZhq96JWqp49Kuz/E6DfjioP6zG0CPfEbM4elUcGovwTOTyDgH26RdWxD7fpoTKlbmHXuob/CjIfP6rH6AkP6LjnershvEXRsMzyR76CZ+40K0Lq7r1DoiLtho5TZFKQ1un73go69/japGWmmtUQsk4giPLiVT12109QUxaWVVF/rsIV1plM2IFA1lPixEF4Kcp4k1Zx5evyHEwNff/4C/+KN/gTcP97z6+BNqWf2hW295iMGbawyTiL7f9MWy+2H3NL4RBECcoNQltfoRHBG3tzDAx77KgB+6jt9QcvG3r7kwzxMxCA8PjyPE7/V/bbthjSlAMPgU2qsCu92/tcGMwz+7v/88TTzeP3I4HFjzyievXxv3wsHHeVrGVJ9u3LUMQbph2LWPhxrRbgfyOq7hGxA7aNc3AVW7jilZZ6fNJ7BIrs+y97u3X8Zno+qrJ4fbuzI2dRdu8cgfiaaO3KkOQYOrAFltKKTAcjwg88Sb+7th9KE7Q3378z9vS/qfOd6p4ZvH9q3NtzC5Wig78LM/9LEfW23OkFMRCsKHX/sa73/lK6znM0VNYDF6vXjbrO7c585PcWRvnsfqXgJS+6xty7Qtu8c1Y8HbWcuWQdVorSI0tQ3i+c0z3nv+Ff7wv/y/rK6sG6L7Wt03t31t6fjSF1f/eTfUWgpLXK5C8D5Oud+rPd8evkq4+jyH2Zw400qh1kIIM9cNNuLvPZ6O7F1v4xH1z5HdC183xAyTFwtjBwmqS217dKVYc9GlrKRgy7ArAqtaWbZHDepVEvXoqxvT21SaHRS9PpEhfSX7/ccjyOjRoEV/zr/397omQF09piswVfZnRk8MfC1hwOLo/5CdO2L6DhgDExkAZ5on0jLTVFlzsUgxWFnS1s4e2Y1LvLrZ12tLP2+H+tTxbj3+WEqeCokQgxIkEiR6btbMcFwjLUrw/nLrTELh9uaWH/vRjzjMM2/e3HFzOqFbMSmu1tBSmJzJ1VHmgA4rs4fcBUF6XblAazaTVq2XPE4dbbU/85yM3FOsYUiBuhVKK3zz//q/+R9/+l3++E/+mMNhQV1GNQWhlj2YvHp6+33pYfZVuc5y4kKQZF16AaYpsq6bj8a+Cpy0jSYNyw48Rw7NNfUah2VhnmceHx/f8tb0J3KFzvXwvnuZLkW2O/z+Q3+eypDQAsNsUkzjPk/Hhf/+p/+Dc76QQqSKnVMYMQ7O1fMGmaajoadHMtU/t28Ag0ugPZDer6iXM3thzyYFWXl3nhKrawzGGGgucBhs5xllN/TzDO3aIHu6qF4W7ruibT6lVWpp3jxkwHKaEpe2Mt0eeO9HXsIUebisPD7a0Mz++Psm0R+KDNkzt6CrDe0HjQLereHngoZm7bhiizmlgDaLh0ptSBQkdlBPEY3MaUbUiDTf/EvfRDBtvvvX99RSuciKuIyTAARn0bVq7D5VbE4XQCfm+MRaVaSaDFZMCbQaFtDDOJ9MI4gNqGweYteGiDKnycCZc+bDZ1/h5e0zqlQ+vv8+9+dHnh+OPNQntO7eFE9Z+sI0xZziHsM02IPzzmMMY0z3tUcf4lfqYo+hL/IwYCqwqsYyGxf/8eGhP4m9fORodpfG6rvCFXrgBrCb1nX+Leo5fTAyFGKVklqLzdBLiT/63n/j9OzE83hDz2GNpzAzB+MptMmaU+7uH7i7f7TSr7/3TqfdQyPpgIi6ZBptTAUKzo8fZU0ACi1n1lyoxRqNelNPnGGaABWq7f9Uro1PRpVEO0FBuxpvsLkPuhPPQBCN0AqaYZKZebKN+PDiRDpNbLPwcH7ie9/7hHWtxCURQxmgninreKUhdhZo87JxT/H6E7qKDr7geLeGr4GCOFXWSDiKaahVaQSpLjAR6XQMW3mWrb189lXO9284P11Y15WQIvM8sV4uHFK0rjvtJbmw01grNHWPpDrKV8pedw5drorOoY60XA2QRGjNB1a2OvI207pQoKLZHkQKhth+7b2v8fJ54f7xHmLj0ee3v5VPiiHaLUArthHNNJaYWKZkm4U49qFmxOqMsODesDX7R4xdggmk1+WborWxWr/n0Mo3xH9n3kkIVI9gBhf8Ko3obmxnCNq1VrWNsYkikhCCXU8t5kUn4VKemA6JJjY5dko2ly+64VYpFkW1RPX2XaN7eNejKBq6V+0bURhVha6sJDAGgPbUYkR8IRDC4i3C1WY3KHYDoxIihKjQZEyyaZXRlzDNCT8DcqnmkZ1lGjz1atU3waZINUWdWnW0eNcAcYmERZBJ2Grh/uHBpvYkEKmg5nhaM6ESi+giIfRow8OJvuxgpMVRvlxt8x3r6u8LL3jumMQuptZ+Ix2tDvtOZrLLBpT8tz/6Iw7zQgiRSYSyGWhVciUGaxzpaHUPVtVr3mNQA4zSzjhCGCU+CzPFp7liNfwQHBMwr9OHgtgYqGgDFqt7qeCjrYLw4tkLbm+ecXf/xPc/foU6j/8tD6Y2PQj3+tFVbsU9mP08sG2Gxk++cLXtYe6Q/24N8TJp8YmyIsb978SZ6xJcDxc/nbd3fzc87lvo+U526mG1VTP8HohVNta88ur1J6SbOPJ1kR7N7NLctpcoectsWx42Kexg12dSpBFu99PusFv3yN1zei++7NejI03RHQRl2JQvB9nxDN29buwpVLuOgNRWtOf2vdUasZx9miLTYSJOCUm2Wa/n1YaSeoXBnTi1mg0Q2B3XWCp9p77CL/zzP5W7feZ4x4YPu4CCIA2mGEkhQq5s2A03sQ7P8WIgNPNebx7vkWCKL1NMxtueJxSbU2eLu46uKtRz4RB8pjuEEH1R7KwuAJXgc+mjh477BmBDKd0YvNW0z+aI0ZpHmkAL4mFutdy1OWklV756+4Jnxxs+fv0Jl/VC0cK0LFzWM4dlpqwriOWCNs1297JpSsSYEDJR+mbQdulv8Xq154FBZHgM09nrYpndRHAmoDXZXL9PXz/227Lb17COPc/segoBy59bs4GOaU6sNfMnr14xLZbr28K9Lpxde2urW2/bRskmJBlg9FE0hRavV7ZvO9r/sxu9fMoorqHxsbf5SRiA2PYfOh24A3P0yKhXPdQix2tvG3q/hm8S2jdkN/qQAqfbI2lJyBRBKjUX6pZppRIkOn+/g6cBtBHd0Psw1S62IkNrwZNR3/CvEsnPPcKX/tSBGNegAAAgAElEQVSPX/mVX+Enf/In3xLUfP36Nd/+9rf52Z/9Wb797W8PUU1V5Z/8k3/Ct771LX7+53+e//yf//MXvq8QCDWQWiC2QCKyxIlDmljEeM6x03b3pWfhboyEKZBrcVHGSs2ZumbqlilbMRXW0pzwY5z71mxgRCmWP9VayaXYTDe/gYhNMpUYXT4rIikOwlETsVKkWBuwqaJaNNCcClvc+EtrEAIpTERJ1KIc5iOhwSSRr73/AR99+HW++vw9tvOFZZoQVas6uMpQj1L6YMwYovMZrAdhFHlV/z/m3iXUtizL6/6N+Vhr73POfcTjRkRGZkZVgU3Famij8FGgSKENSQrs2bFjR4RCEREEX6CIIKgNqdSGgi0psQTtFEqVNrVjw8YH32dVWmZlREbF6557ztl7rTXnHF9jjDnXvpHxyE8/ubUyT9xz992Ptdeaczz+4z/+Y7D2/AL74IxmUYqI/3TZKhkim8Rg3ymISzm755GL54Wd/qNjddtHmRO26CrG6MawEXPifjnz4ccfkQ/ZatQqeMOlk7YYvQV2j4xjsJxXm+iDRVNzCkziQhT9Q7kwTuFiQ/g5X34HMKPWWnMnoF5Vgj43QUYd1DZUJxCBRwvRKDq1FIrzH8RTnRGxXJybvc6MSm2FaZ6Y5kycIy24xHmz6KbVnoqYzJh9rBEAetlaHFQ1spU3eAsj3QxBSDmS4ldv7R9r4//8z/88//Sf/tOXHvvud7/Lz/zMz/Arv/Ir/MzP/Azf/e53AdPg+973vsev/Mqv8Lf+1t/ir//1v/6l75uI5BbINTKRmIKBTp1FPYY2+ILAxxbZvyrLujAdJmKOY5Rz2Ta2ZaEW2/h2g4qH3XbRaitjfNUYHy3YYu/19pQIKUEKtCBotM0vORFTImUbL2UbxV4TfNSzzVsSn9AbRw9BCIl5mi3KIZBjHgMfH13f8O5bbxtnYCto2YhinPYYwwj5xUk7ZTNOsCjOOVW6rJRqJ5AE6yJ0pmO43Oz+Q9/sDsjhRozLTS/CBUjti9k03rpMdYhh/C6e8kgMPKxnnt+/gBRZa4VgoG0t5slbHy6HfUZvA159mKcoHFLmmCNXU2bOkSn680W6nzMdBBG/HT6XQXbvi+7RhW1om4xs46oqfWjHJSnK3lmHQQJsPHgQN3A28i10VqJ/XudAjOBCoGol5UieIjEJXURzK5tzGHZLEaJVrdTR/D5WzkL6Ns69i6F2K3OZEseXIqIfPX6sjf/7f//v58mTlwfy/If/8B/4zne+A8B3vvMd/v2///cvPS4i/PRP/zS3t7d8+OGHX/i+B8lcy8yVTBzzgTnNIMJaVuPtiU1nrdUuTPOhGa1WW8whUFq1sUhRULGutrVuFj4V/3Epq678qmoIbvCNGWIcir/E0PeRg1qBkDIhJ9I8k6eJmPPw9iGah+uTYQZgZ+bXNlIMhGwSXFUV8eciFnqnlMgpM08T3/7GN0lE0EaKgRTFPYqMhQaGT7ijGtJh1tduC036Rsf+vXv74f3C/qNuvHqePSKCl/4HHRwzr2mG2L6rhb8xWV9AbYUWYdNqTYg5mmahr8VSqt9H2/yj3+nCsKylUosRl5JWpqDkBDllUp4vWIrQXxU8BB5phOi4DZd/DmRC8Tq6oxTSN4+/rY6nehriHYHuMIawh3vc3kVqOz6Ml6unmClZCmolPzXEUPHycRsG5rK7Tuj8kp5+7SzK/lF9mAqOv/T3/KrjfznH//jjj4eo5ltvvcUnn3wCwA9/+EPeeeed8bx33nmHH/7wh+O5l8ej6UhuamITAkUra1142BaXLrZyieqA/y6+s3nRquo6bs1Vbq0hRVZlnmcjSzQcGNtZqNEvYm2NGNLwdDHl7oBsAo6PWDbH2hltuhOMROhTaBVjAEoIJmfVw+too6KaqpGAXKe6v6ZjMQF7n3ffeocffPDrpCCkYNcgeCdeH8TQRSx6809wfoOf+vBwxgV3hmPcvTnSRqMKfcP07a06cJGxlv0/3b+oiin6OL00xkRtXfbaUOumjRqsStNHgxvwuGMM/X+7l1S2Ulg9t59D5BAhYeKVIWWERKyrh+y7wbPvX+n92H2zKrhCsTgi3vEKa3s1xqLlcf1a9cgS2I2l2ney8N8+o4cR0Z/T5dBHGzLdifT7ZJs5eJ9EEFibU9W8v8P6Qzz98ddZahm4lPXthrxv8g5cWoTx1Tn+/+/g3hexhr6spngVZ+ZoF2KpG1urnLeNjWZjjtUXSafb2gcM9+DYoF3IaBemh26xgU0+lotNHU1VJgS2UpmmaBNoOziicF5XQojjYjbUaZyOtAuI2sUWDy+bi1N0q0utFvKzKw2U5jPqJ+scTClbKe5yFJta7TpK5Nkbr3N/f+8hfo8wAikkmzjr4bd1zvXGHRsq2VebYuPHkue6wdV8diXei89+6b9emvs8MNafIFaXbp3DoCbX3fUSmlYeltW06lCKg6bSN790xKYDk92t2sba1o3V++0jNhVZtFHrRo3J27bxTba3HAcxwo86iabBrk8Qwtgo9jXM2VQt1tqNXQ+lWk8F4plPx0MuPnOQe9rw0PY1OhBo7c59OYxQvAPVatN/+hraSvGRZZZqWOemwOUYT+2XKfnnycVzda/i9NbnrwH3/pc3/htvvDGktD/88MMxJPOdd97hgw8+GM/74IMPvtDbg01JbapUCmc92dSceqJEocVAqn5xcVS8f53dFGM+3nDQ4OCOtsCpVHKxJpspJNLkxJDWKb+N5pNeSlX31nkgszYY0hZN9tC+dlZYFIJYNaB72eTiFcm539VD31a7Ne4RgxsUMcVZ6evePdOUZwQoXDM3YV0XRCI5ZqJEQlNiqUg101dVrfTYF5R/P7yEh0LoWESKaGfA0VMEdWCth/F2gRPWKKNq46ZLADAuRKq+sfw1RRuaAjUGzjROZbPQXtXKifZKW6AiDl4mQhdccbWaisltnWtjawIB1lgIEomaoUY2LVQWqjpfAHareZHT23owIxtc8HNEPLjDaJB1IjSlqs3iW1sjSI8kLFyPIZGizbAzb1/ZBIqvJ5rxPhhDS9wx9W4yNzYxRL+HsEo1vkSzdKNWIFY6FR1xmXkCzUlJ5mia5/57JDeiUW3+vT06+Irjx8rxv+j4I3/kj/DLv/zLAPzyL/8yf/SP/tGXHldV/ut//a88evToSze+naiyaWVrdUgKXTaK0HMWL/V87g24hHZ72Np72/tPbTZial1X6laGAEitnfi5k1GCX7AY99FONgmm+uNhGISuJTdonWJDP5E9JL/sTusnah6hWjWii1GEwHSY3RBa+jJNh1F7Fqyp5Hw+ObBn+nl9gV7mdCbh5ROIk3EKDFdIxBQHQh+Gd3jpotq199xfRTyVv+jQw8pYtVXWuhm+EqxD8v7hYVROupHbw3p7dRc/MdTWQ1kTtbNRU6VQtSDezdYHlSgWiWzF39vdoDEd4XLCcM91xddT7D9iaVh0AdAeNdLvKQasdRVhM7hm8JribbM+0+5LItmxDGXn8wPDSfRWXOjYg5AnZ2WGHcvJKZFyYsqZlPYhsgNo7J6+g6pYBFi93+Grjh/L4/+Fv/AX+M//+T/z6aef8of/8B/mz//5P8+f/bN/ll/4hV/gl37pl/jGN77BP/gH/wCAn/3Zn+U//sf/yB/7Y3+M4/HI3/7bf/tL33erm1l1Cg9ttaGBYshup0+Kh2/7robLMKaHaP1o4EBdI4Vk8sStmYvdNmJMNjzC7XKUNDbnDtj4RVUTu4gOqHWEV3CgSve6NeI31Btygl7U1Js1WkQJ3nrsE33HWZtnnqaJkGxmHM34+V7SJee08+oFarFyTozR8+ZqYKi/b48miBFNPcTnQnnYWYrsGANYuNyHjkinxw3D6LkzQFC2uhHnTA3wsJ558XDPfDiY8Qv7nernPOy0GrjlrUUmaS7QnLilTU14JfQXW1+G4YACMRKKv7f0Ckb/Br5mZHcUZhi8POYgY+sAWDfa7qG1VTQaMSf48BTVZmIlKXJ/Phn7rharTjSTyBoBtvbzGhd1NzwxjDUxVrKfZNdvCNHq9Cm71XPgNLTgJUHHp+hRmhvX4M7FL/hX2CTgx9z4f//v//0vfPyf//N//iOPiQh/7a/9tR/nbVnrSqmNJRTOulmrZbSrEkYjc9/cF+4G/OK9bNUGbTr0ls6OJPvNrbY5trJ5W2RwWm4DdWKMg3Cj7tuFEmJHxHu5h717rJ9Sr51a8miRSvPcf4BNdo2S7DVm8CEa64ZqY0qZbS1EgSlN1K2wykokGaJci5UBPa/tEUwvT8UYbeEgaLThHuLn73Kxvsg9ZBRxAMpWjQ293FOA4ClDr8231mixGMdcG5/c3bKUjcPxyFo20+uXnv/a9jaDshsZo2RZKkCz+2xy5CuKhfExGvvPOmWtgca3CbCP+LYcvuf3fr8xQKGDiat3WY5825+rjtGYqpJ95yim2pslcX19xTwdUBGen+69hIaRkzzHjx4d9MjOIj37CcHKi700rT0/d0Xi1hqlWomQXl4dgnm9KhG8TGdEt1Y7+7IO9V+5MAaW2nx1MP9qpbfqxrmtLKGwidGzYmSnJu7fn46g94vSPcj4wr5oDVuzsVskQUtHt5sz9mzDVIywY2i/EkOi1cayLMbB9xsVUwIP54Mvkn5OAyF3r9GnxGTX9t+RZqB7Zbfw4ptuGA0PdwXLIVPI1LaRJNl3dunoVo0NJ9oIzhKszWr8tVZisnp0pfeDy24QVccCxzdwkHAx/FEGEJakYwjesCQyBpVOh4lzqDxsJ25PD6wo5MBpW0gSP2+O7Ty89KfNWI+0/pnK2jZmByatgiKEpEjCOiILPsfQ3qM127DasQ1xwNKjGKG3xF5EAXYB9szQ++5DYEh8me4BxGr5/yElDpKZJHJ/PpswK52p2T1tGCVVa9gygY2+6bPPv7MhLWoRB566irD66Pfq79W6PHi1NWnZqvisBzXg0gYH2NrSC5YfOipMvZ7/ZcerHaihjaVV2/SRnXnl4aDteK/L7lnhcP4XwLQfftt7jg0jzgyyt3DWZo0ZFpqpN7m4WIRv4tb0pf4Ay8v3XEo8rx5IPozyXccO+hnhizLEMLx+bxAS3wy1WWeX5WfVSoA+WyBaR43Xj6s1CLlabmcbdgGJ4Lz+IYWFiZaIl5mq594iBjZ5XGSNK7JHNa3VXeVGQI1CRz4kilTuyokXD/ekeSLUxlZMA1HF0oS+7nqo31MGv3OM7eh5+um8sIGXaD1dSQkN4mmRxf1dPLONdw9jzSjuuRGmkAbi3Zt1xHs8grhhE7xRaCISyClZOlZWy4mK8nB/Ty2Fw9WRVoX28GL0VQzewIWcj47v5wYz9sGYSu+CMl9hA2G3Wn1D707EfnYKc3/nQThrOi6n9AhjfLYTji4lhr7geLUbX3w4hloXmy1Gr1X6l2r0Uku35lyE+TIAnGERRniJW19DQrsOfOv/VkCj9rZy6rZ/RlPTMdWm1NpIqbfAquMPXkcfoTqj2eUSK+iGBXDGl6PKrQ2PrM0YhTYH0Dx3a7CulXlKhnq7e2y1ujilYxC0nuTu4Z73DqiDcUGc4qrWiQa4V9gVfaUTjQY7zK5to7nasEUSEuBcV+7WE3f1gXB0STEgNMvPa8XTob7le9Jp99wck4t2tl7/NkOztjKGaBiLuNfT+332lmVVa3tVk0GLanPraXs5bRohMOQ0MeWJeZoszYpxNIWNdm+EbS1sy0aKE4fDjITAZy9uOdeNh4eNF8uJoh4pxi7KqvsPjNRmyKaFHaqAPUJtwLIVlrVQtdfwfWibOsms60/uiSs9NbtMO/t66H9ehv5fdrxi6a04Jn0GnGccAmig6iUN0rjL48sM2WiGAOX4mtqXmyGeAaud1i4lpZhHwj3HxQXrG3DQWn3zlFrJOXn7roxoRD387Kg8bsTs732xe6wSOqPMQ1OnVHb5LPWNDXZOgUDdyugTt846GYbPNqyM8lTf/D1UbMkIQUFwMdE2ntcdLcFTGTdaSi9X2cKsYuQbgK0srHXjfjtREjAFSqlksf5zidYG3dRHXXluu4ehDO27oopszYFYyKlHYx05F2vUQqwcqr18uj8pOJc+qhCbErQLedimnjWQkiHiOWdynohhJw91glKKgrbK6WExObI0kSYhpsxpW5DJSqD3ywPnXsG4iBC7IdoNna9ZsXubnAYsyk6TBnAHYnortom7OGoQPP9XoI41iq+fgFVdRiSlvSlpPy6Nwhcdr3TjD5phMgAK8U0ZnCOhNphyF9d0koK6Nr6WXcwQPASUHimxabOQXhTJ6jTXQNBEHyKpqtTi0YCJzBNFKU2AYiUwH3IQQmA5na2ur+pgnhmM3hUFPQIZibXV2gXHAcQWT7XmHWuzEGgNDbsybgxqwxNKpSwbUpt1CroBk2gbQlslTBnB+Put2bjprMZMk1opziYRpyYr0JKp2ErIhIt6tHY2XLRBD43GWVfOLCy68VA3pnQgtmLGWjd7vRigtJVKayvBBURwcG2PLBpbMW07UZssY6QcZQ2wBYvEcq4g3sPuZJa+qaMGwmaU5TkHDpLJXq3poiUhGP8iJXvcb4VjZ94E4+SnF8+fW9SZZ9IUkQz32wMblXMolFa5L2fjJjQl5clSEQ+pO717x2s8DY0QshOB1FSlJNrg0srE1izcJ5nmvgQG0Nmlxmw99Qh4r9VrYzjGi4RqLMCvqea9as29nbb6o495YH/5Dcb36xDOFx8ynu5IuuBEkv29+vVSv5i1Qmh7vtiqCWSuy8rheLAa+unM1fFIrWV4xx7VtpGGyIge+tHxhZeYY8E62aq3y4ZgikLqHYMRZ6M5UDgF2/S1dWAH83gx0bzDsHfmXbLymkdDvXuwarMeg7CfS9eftxRB3VhV+7E+QwbzTry9VNywBAyH8dbFy/Rnv2mXd1hGXR6M2dcn0fb7MrCefi+lazDYc6JHgSlG5pyZcmb2Cb9xRGteF8eu00tG2fNlrTZMFHAD77TqUiyfprFuK5srMr10Tnx+3e6PiUeTBgX0zfny61qPwmRnHeLn+tKT8evL5/YBvaQ8rupL//x1BJ1XrMBTHfnGwuRqGyjFQHNVNbuQe4gvL20sGY/1HF8c3ECsRbb1PNHFM/rkBMsXbZRzVSwUD5BIaGykKVuueJiIIXK6fyClxOl0Inonlzpg08No5YJMoxfknQ4OVpvll1J2AMsXiHeTRTGCSfTqAyhXV1fcP7+lCLTNitcxJqypsLFtK8tmYhWPHz+yLjMRX7h2XVNKtixEvNSXfIKXod+dgdfEur4IiubK/cML1rYQDwnt8w0KlLYa4OgAqvaNJYwqScNpyro3PDUsN6ttpzJXV8GhQUEhwhQTSQKtgFQr7YaGcSOwtOTqMDNPE/M0D0JO9HSr023VeyzEy2G1elebX5uODR2ujmytmjJO2yhaWLaVpW4szUZWqX+3ztkw1rjubbva96AvPq8CJTGehZegzHiXylrVyng4KSxG04ZoBo5aHNidhOzvfGmk+1qWvqL3cxiSYF9yvFqP3ypBK/Ei36k0WqhuLg3kGHIkHgLhciQdkQcGT1va7uWbKBr9Fnd9ZbVyWfeARuM1woa6F2ibILFYWc+VdbMDQj3312oqbM3RwehNOZfDPjr40zxXsQmxyno+W8jnQJ0AjWrosOfcKQY2XzyKsm6rVR9UdoaaCKflTMiJ66srAwarm0zZF1vx3oE0Watxbc3zRl9EQX1DFEJ2+a8ZlocTi24co/PZLjx5qToo0j2vRUPvUKa0ZriK6qg8jHp7j/L8ttSL+5hUmIjEAmxKbJEkiTllo16LtW0fsK7GMRC12aaorqtQtXn1xnL93nPR2YpJvN2ZxlIWI3SFxtoKp7JStbJoMWPUqzt0w94dh47825xw/93SmBSjt003BJMiU7V+jK1WS3cG8cZTRk8fejRqn6vj861syfjMz8dWP+7xSjd+cuuZQmCtzTe2UoPdIPUgMzjZRGTXxxvRbA8GRAatspNyKo0icJgmk0hauvHwBatqxqU1mjdtNPVcVyA0TG5LlZQy27qSc6asq9fsrY+7NUO/L9V8JATjpV/kYdVR++jdX1GCz5j32nvrugEWAseUqG31ziz7sr3jLIYMwPXxaH0IMbBtm6vZunBkrzjEiATL74v25iILN6urzhTdaFqYUibOiaWczXAC21aIyYREUihsmw8LScHaedm7ycy7hcFlqNoI2mvmhs3U1aO5C0OiVUkiTBI5tMzk4qt5ysxhIjRbL8nz91BBN2/c8WguRCuHNm1oNFlyvDYek230abbWb6kGQpbaWLVSg3Lazpy3hZYCTZTCBW7TiVuAiXiEC0/ffa2OzRgCBlAqFqq7tdtKZVlWNlfa6W3ErVrvSCvNB8Z6VCFyYQD6f/fUZQQTnwv1vy7Jf6Ub/xATSxFvdooUVQOiYht1+F6GQxmKo0jrVSzTOgs4KNUtp71/BQqFQnRhBqeuenNprb0l1zn2xTZ9VtiaSWsnMqoW3ooq1fX0Le/0m4y4pFUPKlwatLfwxouav3u+6iqtwV9lwKLuITBKmidiShyvr7j79DNTookJmo3Tztmmzap7cBPDaGjbm8NDLyGKnVdzoC/GQI4J6sZaKoqlXbVsLOrdhUVYH6ykHSfrW7gKVzy0M00LUaLr/Zmhoe2UYW02+isHhwhbI2rvUQ+j/Kke/oZgswev08RBJkIzKC+HiYRVDmKDVjZqKZxL9dSqL3m7/rYuAi3ClG3mQYiRPGUfNW0AaKhiqUgQNm28ON2xtIIkE1RppdnkXC5Kth4VdjKOhdp9I8rAQaC5IIhzIyz5oPnGr03ZsMEt0lNR1aEYtQtqXPYe+Llgy/yC3Ednf/4IDPAVxyve+BO0xS5MCIg2mm50NtLQuGvqvRy7EejAkoLRY0Nv9fT6afNKQYBKpVQhazQEWtWSR9S83DTbTRsey3qig0IrhRgT61o4Hg7DQ9e1ADrksEDHohdAhvKqc8kdnNLSKLUYEt/aQJyrEzlSzqzrSgtieIJaJ+Tzjz8mp8lIQK7s2pox/MTDwq5S1MflXDah9I6t6J6gLCsPpxPLthJj4HA1k3KwfLsok8y8/fgd3riuxJTc41kuzVOTRVPMu5rOgM1DsOtn3YmW3xsltRu+po0HvRsAV2cdXh+vOORMwqio0a9ZJELDxmWXAs3ToxRH6hA8BNQgSLDBouIruxu+rbhop7iufs6IJk7rmduHe1YtkASisNVdsamvqtjLY55uapML5P0CZdPdsHZPLs1wl1LVW3BNpwAZ9hkcb4nRuBu7T+95Ywf/ZP/1ohR9GfJf4IlferzSjT+nCdHJAJ6qVqZxsExQKzUFxSTzdPf+AkH23EstHEB84QUHyZqroSNeQ6VParXwNqTAVgrimn1BIrSK+MCDVqychzSO08y2rkOsss+ZDzG6fl8dTRjD43sAljDv0rvsgg+DrKU4g1DM6LXGVttecVBH8V1yS1FKLSRXFK6l0oJ7wxjdQxmIVso2yERWtgs9Gh1h5NV85Dgf6MSottaRSpVlBSBJQLfqE3etQVp7PUlAqS4tJubxtRopqYOwjlgPfX+Bm+ORFLMbAoUGx8MR7WBXnkCDc/cL27KaCOucCZKABhLH63dxUPPWtgbaiCqsYWkXrFCUc914cX/H/flEjeql1WoTiWX0Ro17ZvdIR/Wmh/mdJox/N1ubnaVpHJCkJqldnOFY1WY4dnNhis/tJTd9We16aROP/FbHP4iXi6HHBOOJX3q80o0fiaaOK5W1tHGBfR/3Kz+Ub/r8ePHymFQxiWJ22uKIhdwujNLgSxdVRq9217NrrfkADYvkbTpO8I47WJeFnPNYRMWBuVLL6JDDzz/EQEQopdNsDexpXj5KLuTZ2z0FQ3pTSq5Vt4s+dH214/HIdj5TG+QpkTAU22r0RjAKIjy/e8GyroScjLQS3UvXPoLKWYZNqa345oXODoPdg6iqcSS8fNdfb4Yp0lqhqY/l9nBfVFzIZL/WYDl8cO/cwLT2PQRWVc4PJ/scB9+Cd03mabLOOHoEVQfAK+Ldhm5cuuxZQ5lCJKfspdo2HEAphYf7B27bwlo22/BqZcwuY65dYEO63qA4FbhvdOjVA/udkf7Z9TUOQQi2BsCimx7hdJJTTGGkKma3+s3YV9Me018c41xe+s8XPufLjlc+O6+0gGm3VENDg826Vw1kxZsfqpWA3KWJGKWTkVPZxt86qBYUqV3MEcgByYFWFClKIrBKRFsBLCwtmAc+pIPdJGxTxSQcj1cs6+p0TfdeKRDF5rsHb4oJKRoZqCiFBYnQglo4HZLp68XAPCUezmdCSHRav0ShsTlwoei5jSmxZSvcXF3x8f09USKlmUqPiGnytaZsy8J0PPDo5pq0ZaTtC3AXcehYk9qgCL1Ehh288gkSpZfvVF8yauovqp2lop5bdzL0yIv9jfvCDJ1YJTsDEZxP0DsV44VXdl463mvv3yHGSIjZ+Q/eWedpWifugLAGE2jp0mmUyvm8sLTCw7awREUmEwBtwdmS1SK1KoKKRZ/i/HrFowu8CuHjfC+17qFXb7w9W4VIoomtyUUbLQqigSCVFI1ZiUJpPRoFq2b5dXP6+sjvYdf7HyHBxTnsYcRX7rxXuvG3unKuZ2pSalQH7wTjKQsabZE2zEoOpNp/b4hHBjoELSLsSLruhi9NiUkyemqUdSMUW/KNTg22qS2pNbLXim0QQuPhdDJPnRNbKVAZdXwVZalG+IiiPgDS24sxQQzJkbVUolplYltWtrIR08xp2SitkFKE1kghcnW44ng4WAkvJrQWQnCA0tmGa904zLNNTJE+m61wuDaUf1lX83ZqSkAvBYEOOHWevT8KdJjq4tA9Q+gLsI3I0j2SOgzVvbuXU+mvu4i4ughFcDBULyKJIEIXxEjYGhi8dGxz9pBYooOC0Uq9pcXkSq0AACAASURBVBSCKLpt5nlTYCtnjocDApy3led3t2yqMEViTJyW84iu8HQsItQeNUqPYvZIqPdp9JZjC0SspNGv2+zDWim7161NnSxk6ZAM4dQ9bfjcZd/viltnfelf9qMz+y5//x1N2b0vJ87NpodoNHpma0qrQmwBjXtzguHwngy4dwDXhlND/HurZFeL6UavtcYmhcM0EyWbdV/s3jaijTNKka1W1lLI08xWiqHm7ql6y21PCSTEDiRbPdbD9yZKmjOn9cyynDkvZ25v700x1skt2grXVwckTTy/ezCcwcPWTCBLIrTI40ePOB5nbq6ueL7eWQddygRAN1NmnVIg58wUZ4racIb5MBv2UCu1bOaVwkWuKh1/8KV0kTeO5eu19z4O+xJqEoxMA718etFHYTfgpRxVhwWWffMMxVoZYXIKMiogTcRkyrsxgaGONIzJRbk0pUyMgVIK2gxEm+cDp2VFgOubR7z2xpvcPtzz2599wouH02Dq9bRBq0l/EXfvagaSYQBf3ngXaL4bwRSFeZrMgLhOgiqmKlQbijXjRDcuOxHn4l1fzksvrvzFDfiC49IAfN3xtRv/r/yVv8Kv/dqv8cYbb/Bv/+2/BeDv/t2/y6/+6q+Sc+a9997j7/ydv8Pjx4/5/ve/z5/4E3+Cn/qpnwLg9/7e38vf/Jt/80vfe6kba6gvlXZEgoX3BZhNFbW3vdtNahCMfy09TsKJIr4Ae97kKSoiwtY2ziw8PT6BJrTlwUGhOGitIZjVL1ifurgaSnWZq+kwcX11g4TA3f2d8aVz4nh9JIbItq58//3fMmWhWJhmI8uEQ0KLod5aV5Ik0px4cTpxLoUQzYPROkgkHIJp723LSrx5RMiJrTUSSnZgrNZC1UZsJsWdY6Jooywb+TCTQ2B1d91FN7q7Hkqyvla6yPS+iHwBDpdzEWbu7n8Ykx7Wd7mp3YTsv1kLMkO6bAw4UWtfrW68U0pmlJsNIg3O+e9YjaJIrxL453fhkZiTlxIntlosz2/KumycTmcqyptvvMlr0c7js+fPeXG6RyT0YpIJt+55il8QN1C6N3/1XL9fCAG/D/6v/YKJa0RY+GAgtKh1Be7tHnu6fpGCjX3+FRu+G56vJrK/fHztxv/5n/95/vSf/tP85b/8l8djf+AP/AH+4l/8i6SU+Ht/7+/xi7/4i/ylv/SXAHjvvff4N//m3/xYH76iqAQkZFLIPiCx2ujkaqKCKWXQQq0u0oEQRUde1w1i8LysH2OEVF+kEc7lxLkcDFEOwYUeK9HHFInY605l5ZBnG2sl1tAzTZnT+QwudGEhdSTMk4XhEojAo0dX3C0nWlBvxmEAQM1n4SWfWrOsGzH19eS0UJERuoe6MecrTuczh+MVcZ45bxtznmyue+m9/A1TqjKCS0PZzgsxBI5p4tNPP0XoKjC9hfdyGu7uVfu6GQsbhlTVZfg+vOHYjPtz9SI0dbNM1x3oKkBGyLRzSQ6Ygl2HWqv3YdljsX/quD59Sg4j5F5rtZpNDEgUMpEUbdCKhkaSQC3BOg+KT69phSfXj5jyxCeffYJUJeY4Iov+hVvTIcBqqj97ONNr951jlaPpPNDM2AYJlNZFTMMuC1ibNaR5i6I2GcajHwqDors/sNua/bLs0dv+8I+mBJfH13H5v3CYxh/8g3+Q5PLRP/3TP/2Squ7/l+NhqTycGttJ2U5KqIk5zNTNxhKtywYN5mkiBSuzTjGQo0AzFRqazbWPMbogYdzRUcURWYDG1jbuz/cg0KK3ZmKQSu/nbjSWtkKEpWw8f3HLi7tbPvjwfUrdyNkktlM2kG3dVn7r/R/wcH9PjpE5J3IKpNFxpRAMwbX5dUpKkdN5pRaQZucX1AaGSgxoFGp0aepWbdhCjDz7xjtoCDwsZ0pr5MlaTnuffieXJBeUVB8+cnW8GtNYemvo0CrAN2JTny5sm36AKmp/2jWy87SuPAxwgyEBFp3OPP7080iOT+SYBptNFKPZNnziUaUVm0NX/PdafEa1e9oRxrmRbHjeTwcJ8Dp5pdSN2qq3KTubUuwa5xCYQ+KQElIaN9OBn3z3W7z77G3mmKE0YhNa6Xp63lTjaP/lprqMk0IQE/Po1SBPQU2uSwctvbVmZJ3axtfradD+7jvduV1+pl6iNf0hHUap9w50decvO/63c/x/9a/+FX/8j//x8ffvf//7fOc73+Hm5oZf+IVf4Pf9vt/3pa9VsfnjD7crsW2gpm2XpkBKkenqmiCBKSiJyOl0NiRehMkbTyza19GiOCYy9YjAS3vGl7exW6dycu+xL9osyhaVqomlrrx4eGG6eGpB5uNHj3nrrWesrUAUzsuJSqOuwul85tc/+u9847U3IKu12EqXSlYPtU35JkdTf3lxWuzcq+X+sX8XTK66eahwJWL8em1cPbrh9WdvcvfRx4YLAHMyAk/xBp7kMtLBhR1aaxyvjqjgfIPmog9x31DwkhtRcHVmuXjsIta8yAD2dKCn6hYp9E6IwSaRPUePTr7pEU4/p9hpvo7m9+iglBVxwyI9OkhcGDKhbqZapBQM7ce5+l6FGQGgN085X0KboutGKXA9HTi+MXG/nvj4k09IMe4Ji5N1+lUaGc/FYbr5PriltrEZS+2NUGO2EW5e2YeXXkZjL7/vZZYv4158xaFf/5z/rY3/j//xPybGyJ/8k38SsIk6v/qrv8prr73Gf/tv/40/9+f+HP/u3/07bm5uvvD11cttUaKXTSy8X5bCeSv8rnff5a1nb1koU61s8/yzz/joo484n8+j1l/8Ijd2Qse4Mxd3yjq0KqdyJseJIH20VCBFCNWVb4MtiKqNLBbGv/H666zbamjxtjEfjzQa8TDz7K23uf/kM77///w6r7/9FK2VqgV10oa2YKoWTUmTebxtg0POplRLcGEQQ6etlGT00Ouba5oq8+HIViuH45H5jTd4uL1nWVfrvffFVos1FvWe/RCNS39aFw5XRx5OJ7uOQbx64mAf1urax5Z3BR4Zl04GANgXX2i7d9LPbYOe9xtw5SYjOKFKTUuu4w0dVY++iWut5JSweou9rrev2rkBrVHE2IPB25tJu5wZYLqETmFu1diXzt2ypiVA1K5dyhPnbXG0vvHo+gZtyme3tyOd6ICl9O/7+X01wnQd7zPATF+olqZ0IpUM49ncSe3RhI7Psvf9nIfvdXyvznxRWv9/TIHnX//rf82v/dqv8c/+2T8bHzJNE9M0AfC7f/fv5r333uM3fuM3+D2/5/d84XvktitoaAgo/cY1Asr3/q/f4Df/799kmiauro4c5pmcAo+urnj66AZa5bPPPmVVZauwFOvmCyFRgCYbTWwBzWIDL0hiwhJFPewTojaCKClCbiAh0bbKhlKiMeXe//S3efett1EJpPmApkRGCC0iSyVUIecDwkRdT5wIpCkyTRFdV+qykQRSzCzLisTA0jbCBCUUUhLKpiZe0YQ5Zt5+5y3Uh2Y2erkrUtLE1U1kXc68eHHL45trG8SIDeCYJ9Pn70q8mcB2XriaD5zX1Qg+nnqEYFOKak/Mh37+3ltwmftrB6sSw0D0vLPvhabqqH8XwNDRSGPKtm1EAU2N8dhapS3FvGytaNyHeooEL0uGoYuYu9JQtSqJnaPSPAKI2XPtYpNxmmKdMwKhNQc3LbUrbTMVoRCZEITM61eZclLu1gdSFh7qAxpslh/Vvbd/z97/EbK1dDc20lZZW2bVyEajhNWIXWGmlYBosvJeG+ZiRKAKOw9ij/DpAeRll+QoBXaij2Ckr/8TG/8//af/xD/5J/+Ef/Ev/gXH43E8/sknn/DkyRNijPzP//k/+d73vse3v/3tL38jTxi7TPH4gmq9+dH/fl5X7s8nnjx6xKObKw5z5vb2jhyUwzSTs41WPm+F81LYyopoISe7WMnSbJBIFW+Q0UYplS0Ivb0mBHE9gEhtGDfcL+7pfPLJJ8a1zykQ8uShXOX2xS3vfONtPrt7buFraaQ4M6fMed1oDeaUAOHufjPAAk83AC3K1TxTt0aeJ549eZMpZ8pmk31xz5OnyXLPqsyHA6A8v33OG0+fEpOgpbIsC8JkOXyyOey1GJHoeDgQRHhxf8d8OLDZnDFGWD4iJN2758QiBPY7dHG/jLfeiTMmM6VjcXb0XkJPuSwn77qEQUwXATz68DZn85Q2B6+ncSZJttNzd6ad7ufjUUFMyZWVx6CugVc0ZTTgmKpRZaCRwfj/rTo91ys9VXXMGeiKUEOHQNXwjBiJWMt2Vx1qLmgiEVNAdpDTZ7d5ZYnR5DUSkouIYi/vyRd7cun0aDuX1lvXv+L42o3/RcM0vvvd77KuK3/mz/wZYC/b/Zf/8l/4h//wH44pNH/jb/wNnj59+qXvXaSRDvD40ZWRdqqyrQbwtNYIcRo8+EbgkxcvuDs9oNVUZhOVRzdHs9bauM7CzZQQElUPLNvZxjs1kAJbVZbzRi3G5Y5zogTh3CraIGplDgHdNqY4UbSx1EZIdlH/+6//d9589oyYM4er45i0Wkvl8WtPqcuCoqScePboTVIK3N3fsjysTCEgIfHZ7Z01D2Haaql3EKhQHgpvvPYmT5+8Zkh9iJCscaeFzhizEmJvXDk+fsTNk8fcfvoZddu4OV4hrRJKs0nCqRKvj2gQpjixLgsB4fUnT1nXla21DkNcriQfSsZA1pG9fDWWYWfjAeqYgnrPefEN2jXmalVTE9JKnDJts4w35TzoqhZ5dLpsxwTUuBRehuyh8JhM3MNeb5Q6zgczGl42M4KPvU3/KuqdfVrc4GzqgywEiZG1Fk7rwmcvbiELVZp1+00JUaGUjdT8fjg/IiNMaqSdtRRUI0UqG4WQlTBZnlHr2b9DfqlSMIqebnylx/n+0OhTYX+ci78pboiaTTjK81dv7a/d+F80TONP/ak/9YXP/bmf+zl+7ud+7uvechwxCikH1JtpmloTCmo2evNNb3mft9V6LlSaefnb+xNzThwP2fnwm4VxIsx5YhYT99iqT69x6Waa67N7KNvLUikkUo7Uzdsj6V7EtOxatW61j377Q3Keqaoc5gPr6Qw+zGKrhfP9A++8+za1rJzv7kl5sgijgrpHuXl0xXY+ex8CPH3ylCePngx9dpuoks1rxQCD6+2bKkWah+ZP3nyT+9tb7s+mEPQkH6BglNR1JeVs1zFPlHVlWRYOhwMigbsXLxxYU19kDaG7tV1lp0cGQXAG274U1VVtCp57Ovmi+iqWICNXDzAai4BB0+3tw310Wff8Fo2oS3Z19FouKLo9pzX6cs+LXyYf7TtfotXs0xRMX1+bFzBsmGXOibty76lEo2JcCxNrMYUoVvVUxVLGqI57FPspCGstaFBC6uxDoWh9CVuxE9qB0XbB5dhLnFwYid2A7d/b74H26U/h87bhR46vLef9nzw6ot633wA7XFm1jzhWdlZe77xRLFzbqvXOb7Xt9VKRASqB17ddiDHHrhKjPoG10kpxpNr63VMvrrNfZFuc6s05O1nIGmUKz29veXg4sW0bAOuy8uL5LU8fPxn5ZCnWixBi5HicKaWS0jToqzdXN66h11xAYpca035D+7eSfaOoG4H56khI5rGqt4e+JFXmohvmZa27b8qZaZpM58+7C1OKY85cFPEfi0xE1QBCZc9x1Ta+tjbKgM3ViLQ2Kyv6yK9WTYii1TZe09VqumzZZcmsd7p10VNzEPtz+obZZc76/aoMrqIbnn4zx+zAnip4eG9TfK389uTR47E2UzRANkXT9Gt1lya/CMJd+ZfRUFZb7QwyHJOkVh0t5uP12vN8P8Yvdu5jWpC8ZC54eXfv0c+PwoE/erxizT1Tvg0S2dZiF6UqNKdthv3m9mEVKWWz4i48udVCXRtrWchBDLDLxr4K1efTB28vDRDmxBIKrZgEUpZAowNqiSlmkkKJxdIwqQ6sWEPI6fSAinL9+DH2hoFlWTwEbtzfnTleH3n9yVNun9/y9rNnTCnbrDtVG4QZgtfC1cZmtcZPfPPb3FzfsJVKnick9/ZSwyRKKUOmuYfbhpQbCepcCyFFrp484eHuBZ/dvuD6+oqYTUugqA3drGpzAqZpMixAhCdPHvPZp585G1FHnn4pFBm8jNbLTy/Jkl/mCh1kajIIP72kZ0C3EPHhEFvwtmcz8hLM0BiPwIRKOtXY9rAZ2iTJXEVrVK1G8Q775rBILdI77Pr3sM1mJ9UBRWKgNnEClgyjFqPwk9/6Nr/xg//BeS0cH2XaVqz5yWwoPu/EHAB23qVa9La0woaSve++VljPG3UzPECoLyHyWl6e06DFGaudazHCfot+7JrsDMiuUNUp2V+3819td56XlLRZLbvVRqs6qKutM/TFANnQuduCldyIXp83SalWrEPvvK6EaEM6UgxMLrsUg5BzJKdAq4HzsnJeK2UrTHHimA+uRS/M08x6frDTVCu5JLFSnp7g5vFje7xWPv30Mwe0bDDiw/09nM6sZbGee+fBxZjQGK1bTI1PfpUn3njtdXKeWLfiPQEmpd319tXD314aUg/JJbpeu1EaqduGivLaszf58MVv8vzuBddX1+al1kLBtAC2okw5czweOZ/ObGUzkpbA/d094Ki8R9kIpj0IA+E3ohTDOHWv1tOCzpwc4lVdKcfBLautWy+/9u8SrKW3ipX0NDRrbQYTCPV10Ut4tlHCyIf7BjAh0X3yMT0qCHuVQRTUjUXVMNbdmGmocH048q133uXufMf9dsc0Taynxejk/ejXAMMMarO24aZCSOaAKlC2xrapyUKJIp0W7hv95SEtjvJfRiv7x9EFZuVy47MD5AC/oyfphCioZOqayFtjJpNj5VY31ugr3jd+TBBig9A8nPaJtxotUhCr01ax9t7YYG0YC2trJIHreSZLY4qRgvBonpl1ZdWNGJWrLD7PrHE4HK3+rXAuGy1CERAaW1lZ7l+QpqMBbRSmPBEV1nPlMB94sdyRYuKjjz/jvNWhd5e0gVZiDSQS33rnW8yzgZgSAlUgzzNrKWOxajMxySiBrVWrfVtOgwkHNxtTHW2xV1Vee+tNPv7oY+5PDzy+uTEa77oyZRNA2VZrJz5cX3N3f8fZN39E+Oz2U0KYbIMqY9rQvrGNfvzyIWPAhyDE4FyC/poKoxZv9EBf9D5hqNfqnapbigF40TUKCEZOqs3TQMvNPD3po8f8TJqlctrqAPeMyNVbaRViIIfMum3EkNFWEXHEvyrzIbNsC4c8cX31Frd3B27vniPFwvkQI6jxAbIXBFbHpJIIGpVpmuzcqjERo9/PWhrBh5gO6TC1ph67q+Idvt6WVu37No9+J7k0ZM7RD+M20FPRrzpescqujxuWxiFkDnnmakqUuxfcl5XmjTgSLDzKOTsgp9RagK7x1kEQHU0kbV9y1pcv0B4W5iisMZKS5fOHo41Wak7RlBCYYub68TV3ywOnh7s9b9RmAqFNuXtxxxtv3Qyy0LqtzDGb6GUr5JypTfno44+QECgOCjaX52qt8ZPvvUdK0UUazAtEn9iTUtpLU05Mqa7vjue93VvEKGxaB1W2uZzV66+/xge/9QO2bSPmDGLfcWsrccpWR46B49UVy/nEeWk8fvKEECN3d/dUVZJjDm3IodkF3oXO7OiRQMcTBsIiPc/mIoJwA6DWdSm6y2bbsMuAzae2FCdPpoYUgqC1Mk0za1mNQz8JpZWhpY97y34+HSfpFYi+Tvy0iB31R0zl2ElcZV04HmZiMQzn8dU1V8cDH/zwfV579oxPfvtTVJJzIQwsrOoMY8Gm94SMNtPZi2rUZSGgycp8ASFhOpDbtlnPSHP07qWE335a/029uuGKRSqYuEtXpGr77MIvO165yq5WC5NyjmQiU5y4DhOKclZXHFXFRkgGcKWU1gpBA6Yr6bPg0d4ebvPIXHzC6q/KsjUrK9XCoUJOG1PynvY+sKEp18cDN4+veXG64369h81uaIp+Q7z0uJYNSYkQE00rRU1OpBQbQtEHbxZX17E838L3N197k9nlvFLKpBRJ08RpOTPNs2mudzBHupftxBcZaVIPNf2XjiVRWiXPM3nKlFpoYA1PGK6i1Wi927a6GOVE2Tbuzicev/aUrVbu7+6sHErzUN3/5xsX2EOB/qd/fv/3vd7f0XXxKkUcpkPVu+1ca4DQvZ5JiHUte+sPaBCTIejep9FD966fb4a/zyG0Bp1y0Tos3glolQvDB1QEqV5X9/r/cnogTZl5PnD/cE9ojW89+wbf/63f4irOXF1fuWR6tRQB669QwdSHg9CkcZUqbYZANAxLxb+j/yhorg6MmqeuHQ2EwQIcaZdHU2Zj/Zy7crKbh31K85ftvVd4HMhMKZC8PLMsG9tqFn9KmRwijQrBcr7DfLCutSawtR3RDY5wq/fzN8vf+lCOKI7DTcGQdQUpUBoGCsZEDpkcrUuvtpW6nLmZJ25DpEmfQ44JRaiwlso8HzhcX/GDH/7QdPVdwdXuWRu16RgjZbNQnqo8ffKEd956i9vnt9xc3wA+TaZs1vzUQznP8fp0HYUhzilO07W/iFcOeu2dwVJTEWeI2b/ZGGtlWxdiSqScqEDOiXk+sK4rv/3JJzx+8pj5cOD9D3/Ao8ONCXsMJFv3gRT9FLqH0r01dG832Q+lUYsirkE42p+HnmKfWeCdeyHQts3CWY+E6nklZF/YpUI1ea2YwHQaXKQjBUuDxPoUQtdJlDAwh654LE7QCTmh2OgyCULZVpbTiZubR9zd35FC5tvf+DbbaeXhfLYopWCy5RigWVGiKFULzRmQ0fMdceHYLvlmlQ3HJLTvbVee7qmuNzYNcU/VwZVpdDygX2gnSwXhq4L9Vyu2iQ2P6Hx5MEq7IoQozJoISZgPiTRn0hQprXGTC5/Uz3hYNps66x5dLM6z2WhqrV8dHCNACDZrXothA2uxUGkLhRQrxwNIWynnhfqDOw7XVxyjsf0Wl/ZePR2Yjwd+6/33WV1ltvn4aUS84SSzrZtNBfL567U2jvOB9775bZaHM1dXV/S69DRltmqRwVq2nc8OL9XuB0DVvRd247v+XsNm2SP4XL7eJeYaX2kynUM1ZtxWjOW2rpsZgilzCIGT1/mfvfk273/0PoFACom1bRzy7E1Ce0jaF143BsGHoIz2aP+HHqb2gSQAunmpLMZRPdAxNkoHsm0jwr1XIs6UUhwhdxp+2dxomiesbly6eEoXElV0aAi2ZkrHIVolA1Wab8paLS/P88Td/QtynmlNmaaZIImlOd5UYZJIn6hkUaer9MrlNbGApTUl9AGpzTgiXVkJEddCDMNx4DoF/X2M9BWMjeo9JdHboix6UlQ6E/CLj1fr8eeJHDN5ymNOtnYxjNY4FuNcT1NGsXntOSotZPSqsZXPaLrZBJhgmmXRa+ah8yoFcrRUQhBKMXZW5kilspWz1dg3Za0nUoTDnImnzaSRq5JjYmsba1VwvvjDtln5J1jYZiGqeejaKq3ic+9s4WtTHl1d8963vs35/oE5Zc9fTTu/C0XWWkevefPX9bJUP4zX7Z4f81pdxTeKWDjpqrxbNVVXsPA/pkjdii1itQ1YS2GaZ6s9VwMnz61x/3DP48ePeUbjo48+Gl5tLdvogLz8Ay6Nr16cLSMF2x95OSIotSJVhhx2iokpTr6oTZs/hWSrIGJCmR4lpGi89yBCKzZIVYOYHqJXH7q+QA+Pa20DF9BqAXJwJWJJyQ2ZbWwDKw2ki8mwkU0bj54+4eOPPjaqd60en2NqwV08qHl2rubUxL2yVo/qPFTqRqnvAcAHmqhHcjqqAKsoIhYZSxJUg4/d6u8plPjVm/vVovrBlWBTIuRoGycGJrWSVaLnbA2RTNBKKY0YhDkfjL4qFl61Yh43NAvXYzPTLyix4uOQldQCSTI3xxtUG6clcVrvLS3wFOt02giSWLcNSZGu2R+xZqLhZx1oNLKYBbidXCIeqgrW+XY8XPGtd95FSyOHZN4qJssR1ax2r8ubd+uTgfvN7GUrz3/DJQDkjzsoVGoZks212jiyJj6Ms6xczUfWbSO5Wo3hEIXQbM7etq2GSAfh+Ytbjscjz956xkcff0QgsNYVEcMLdpyhR5raE/cvuOM/uuE//++lmXRWKMUqGCJM88xWC1c5DipxXc/cHK9N0qpU16N3rYAQbePXOtKzrRmWkbzfAmFo58VgjUGtb8rmXAA3KF0hOeXIthkmEz1leu31pzz/6BO2Wji4sGlKEXV58w4wNk9D1WJ2rAQY3LB7pAiuhuw4ysVwUb/xtsbcoSl7mrCvE6A1mv4O9vin9Tym0aaWfc5ZJAI5BLbUe9RtW929OFFL5erqmhgT0zyhGHOviaCbobI2DCM6C95HcG2KqHCIM0kmQikQhMdXV8xTYi0rdw93ngoLt1IIsdqoqhC9q3bfzAPkkuDuTId3HiUklxETAu+89ZYpC5VqQx4PE6UWYwJ2S689g2bwCfp4x72+66FjCJ4vXkiO9wzc6/2llEER7eOa7s4PKPD4+Ih1XZ2laAvbghZxzTusIQjlvFrY/xM/8RP85m/+D1JIFO0cc8ZJ7R7988elgGeHCC8Pz2VHVGOsxdO22DDMeeK1N14nTXnIogcaL57fcki2BrouQ6+EKDJKkVpthJa1WltDTicGKeY0LD1w3Kgr69ZGW001KYggydImU1Y2ZSYR5elrj/n4w99mU7uXpTWmoBffTojJh4k2S8e09RxcbGxIl5xzjsOqdadQa8/hDVsJW5eYM0n1IWOmxdKLIOSQfufm+M3FIEspTCdj5M05Ga12mojXGfXmidNy4vmLW+OXzxGacN2OFtaWlVaMggpKlEgUGyQBXVEGm6Hu1YFo/aGEJsyHK9L0lA8l8Pz+hZV+5pkqYkKpTWnEPU/181ctHsoPvNoktzebcKPNzuUn3/sJppytrJMsF1tqGeKbyN4L34coGiFDPB+18o94Pmu4ajhkXgAAIABJREFUiMlHi2sH0HQXcBQ17rg9mRWfPqOCEnh+PiMp8eT6mvPdAxEhJ5vFt60reZqpq40MyymRYuT27pYpT3z7m9/i/Q/ep6zbAM7GwuxXQQ3gGiiET/bpVynoLuvVS1Q4ONkr09VxizhPbDT+xw/f5/HrT7l58gREuI6R+fpIeVi4ygdjvnnvvkTQ4lWMrhtQGGh7w0ZohSBWGSrbKI/Wjv4rpDghk2EL2hrrUoZRX9tC8nTt6uqadDyyPDyQQzSil9axUkR8nJZ/4yji8uZO2sG8d9FKc3wCgSIG+vpkR2LD0sueUvmMBjPcrjSEEjQwlfyVe+8V1/Ed1VR42DYEWBbLp26urzgcDPGWGNmW1brPXnuNSQKbKHOeuS8PTGkyoMrbfJNXmXNIo2vLALBkck9u8RWTaipUVm3MhwM3AasRB1sEl2W0jqQPYU91b2sr17yeqstQWVj4jW9+0zjoPjCzvz5GW+LN+e2dS95qI8RAyraobOafDMpyJ8nYR4qHjd21WTrTqrI0a7ddKEOsNCT8PSJFN07bmaubI8u9SXlFMTJMqab6u7GRsHkB18cbSt04nRe++e43+Y3vf98l0nzZDpBvp8Xuir0DcUPpLVk9j917CczcsWvaiaU0y2a97GXbuP3sU95++x3Op3se3Vzz6b11YMZuZkKwbSE2B3AIuTYrSQ7ROwd+q5pOXimb90yYKGZOEWkWBZRiWpCdD9AGMciilNPDiePhwPNPP2Gj1zTcn0uPZsIoc47UDejSXvRIxQ2pejdm6RHc2O4yojtFqU2GccOvXaeX78b4R49Xy9WvphNXJYzwz0QdIy05SKaVZPiZyUzVRjkvpJSIKrTFCB6LdeuQJEGxjrnO9FKtnlPZAjS9NrvQkgMhRU7bwmld2Fqxi+5NFjv5w5H74avERxb35gkGj14waa6f+vZPWjrSmklvu3XfpbqrGxQZoX6Itqm3bRudVg3nyneChipIX6SVBAYSjVAk0JJlIddPj0wHMQzDF4xIYGqBdV2IGrh5+oT72we2Wm2uXMimYCMMRlxMiWPO3N3fc39a+NY3v8n7H3zAi/O9l8T2vH3355c324JaEKqNTBnfZZgBj72tqy6ABMtnQ2ArKzfRlIzL+cxaVjRco8EMiankNhtJJkJQm14cXXCzd+/h96g1z8FVWao1EE2zNSuJCFuzqkWIQiax+Pw/3Sxfn1KyayPmbadpJqeJVmxGQinWrDXUoFozxmK/Fj5pOYdojWEOaKoY0Pnp6QUV5wX4Ne0g49Tp29VLhVOv0qgLqmLY1Fccr7xJR7VRWhkjiY3bEKjnwEG9xFRNISUlm1zTZ9FHCWwPK+W8WXOFD6pMEjnmgzvhYt1rfrHsEJay2Rw6mTifHziX1abD+IVT3ae+Ig5i9RALBuoO3WPZ79KMWvruu+9yfX3N6iO1VbngqHeRBw/+eqgsDD4CQZj6aK6Oc3RL34wIJOoeTJw3oMHUWptS4sbp4UScAyUsFC2IE8NscSRizjxsK7qeefr6a9zd3rIuZ5pWW8ReokwIta2A8OjRY+7vH7i/v+etZ8+oP6yc1sXWNgY8KTt6bVfLaLwjApBL7M+Rde/xlW6QXZ02iVURVJXnn37KG0+fcr59AXPg9vlz7u/uKWHl8fVjC+17tUNMh7CDrNHJV8P4YtLl4iAfqtTNaNIhWrQQQ3Sijw0TXZYVbcphnr1pKo6+CYDr6yvOp5MpH3lVxf6vQxy0f39bY2pDVoKxUmOwdHKeZp6f7uk0Xb/7VsmRzj8IBKc3B6dJI3V0gnYs6suO/yVd/X/0j/4R//Jf/ktef/11wMQ6fvZnfxaAX/zFX+SXfumXCCHwV//qX+UP/aE/9KXvrdEWg4XT6uQHqzmf71+QHu4d8EtczQekWFiccraFVipSQJ3+KBpsKIW3UVruC7tmunnWpawWbkbhYT2z1GJjt5z51Wf1DWWXnsYLY+PjEFOvL8cQR0787M1nzDnTijG6RnjXSTX+Dt0T9Z8hr+0rV14q78kg6BiQ07wxKCJeLkJdcyAIMQnn5Z6QhKoFx/C8lqwUAQIsZWXK17w4n3j05AnreeKjjz7kybXNroshUraNEI03f393z83/y9y79EqSZWWbz76YmbufS0RkZERGZlZmXRA9QiAk+AMwQBQgMWHADMSQCVMmSN+sRgjxH/gBJVEgBIgZA6BbLaBbNI1EQ2VlZlwy4tzc3cz2ZX2DtbaZR97qU9Eoy1KRccKPu7m72d7r8q53veviksPhhmmceP/d9/j+hz8gSeEwT2SB7ban5KSNL868vKHXjYDkbRE7UV2E6DqqaENTcE77+pdIq7LdbpCUmI8jvh/ou4GPP/yAwXXc5SPT3ZE37r/BZrvTjRFXReFoxlYxGM9+v2eeRg7HI6CzCBtNOoTAsOlMu1DZl00hSGo1dSOIogpILWLJOTNsNhz2e62eWCrZFIeaU2vGINhcg2L3UpmFVjGSyr3zC272d4uzcijTdUlpYHEcuazOUDwKIv9XQ/3P09UH+K3f+i1+53d+57XH/u3f/o3vfe97fO973+Pp06f89m//Nn/xF3+xCC585jDvpvLYvRJkrIa/DEB0jiln5umGThxzjDqOyurRKk9tJA7RoRIxxJMmBbWUggIlc5kptejM+cOBY5pscIYBbKUuaien3U4L3bM9IOvP3tR4PZ7L3TlD6Bj6YUW99Wy0Grc4Wdpa20nqSZDsvHINRLBN3zTksQ1uZSunEtqIwxMpNZPmER8C03xAJ9eiQhBWAxJxpFSYp6QtwTMc9895580n7KcjF2dbHoe3uP7kahkS6h3klMgp0W827O9udUyUC9zd3PLk0Vu8urkybr9emM3ZhpQyJVdysbnFDrIU+vA6gh59Z9fcsmOBLFlHZJfK+++8Sy2Zly8+4ebuwBwm/A2c91t8Be97Si5cX11xOBzZne24uHcPKKSkHP8YIsfjgZvrGwWVS6b19M/zZP0QGiXUmo2ko3hIO0pRSezj4UBnTVWKq2oZNDgdsd0wmdPqhdCiRqskWGttlsI4ayRRYyXGjoqjD5GzzYZxnND5OygojVuuU+vAaxwSH6xX4/NSrU8dP3Tj//zP/zwffPDBDz0RwF//9V/zK7/yK/R9z3vvvcfXv/51/vEf/5Gf/dmf/dznCx6pefV4oqKYQ6cCCbkmcm0ML93AORdu051652KP2/m80xvchBZEWPL6LJVUM9UJmcrV3Q1VhNBHKjaOuoWnwlJPbzU0d2I/V59/Gsg6og9cnF3Qh6gTcGxEUxONbDneqSSamCGA1uvOAiJWY3S5Fhq28l2LRFxQw2fNSqrdXvABjncqIe69QxWmPMdjJs2QGsJdtUd/njNXdzc8vLzHmGb6YdCuuJwptTAMA5uhJ+XMPB8JsaMUZVtu+w3TOHOxOyfVwj4dmVKCqB1oqRTioPjAnGbmUrj3xhYpqp9/vJttdr2w3Wx49OhNNpsNHz97xuFuTxpHSzUcaZ45HzakknFFp/1WnM5ZcGowx+nIOB+pwL379wgEDvsDL24/0cjF1kR0OnZ7SjNSdYpxPhyU/DIH6lDxMdKZaMk8zxyPh6VpSUFcxR6qYJ14ugmbQThxGyuGYaumSutcVHxktnkAnbEMO6fRSRej4Ub6uU+kWBaAtxjWIyJko6m74Om/ZK/+yAo8f/Inf8Kv/dqv8fu///tcX18D8PTpU548ebI856233uLp06dfeA4pWnrw4nBZcLnikhAy+CSECr1TBtcQB6t1asmMuuZMrYzWxV6Rcx+MCtmwT/U2FdViq67qXLbgdeRUrQYCWrOH2KU5Ae84eS8LtBS9d34JIbf9ZtG4d5Z3R1N5Wb+0/m/dvCyGb+HhL2i9W6KJhgDXVgmxEpNGEevcwRAdx/EOwcpaDlISprEwjZBmj6fH1R4pnlogdj23B50Vf5wmUik8evxYjZ9zHA4HpmnEWc9jLYnxOGoFAsem76EKD+8/UFXf4JXeYL1PVYpKUA2OfudIfiLHhPQVv3HQCa53uN4xnA8kl/jae+9SbKoSKTOESMCTUtatI+ZVqzCnpIIsptBaUNZhFeH5ixc8e/GccRx16EjjOKSMFGFnqYH3AR9UDDWlwvE4MY0T+/2B29s7bu/uaCSbrovklHn16hXjONLovW2tLWpRaHrnjdHZHMQyt8wsukYAuj5Tycw52WRmWRqatLpjZxCl5WIYGSI2ossEZHMmlU+3Tb9+/Egb/zd/8zf5y7/8S7773e/y+PFjvvOd7+ials+GGF+m7y2+Urxxo0MA42p7ETrvOdtdcLE75ywOWr8UJe5mhOKEwAwoaNXFoDJSIWoNOwTb6KrkU1wlSTbevqeItZqqHpKCLBZLK8DojMgBzccvijStrGMX3ZXKNvRsQ0edZ+WMi9DHzqSl9AZVUcOTpA1Y4LXziWNpyxWvenWpZNWGr/rHg5aAXK/Qj5sQRl0EWVVjcz5S6wQUxmMiT5Ey9wR65TgAMQgXF1vOL7dUl+k2Hc9ffUJ1cEwTh3nk0ZO3OEwjXd9TUmE+zvgKsYALjnEeTVjSEVwkHWe++eRrbKrXvNiJTRTSBqw8WdnNVW217gKuDyQHYau8iblU+mHD8XCH97DdbazhSTvPjpIZXWZ0hdEJB1c4+srkhENJjCWTpHKc93z40X9ydf2ClI/gM1lmEjNjPZIRpjyxO9uC4Qx6fyqTcxwlcTvfcjtes5/udJhqt6HfDOzOB7p+oItRh3mUTEqTCrH4iLNZDKGh9j429gieSHCRnk7bcFt1v6osWi2ZmmfKnKyLspWLUYo5mQRk78jeMdfKVAtJikrQUUgUCp/di6fHj7Tx33zzTc39vOc3fuM3+Kd/+icAnjx58to4radPn/L48eMvOZN6FBXU1M62WlTFpOmdTePE4Xig2BSYBmJp7VIXeozRJtjqZqlgYaAi3rHTUlgL6WsVUso6qslyy6Zm0gJ34DUwbzVgjRmnf2erFTcxxJTSMiV1nmcbn1SXWrWYgfE2QGI5q2jHlWtYAyyahGIIsF6n1ghiVFMD9KoUpeXWYsZCv4Dq5xuj0Hud5Bo8fYxc7M54681HfOv9r3N5dkZ0jmdPn3LS98fDhw+5ubmxaEQ0By5F58yhfPiUdJDH2e6M/e0d7771tqK0SYj2GXPRtuWKaISnmip0BEKFKI7pODLESB87nj57wW7YEiWQUlGx1BZtFY32iohFbCct0aKjwVMpXB0PiA+I8xzTzCyFLKrSlClW2bFZhCFqymTeWIE5G29dNTfLRqOO3cD9B/epCMMwMGw2RsEODMOgfsKvxlxQ9ahgMxL8ojPhjX/vF3FSXbvCmJOG/7Uwl8xUbDRYrcxSGUvW54j2DcxSmNFZEAUoX+Jw4Ufc+M+ePVt+/qu/+it+8id/EoBf+IVf4Hvf+x7zPC+6+j/90z/9hedpY6fXmenexj+p/NR4HMk5WflqBUcaEaSChVJhIbUoz1k9rGr0KYyhpBTdVPlEncSdIukNNFn+9dmLt9BqTxD21tyRc1ZuvAFwDUl+7T+xsoys3zlYDRfn1p571hRgARpl/QwiorJe9lhr8silqCEAKs4GZsjyPq1bLDoNGdM4cf3qiutXV8Y8rgr6oTn42e5c24pLtnPoZpeyilnWWphTouTMbrNjHifunV3a3EKnU2xOrjfWVUYRbYJynjpndsNG+wtSJqeifPgiShGuOinJOdVHNMVUxFllyIDg1pFYnaZJWappCihrsYjW/QtCImubtz8ZlHmyDpTz0c6vVZjDOHF9e0OuhWEzLBoCPgZi161RnH/9tbJgcif31oxDaxEWi/7Ee5Xrap/VIt0klblWNQY1L5u+tM1ue6I61+Z0fOHxI+nq/93f/R3/8i//AsC77767jML+yZ/8SX75l3+Zb3/724QQ+IM/+IMvRvTty/uwDlWMIbDZbFR/bp65urkmp2TrvUHa+rNeUg2jdEOBUE3ARGW7Q9cx58T+MGp6IJUxJ6O8+mWDvX7DBZV2OsFGW90dt5Rjls/vgw1JVG+WBbqqHVxtCqx3KsbhjK7bUHrN92QJ9ZtB80YSaFWBau3D0Lj0ArUSu8A8TThRNDn0keM0mmpsZ8owynjLMjJ0A30/cL7bUaeJu1fXvEozFeit9CW18uzFM568+TZD39HFyttPnvD0449wOM63O2Uljgk68J3WzmspgMcXYdttCLGnJ3I97pGSFi+q2AdIruQyUyfHLm7IufLw3gP6rucHH/2AKsJ8GLk8v08IkVfTkRyC9rVTyDiT2253zrrXvF/y5dYAcxqtYYNLi1NtPPHKyV+yPoAgCjyzGm+Vcy84KcxVuDl8QN8PbLZbtpstNevmPMyjltYaD8SWraNhNw3mOcXeteypt92MupwM+WhAM6YOZCcUO2c5qR41PDr/kFDfyecl5v/NxwcffMAv/uIvcvvhcwIqEtkQzBC1fpxyZr9XgKZYi20jJXiMZ01gF7WmHxbhCdNDC9VaUoWxZLKIsq9Q9LdxTBdGlx3r1agnj50AcbgFnRWBUB27OLDxEVcEF2C724IPdJ3ypbuuW1piq4MQIl1nnXmW7jTcQQkZxme3kle1HgTJax28c5FpHsHm0Y3zjO8Cd9MNqY5cHfaEENj2G0jeZKA0ddofDnQNtHQnrDdTIXI4znfnPLh/Hw9cnO148ewZ4+FAdI6z7RZq4DhO2qbc9zYDT7dLcI4aPS56btKRV+Mtx5IQV9juPD56pmNGMoTs2MQttcLjt9/i3sMH/O//5//Bho4HfqP4zvkZ//7yKTNCmSYGHJNvRnpdV96IWg30knZDTzZPW+5emzh5fH4BR6jVkcgUVyhOS3FNB0GfH4jK9lG2pPdms9W4b7db7t27x8cff0wqyZR+3eoksI0KiwM5RedXfp4eOkLm06CyRZlu7emotbZu4PWNUKble9/8Fn/913/N1772tc/swa+Yuaeilq7TCS+lFKY0az5YNZFrUkbt4niUh+19oMPjsg1A9IILHqJazqlWxjRbqlmZtUF+9dqsm3717KyLpT1wcqiXluU5zlhnpgOyePGcMrH31g+uHIIQAi4o797HsJzZL22+2r4ruEWXvRpq71hrtt45ExstphrjSSWz3W559vI5Uxnph463Hj6x/vTK06dPae8SQ+S8H1QIo+EDmGAF9iVEuDncEvvIdrPh9njg4aNHTMcjzz76mLo/cL49xwWngzvRCbg+BIKN7ZaihuVyd64aC7ev9PwZ5kUgSv8c0sz9ew/wfeQHzz7UlqKS8N2GXBNddBQbRRWDV66FmME8LZiYxwShthSuqdhgjDaz58WpfPXNcc+WLXht3ikUCIJzwRRxdZ2KK2SnKjkCTGaUveh3me4SV3c3AEbZzZwgRMv/W8WmYoo96y8W+Kp9lyK65kWaxqE6H205aKGEe61DW+w1n5OlvnZ8pRv/MB2RXHhwfkFjwKWUDITTq95ooMoFV1TY4TXMq1rT886pTlpUr1+lckyz5ki1kpCFV6/XTy/WkksLi5ikhttuvQPm3ReFH8uNm6jnkodLU7qppJyU+BFdowHoQrWUwfto9XusrdaiGUt9lhVgObdIXfLvUpS4451TolNRA5HHUdt9w47r21turvYKBiZhFzzRKfWj1kqZZ1Zzo4Zr5dobaArc7G8JvaLQh3mi3wycP7hPTZlPbl7x4N4DjvOIyzO16AjrtlBVBalw3B8oXogZ+s0G74XJZ7KD4vW+dLHj8t4F3jtevnjBu2+/yfzs2kqCgdvDrcmigXOy0G51ZsIqOsrJd2pOAs+yaaSuG85FZcHlXEhOdQWzJFyo+KjGOpd1Zp7vYLvr2XYDtVRyhjQVahFVzfVBhWNz5pgz0bcNrkDsqXMB9d2t7r/kQNLuQFt+VtJdpMe15BsMU1L6dsPKWuXIIuIvSbHhK974IWhYfzwemaeJxt0vKk+i+I8zlF6bQNGLoQvVSWFwwbyqbkIt02RSrTZnXnSAhYlcLLVv//qF1qP9/Hon8+liOlWWwd7zNKQD3VzTqAy6fjMQpVejFIyB562ma1a+Da9wXhUEio0NQ9rC0LSiAVC1as24ilYndtsNHz39kCqVm7u9ltcC+KrDKwbCki44hAhEImLXtNgCqyzDqcFXxjRxdXOFf/AQug5qYXdxwfWrK5688w5Pnz6l7yJpSppiSYTgSFJxvjJNI8d5bxyLyOB67m5vYCPs+gFxOuj0fLujD4HD3R1vnJ0xiENcINXCcHbGy+cv8CGy67fkmpnFEYvy0nUCT5PrWsumTd/+NFxu99I5ZVkGHyheIxAQxFc2O22a2R9maxBSA+06qD4x2YSeMTkL9dsmcxbRiRqpqveulW2bc3FWzQkxLPdS/1TTjrThHiGeRHm+VX3VVznVd9T5DBjd26JFxJSFfozFNkH7vwX1olWqCWzZgreNUVvohlty/lwy8QSka+hoEZuuI22clkYBRRov3i/z91p5TY+G5L6eN762bMQYfSefpY2d0pFNXqerWLjpzCOFGKjeLaOaqqinU926EwFN+1wll+WN2+f0LURcOs1k0YtLWUd4qRc0AcbSmlGU1hvat7HyoMlOIk7poOK8RkiiZi9XnR58N44Mhz3u7Fxf7yOX9+6x39/w5qOH3L66VukpE/6owUGMFsIqDuMEasp0LkARttYeXYoQcZxvt2xix7//4EMe3L/gcHWNF08cBu6OyhUI4nFeMZpUtCNRacuO0LSkndeuW3WDagScaR6YG1VmndM0UrQCkkVri2fbjthpqlYbruTQZhivbc0OmI6ZOaNUcZPE9kYLb6lXdMEutzqFIi09sZZsixS1KUlbhjWF9UsXpt5joVLWzd8IUQYE01JF56CqpoFWdL5MhuOrlt5yqoSbli+iuGW1dkyh6gWrLdOFtiN08ImjeHCuao7mKmPNHGth9pavmjcLOMvv2sY2IcMWBsqaF2lquIa+i3dnNQQtwvBOc87gtEGoSFmCZUF11g5pJnSRKF5JGwVwi281TX1INVn4Zz36aL27GSIH6kmqlvFcFeLQMc6jWnsneFfBBC3aq9zy2aWliVS06y5KS4ECyTtGqSQRjgRChBjgrhxgDpwHjxSdMMNRDdnFvUtevfjE5gMUxvFI2PSEzlupNS6eb86V8809Uj4y9AM1z9zfnNHN8PKDp9wfzpGp6qSasKE6mMpIdpBJzLN5eedMmETXSEt32zQaEUwjz6jYy7EqFYkJWgpC9arQW0Ile4+LDRiMOCkEJ8SA3tEaSJPA5LTHA7G9pwalZq3/1KoqPW0YaUGo4uw+a/rmvfJNBMEby1JsbByGIeSiE3ea0ZFiKamJv7bBHG0p+wAiBZEfZ49v+U2VhViLOL+AefVTjQ4Ny2lodHZFkd6lSUWJD63F97QO386y6LyfbKa2iU9d/SmJp51hEdywX+ggSf3dMlXWft88XhNciDEqp8C8erZyjzPMwDkdv5WTAoKlJIuEoE2OEVoObs83EOkHH32k3/jkOp5c5EXsojXGNhqyCGsk5SB7GyUuOofPmXZVKYX9cQ9VuNxdMOeZ+w/e4PnTZ1xsNrz15Akfffwxzjm6GK2UKXRDz6HMTCkRQ+DueCCGQKqZzRDpu8D+7kgatcTmnKOkrIzHHoZdx3DRM0lmnqux6xpPXhdJLXW5bacp2ymg1lJo7fFvQKy1R9eKM4k3HdFm5UZ7sfeKB3lvKWYW5rlC9QSnrqpUnbDbBe3oS0mZhs5StOY2FP9wRtRXvAaLIL15/NccU3udpXmdGdecLe11ukeW6bju9TX/ZcdXK711Uqu2T02lki2kVnRyBciWHMfbZfRCcdry6Zyyu5LJFy033q2b/fUdoZunXWhnr3mtI6+9xn63LiU9gm3MU0153aftLqzWuIXCy/mdtvA25VSpFSnGCsxlVVWxULUh8MsGNrGQ6oQpz4QYlSVoJc8FPLI6r9JXTN3GeAK1LUan5JBjqmTvoFM9uZIzoYNhGJj2I9M4sR021JKJIfLGw4dcf/IS6eHdd97l42dPmeYj4j37/R3SBeLFjv1dXqin4zRTKbx48ZLgAruNCn96r94r9FoCHS57fAAfPQ8eXnJ1dSBNSvbS5FqHXmZLYzR3du2GL5vbUQ0g1R3vTDexDSxpgZ7zrVm0UjIUiw51qi54J4DOZTCmtr2HRYzWQ+FQwehSUYFOLP+WBpkucd6yPkNQ4A5bJ7rKAphzaBOSnAsW4RjxDWsJD1El2GxdK2HrxxjcW3JmVqWRxj6SVl9m9XICiNffVaftpkTlRecspDlbirA216hhsZwPaCNcvbHYTiMDYRXa+PTxWVUZR3Qm52nIO84veveKxPqlXOcMiW+b10fLtzGiSdHhndEFJcM4y0ddi04U/Gu8/1y0LXScRmWoWXnOLqvm8JbXtmZA+6ce1r2oFFdIDhh6nFdqqhJSdBhJyonYd8xT4urmmsuzcw7jROcDw9k58ziS9nc8fPSI/u6GT65f4VzHVBLz/kYNcSk4F+kJ1rWXdcS2E23MKZmwiYxpJHQR7xLeCX3o6TeR7a4npyMi2vhUReWum6ZBjO41ObOUViO7ci/WC7BUA+wztMqARgHmZc0YaDjtcQQbT+UgdlYZqot2QzGt/BijgamKldSqqWjr9WhlSH0Pt4z/xnCppplQq6UxtZCrx1XVZyhlndIksIqyoKmPrvkf51Af0FB03doVK5WZNcZZ3zlttrwzDTlH9YWxJLrYkRFm0cUFrPn8EjrJEmYvnv70d6fHEvWfPOckigBWTMDRTL9ufoyUYwIiOKchWkrUoje8lMLZZkc1QpFaOuW1W4O3fl/nKcVAM1OJ6fpumajbhcCrq1eKg4Dx+vW7NHvfOg9atCK0lISl4YngIQYk+KW+7rz6p+r0miOO2EeO80TX93RhIMaOYbvh+uqKs+2W4zQR+4G3Hr/SnwTcAAAgAElEQVTN1d2eebyjTtpEte0iXXXE6jjfnfHy9oqz3QbfB6Yy4TaRT47XHFLl/nZHdOYTy4Qjcna+IafMPCX9FtXr5p9LW0XUunpOb5hHQ971Oash0Ak+Fna7shgNnVrU0ry60mrRFCCngics0Jk3bftaMyln2nCYEFdsYxHYMPzmJCBctfxhacJyYEzIFQPSWZHN07f9ofyQJSqWdWJum7vwRcdXS+Bxot7bykq64MziOUtAsQVtUtlt4TorXyQLSWtu9ff1d2vY3vLiNfzXkMiAEn109erC4rmXz7q87jR3VBBmAeTcqmnnQ8R71YjrurjcTB88Q9cv+nwtdC1J9du9bXaplZQnjsdpudnV2H/DMNB3nrlkDuPR4htZPdhrn9IWLSeRjUBGKcTKNwjkIqbzx9IOqmZMgyRFl/U7X9/e0ftejehmw703HvDBBx/w8MEDlFDi2G42XJztGOeJ6iuH45FcM12nGvab2C+SW3eHA5tuy5gr3eA4zjPbzYCrOhrLu8imO+PsfAMU5nk2bT0F1hZF4hYCW0XIsfavi4aXC19Cna9GCcU1po4jZxZA0HmxMF89c5mb5oHqHjQH5W29laq8ee+E0PWaWlRLqGx9NGS+zbZzNAUqEyaxCCCGdUx6zprSqICmWzb1Egm2DAc1Ts0xftnx1eb4VKrRIxt1XlltWsbIZlZb2F6l6EwzuxHYWOPgtR+58x01V2vOAJ1S0jZ4wwjMsjeQqHHUDYiheQe3bp3V5a9GAguBK7AxPoKz83rvjbmlOmx5SroIguqk5XnG1WykHov5TB33OB44jiNi8+CcD0t6oM0clXG8IcTKYTxqOO4d0ULcpjDcUhhxKmOlKmSLSj8uOs7vXbA9OyN0HXe3e66vbpA5E8SRRBAtR+uwSq+DQlMuOKk8++QZDy7vk6sOtHj09ts8f/acVGYCgZ0LXHQ957VSOmHyQr038PL2wHl4SN971SSUzDcePKDfbbn71/+HmoXQO9I2MwyRnBN3h4m8KWw3Z9x/cM7heOB4VwkCzgXTn3e2YW3DG7CqobBVbbwsYXkuhjs4cB3McyblQlG1V4suIXZOgT2n17cLHdF1tG7Jaiq8ugf9UlPP5rGb09F0oUEQmVLaxrXP3Nad5VdDr30mWg52bEw+LpdKzVirtmFCpUWIihdoI1p7/88/vtKN74KiuadChLkW7QZjBVFaeLQo09Q1xwlOS0yBoAgvWIODheNyGvas2bxr03RxNvLo1EI6vJjgA6AsDvUiCiTpeZoIRy5GEmrPtzSh64K2yxpI0xnCH4zAE5xTeapSkVKY58R0nLT11jwZqNIrTsN75z278zNeXX8CDqKgs+XxBGfhpStETInVurfwRtDxTr28C6Tq2CLUOhG7gu9hyl53AgVcWRZnLRZBBf17qvDi7pqH3tG5wDgduXzjHt9//rEy3/JMwHE+dBQKXdDpNl3vOaQ9Z2fnFMmkcWboBpLM/MTXv8XLm2ueffIJm3kHnW7e2AXu7mac29B1MGwjUuBwOC6It4jmxWv1ZvXuYvejVY9amO2aPa9oxFAU4ykS8EGpuyrZE8g5kuaClIp3MyJGuJJILR6oeGd4AZVCxDmNDNSpGeBmvf+L0TkJydsAjVKE4zhZq7rQD4EYPaHz+FQZi5ZsV9zCzuNWF/UZSOpTx4+swPP/x+FbS20LXezx1p6IhfzViBalrjXb5UYrjM5Jwr3caBGWerz71J920RTYMdrvsvcFV9EBBlUHGXR4BgIb37FxkY2LdKFT4QUTRdKP5i2EDPigDKvOFFQQsU6wShon5nHWPD8XpnFiGkdrrxXtvjMBz5ozkvU852c7znc7gxZOMAfWspYTRwlCCarLXp2Bpu40zBVSnpjmkVISPjg2Q0cMHikZmw9mG2hBO1i2llMQ8NYaqUBlvN5/8g7kxNFrz7ikTC+ezoWFxVZLYR4n0jhxvtlyvNtTpoTMlbfffIufeP8bpDEjydHHDbXAOFVe3dwyl4r4wGYT2Gw6gnf0MdK1qTLaRK/pX2nAm4XodQXiNMtb6b5iwJpGfQKuhfnqHEqplKzAWkplLSNa9Bmafj/mxJzy+72HGBXvUXS+EXX0ud67Ney3dd+awtrUoDQXHf8WO4bNhr7vTBugEbpWZyS1WtTyQ/beD92d/42H4Je+cRPEZak0e88JodyAGgvVUDKOaPP1kieJsGAFWuds/Hpo3r55Wq2b6lJeeqOXIFkVTb3o34MPbENPjycUwdsfFbm0TV/Rs3tvWm1xQXpPbwqieKurgtiIZxryK5buGBnIoYqpu82W84szLi8vwAlPnz1d+srb4d1Jp59zTFJJToi7QNyEpd8hm75ckYw4nVWn74lNzXEE32DAdulWb1JxxrVwhBgZ08xxnqyn3fHojYfcP7tg8sIomSklLf85Dzb7oFreO88z282WPnRMh5E6ZfbXdwyh591H7+CKp4zC4Dc4YJ4LN3d7UoGuh+22I3bKXGwUZ7+0eEdOlYmbccetG6wxOdvaWQ6n5JoY1g1VctX6eXXY0BqbBKUR3YpTGZ7k61qi9N5k43R5eXHWb9FSTD3h8hlFx5dtNls66/FPWatGQz+w3W3pYjjBK9aEFF5PSr/o+Eo3fkFUgbWoJ2/yR+3m+KB/XFjLYqceyFwPr33Vk/FM9sAKfp080zsb0WTtpE35ZxW88LaYLTwrtVkmY8+p16jlBBuwkMv7oI06ThtQlMuv3r7mrPl7KuSUlmGfXYwMfU/f9wybge3ZjvOLcy7uXdANHVW0+acfeg75aFLgSnZq/ec4Z0o0Fd9Bv/VcPLggS6UbIm88vM+D+/foOyU71apIsaoEFZORbiEri0Foy2qF91SHIJeKOMd+PDLnRIyR61ev+NZ777PteoqDW0kcayZW2GWV7WoUbB8Dh+OBy8t7zHPi5vaOUiqHwxHmypvnDxlkIO0LoYAvkI6FaT+TSiIOgc3ZBhcdc0k2FVjTmkV4pGnV2QYJli6eAp3t/q31D/3+7blSdeNrXu4QUdk1jUJNGk3UhbXgU4HB5mD0Y9UiJ1JahltZJLIsX5EFq2i1/1J4rTwJLAIzSpeW16TkdeT4l2fxP5Ku/u/93u/x7//+7wDc3t5ycXHBd7/7XT744AO+/e1v881vfhOAn/mZn1lEOj7vKLUuIgJtD5/m9O1i6Jdjsd4quOCg1MVCIo0M1GAtllDMoWlFcNaVJ6i+n3HMW2hl1X8L2Qw9dR4ptuQ/hZiLva/2s+t7d0sZT5/fxWjCmGX5THmeyUnBv6alHwxkLKXQdf3SlJSLNsBMaeZiO/Dhxx8qb6Fmgi0qqZCoXO7OePjwDYpkrsZPQIScEsMwcHFxwdBvTIwU8u1EyZmcMl3UBd5kwkqpKo5pcmLeOs2CV6ERDEdoBKNUMzf7O4ahp/eRkjM/8dY7/McPvk8eAvsqDLVy6QYymTmogdr0G273ex49egtxcHe8IwfYnu2QfaamjsvdPXbDBT/4+COSjUNP+5mpCwxDx7Ab6ObEOJtSU7VGmBMZ42VNLFWdteqzptiiKaY5Ht90WiylrFkjspUgtQLS4iw9cxYtOa/e3jw64g25104+h1uoucrmE5v8421N6UYHtzgWEYwRaBJcJVvpsoEVtj5F08zW//FFx4+kq/9Hf/RHy8/f+c53OD8/X/79/vvv893vfveHnXa52A180dIYtmEMpLMvrR7awn9WdBTTnjd0hqWUZSG+O9lUwbvFSGgkoaGSSqxZvuWCbvQqNtGGxSDox2sKPKvlbQo7AgQf6IIScDbbXkkiok03anCElBM5zQTXvd6RRVh020pRGfBaZUGHuy4SvaOkWcO8XDWfBXabjgf3H3J+fqmae7nw8PxSdQVrxQ+RgOP66opaCsdxVCEQ5xn6DX2Mamy9lrW0hdmMoWh61PWeWh01ZXISGqusis4EPKSJ69tb3rz/Bnf7A5fbDW89esQHr56yOTtjfzvyaHPB5eaMF/lAq68fxyNzmqm1cHFxzvPbK64Ptzy6uATvub09shl6vv7OexznkY+efYz3gWlfcVLY7nouLnZKd56E437We2XqFGvXnpwkcraBzTj7E2C3jflrpB1Fzas1/1iaIA1WarmZDjYNFta3dE03ezHpdkct69JZnZQs63ShUdfKPGtptSWo4JimiZSwjV/WsNfWaeOWlFrx5cubdH5oqP/zP//z3Lt373N/JyL8+Z//Ob/6q7/6w07z+W9uXs5SrxNwDfPgxpsWpUQCLIx4u+a4FcltW1MNwJr/OLeGdu19W5TRooHXQZ4W7rtlpSzglqAUTDtdW0xaSlG+fXunELx20IWTttGyeo4l0mFdEH75zH5BnksudD5wPBwIzgQ+UFbdbohcnp2RponrV68WEDCgCq+97+hCJOfCNI02060urbLBWkqr7mX7Ln79YqIRV4zKFe+iGlFsUbZFC45xnrVMJhom77ZbJbIsisHKuitF2W/FJMXSPCv2segmFm4Od0yzju2aJo2Qzndn3Du/ZB5n0lioSVO04GEzdGyGnr5TEG2p57O2tb6e+Z5uPHd6q5dUs0V2r92oBXRuS0SWtXuaHmF6g4h2DNbGHG0r2DZrO+V63/X8SwlyWWXK2su5LAzBU6PWqO8LDvYaaPHZ479UzvuHf/gHHj58yDe+8Y3lsQ8++IBf//Vf5/z8nN/7vd/j537u577w9V1UHXNy0W4pO9b2BA29qgluLKAMdtvca3NFLJS2Dc+qY27+CxEdlY1zy1QUZ1NWnFO0tl3kpmWvgpGsBsCtn1JbKbXU13Ud0Qc2w6C/9rLUd7135LmYIo/Du45UxFoqjRxjIFsLS2tWFWARUflv4Ob6miCiVGHvDS32pONIFwZKGXlxe0vXB3zU4Zy+6+h8ZK5poRd7wx76XuW2G8c/W+dXiAGx+ezYdYjKYmG7cbhSSZJswKMgKDFpyolPrq55/MZD9nd3dJdbHr/xkJvba8LQsU8TZ2Gn9F0R8jzT+cA8jjRO4a7vOKbEISfy3S2pCBe7HTe3t1wAF+eX3L/3Bt//8AMONwdiELbnG1xwxMFT54HZZdK81rGdc3ShW9aANCxAlCkaYzANBHUuoa0JZGmKgdaws6YISsSxJp7grE1c12ctGBFL1rJ0PcET3ImxN5HZ5vHbhCTv4+K4qlTrzJMTo9M8/bIi7b0L9b8a6n/Z8ad/+qevefvHjx/zN3/zNzx48IB//ud/5nd/93f53ve+91oqcHq44AgNzDiJoBU8Ur57tTFQLelfa/NaYg0+UGteRAsWOy0mYWyJdbEx1VUUYCk1413AFw2jfMv/cUTXg7XI6qvt/UWWGytLiKb6Zj4o6SMET85JK3JFQbucM6PNXNsMG0op9H2nKkMii0cSE3QMISwSYyCcX+wYjwdCFbZBJwb7WrSFdVJvkSXhnWfjPORCjZXqMjEKUid673ljOOeQE8c0w3GmeM8UgjZFZRjnSi4F7zV3976qhp4UvDhd6WidM4jNmaep9zggcjfNhNsbHt47Y5gTD7sdLkxkl7lNR3xNBHH42XLpzcAUBBcDsQiXZxfsb18hTkd9z/tX7McbHpxfcrjb03kV8Pj6k2+wH2+4vX1B9TP9tlM5rF6bj+JshJ4q5gLMQxrpy5UITslIzotpIZjXNsaeNu1XKBAJ1BCUAhVabV7veYxBjYXhSFRhEq0CZKNpOyPY+NM16j3OqUy6ztJToxpqUJ2gZT+0sN0t3JWCqv+cxjD6s6cxFr/s+JFR/Zwzf/mXf8m3v/3t5bG+73nw4AEAP/VTP8X777+/gICfd0jL4RuXmTUEaiIWa/4DJ4GxAiVVQY663DFWA9ECOWERTSy16hiopH9EYJ6TndOtZSDsM50M1Gja5w35bdRJH1TXv+s6Q1qN7lorXad29Xg8kua0fPwWgSj2EJYZBarLvlnUYod+sO/i2B8OOO+tI08pzLIYw5NUpmEdRVTJdpyoUyIdjozXt+T9ETnOTPNMyToH7urlK+5ubknTrL0BreHHO4pTjfbqtfRaDPwLTue7DX0kxlbuU/r13eFOVZXmREqFB/cfMI0ztSrGcf/sUoeBNrTde7q+4ziOOHQcuhXDUfCw8uruirvxjjGPjPnIze0VXQw8eesdpHjSWIiuo/cdkRXBbzXypmrU5Meb12/LprH1VPfeLbl8zlC0WRKcQ1w9Se0cXRfoe52V2He9OqJSTzgE1myDUX2xPxZ5NYxIZyw0QnoDt+Vk08uKg7FSfoFljyyr4DN582ePH9nj/+3f/i3f+ta3XhuZ9fLlS+7du0cIYdHVf++9977wHLWsuUyrt3+mLXbNolcPLO1mqRpPaKU+lqdqaGQCF9XUd8TKJJj3mqaZ+xcPkKLdbgI0gFZlF1oOtvKqT5I9JR+hKGrf93hTR3FO6EyEQfPTpHrxFJJTBdZgAylhxTrqSe7mQ6TUSuw6Xr66wnyGgmn2XZvYZzvk5CdfbHGakYpEsgi9dyTnuSVxd3NHlmb9K9Fbzbrq4gymjVBwC5qv9sDRExbdw9De2ELhUjKH45Gh71U4dSy8887X+M//+E+mVLgYOkY5qCrPtifEwPZsx3w4UubErhvIRecfuOiJvSMGqL4wuz2uOIJ0+FRJGbbDJeIqOVUGAB8Ysc/mFbCttZXf1hRApOKD3l8hL2vH+bZudO5gXrDjAl7791WkREe3xxgZoqZUUgoUWUZradNWWzIC+MWogFtmIa6b3uS/lz3ROlTWnoG26dWoyMmaZtkn/+Vy3ufp6v/Gb/wGf/Znf8av/MqvvPbcv//7v+eP//iPbWJI4H/8j//B/fv3v/DcbdYXJ4hm8+xK8miNOc2I+ZPNv7YtBncCmDREfwH59KgL6t/MiaPvejbDhuP+yClv2BmAd3qDWh7ScvYQAl3fE0Og66LKaxUda6AjljV0n6dJxRhx1FKYjqO+NqikdeuuAohdx2hqwzH25Jx5dX1jxtBYac3AtfB1iSIMqKIF3YZP2OZvSq1SoMdz3m9UYitnGtl4LoXZ9AyaAEjFIVknzzggVk8fIj2ahtFyY9F3d4ZW3x6PdCFyvt2xO9tRK+x25xyvb4jeswmRuWg5cX84sOkHdmdnjPPMJkb2TMY6LHRnG2InBKpqCIYOUmC/v0VqYJsd282WIIWh6xhc5PpuTxGxKklrbrF11q6XpWrCKfjWiDsa5edsALMzSCZA7CBGa9l1ghNlWXaxw/lImQveTbZkrHJFM5r6Ph5dHyEqLhRD0OGd3ivoWZLJzJnz8W2GgyWaDVy1u68+yZ0GqV96/NCN/4d/+Ief+3ibl3d6/NIv/RK/9Eu/9MNOuRynyGvrV1cvUrRl8hS4cKY3by21ak39qrcGy+LTFywPnZRJtHNKRMGqs90506ghr15Iv2wcnDPcQL1YtTq9N4Zf33UMw2AhpeED9vl0Qm0hzZnDfr9ECsbZ09Fd40gIkX7oqUsdN9ANA51zfPzxUw7j0cZkacSSLdJZGY7tGzaPsQI8WgUWvFTzOa6tDf3NpC2kWxfwAl3soPM2h60yzRPzrHPeS9XW2uAcvno6FwlGgCriNYymsSbr0hI8zTPbbmAaZ7rNwKNHj/nPG1Xy2XS9Rlq5KKqPUpOrVPrgdVAOOiORKITBgVRSSngvnG03dMMlVy9v2R8O1OKJlv/2MfL+O1/jBx9+yHicCLEtc2dyb1ZLN6JSzutwCx8WzJWSRVN8gRgVdwp9IEZtDJMqpKzSWdF1BALxhDAkgukeyHpjXKPqmkZfC/eDAnzBeyRWQvFMSclejdQWDEAsRVNW5LQDdS01qyH7MW7SWZawcyZCyVKWUAnt1XAtG5JGbQzY3BPEQmDdAmbP1+uxiFg6U0ZxDqLG5czjDE2fzK2ob61+Kem8xoe2cGvoe6KV66RqEwuWt6c5EYyb7mjfSaOQ4LUVtthIr2meiV2Hq0K/CVxdX3N1fb2kP40G2lIh12a7LdEIJzH+SWmJFrkaFbj5B3tu18qW9qycEr4GYvDErqdzjlSrsQINBBPBixoK9YCeTb+hHzbggvVYaI46zolN7Dkej7bZoOsH3nryhMPzTzjfbAnO8/J4RzzTsdw1KGB1fu8eL+6uiMGTqkZJXdcTvWcuiVomXJh0qXc2vmuEoespaAju+w1ff/c97vYHXnzykjkl1cjTUUuaIlqYnxdhi5OWVqfKTmCe3VdC9PRdoOsCrurAFsmFKWekHKipsul1nPjSFmubtkWswSu5yHmNjkoV5pRVU6IUTRUagO29NmCZ3fAOnSsgLK9t7ciuRfvu9fz/i46vWF67bWWWOv3KrVYPX33VEdBSoTpiWOfFO2mASfN7etTTjdJCY+/IpdJ1jmHT83C45O5mjxetsZflBA1KNdKFKDMvBKVC9r1OSA1Rz+zdCkR6lP0WQ8DZEMhWvlmbQNbXJJtBl2vl8v59rm5uuL69BfvujeSxzl1bPXaLIfRinT62wEAsv2xN6QtQpI83oxLQ6oqzUVJSzJBK4ztUnBMwueoshocUYSXyejMkFecquMBhSvQuMI8zfVAewtl2x8vyVNH2qnPzDocjcegRB7fTAX9zxRtvvsl/fPAx/dYjc0Vm7SxMk2NMggwjw8YhMVNjYRwzcbjPWCp1TvRzoaTCdrPla2+/y0fPnnFzuFNdPRSJx60hvrNQvuX8rYzsLezvO09nmz4YSUtyRZVthZKFsU6L/HrsIl1XSUXbt1oe7w0jaUZXrIyabA5iNTWmVPJ6t5ze01IKEvSzOatC1RbsGz7jvDOy14+xEIdu4kAVMNl3oAkq6IL0jYxhqjRKa15WMk2ssuEAp06wqfC4E9Bw2Azsdht63y31efWQsgzJECcLWOJQBZ2+7+j7TrfLwhVof6MLRQSqdVU14g6NrOMWbn0RYdtHtrsdzkqMr66veXV3jXfafx9PQJ3FeNn3xEGoJ9IkiyM/Df3W69CEONsFcsCImExZSwnWkpYSeQS3QINaq9djuVFrXRnHij43nTgtAc61IEcgqCrN4bCnSOVwPDDETkuyaGr0/NUrtuc7ppq5PHvAZug0euo8h5uZGCMpeea54JioHkIUwhZchf184OH9Bzi3pe4T4/5ImjLdMPC1t9/l9rDno+dPSTlRiiw99+ZgLefX+1eKXmsN8R1d53Cu4tHSm3OBPE+I6MRm7ckQ5pQwU6k6iKKOy9laas5FnM3iczrNeE7aulysIpBT0YoKLGXjZiiwtdAiuUUH4GRPnIK+n7v3vvS3/82HNwKMbgoNrdqASBEQXxcAxjmvBqJa+Grdda1pogFkCuotS5MGgIXg8FHQOekjpespIatUc7YN3DACcYpUt3ALzcO6EBW5r2Wx7KVo3Z2qQFKuOoixCnSbATnure9buQOIgppujsxSiEPPnBLX+1uTYRLCso1aRCSvAZWIjYx2sjyD9ky34iKg9itb77Y7jWg+Jc30mbHKnxMhrEddQKTlE4iBsc5IWSYG6YEqCRn3hOA5Pzvn/Xe+zvd/8J9415PQKkXNM8O2o4uBR/feYH935Fvf/Cb/97/+KyULUiCmaoYisImFSITi6ZzgtoVpHNnPtwzdls2Zox51MpNLlcNtZggd/9t73+Dly1e8vLkylqGQlbxok3s8OZiGQlQNBe88nqCdeAhEK+9hakIbODvTNTLPMI1KlW4heFunOtyl6PrVMgneC30MihnYIBlNm7TM7J0Be84RfST4SK25KbSBtYMbvxfnhFISju7zttxyfPVimyaoocBeC4Nsc5mYhFT1RsGtE0xNo2IJ6dtGX5DUk4XqzEhEI9Nstp0q5DQL0z4MNN9K42mDMrv6rlvGdQe/jrNewBn7u7PniUAMgd12x91+j3OeaLJZMUa2m4H98cjzVy8t1PQnlYc2kPEkZD+JapZPu5R8Tjb+6eb+9JVoGMHn3YrXA4U1R/2ym3fyk2/3hrWJRUE0jdyOacIflSeRdxdsd6qk88bDh3z4yTPOLndsNtpMJE64ub7i8sEl203PcZ41t60FnGc7DJyf61jq/fFIrVl1/XuY5xGA4CPihHGe6Lqeu7s7trszppy4d/+SzdmG55885zCPuIoyHb3SoJuR7aL2UDhRenGalZjVdR2IPbeBd6JErq6DWgN5HNXAszZf2S1Y1HgbbhRCsFFqzsA/A6LQNNMbaKmlPOWbrBBCPVm1lio4NShfdnzFmntt0wpN/KDpqMWoqqal5CUcb6WQ5f/tu2k8TfvvFATUC6vvU6XggpI5xFWKTTxpnGfv11CqIeldFxkGBfK6GCl5tvB73VK1FJyFaaCfNVjb7MXFJf0wELtOF4xT0tDd8cCLl59oAO1eNyTtM5gRf83ZrrfTMJE1vDnJ7E9MhKUlLQxaDMen1oX/X9/nrz3UXtZwcWmKtX59P6laz5jyzGE8cNN1nL35gHAYePHqJTEqYLiJHVdXr9jutrzz9lvkaeQnvvF1/q9//X9Bqgpiiii+Ejq873EukfJEjE6n4EilykwSHUVWnQqQ+D5ws79hu9lyc5fZ9D3vPnnC1d0Nz6+vFDhzdh3armqdmblQsijvpKiirUfooleDRGUcZ0IuCFGpuksYrj7ZBU1PfWsxb/mF4QwOp2mCNL6+s3XvVmanOYbXbka7Aa1kiBr3JtP9RcdXuvGVi14JXTjJ1SvBQ99F7QbLQtcZeQHjtdumr5YfnYpqniLawert3q/907FtfF+XPuqApQ32ehXQ1J7LwXJ7KVmDKqd89857chVrBW0jlKpt8J6UZvPihc1mQxVhmicA5nnm2cvnNB8gwhI6t7Kcfpu2o5e7u/5u2defb9k/S3b61O8//YIvdxCfe35nn512b2hgn5Bpde7W069VguM8snOXUDN+6HAxImmmTpm46ZiOR2IXoRbGeeTh5SO+9Y2v8dHTp5Si3YtTnhiPkTnMTFNmnCqDQNd58Lrxs22fwnkAACAASURBVI5YIkvmMI9EFwld4NX1K87PzpmOe0LX4UNgEzekOuGxcWIFglgf/JQw2QIzYpDmTOc8QxcIoaOIdjwejkeqWCu3aMfeWoFiqVjVWgg23EMEbdF2rHr5WvCnhbK1tJTX4XqNKoOfzcAokLrexnWtfNnx1W58o04GwlIm881yGaAHQhehiwqUNdGOlGwgRAuB3eodnTtlOTnrc66WX4OvOhB5GDrKsaiWfW2bfvVjykKzcdQGwoUYaO0/eUrLxk85EUIkhGhjrLHW3spUVDQx18zN7Q23+z2ndlsQsogyv6SCZ2kSWjrMGsjZogwpi0d5PSRoxuk00de/HKdRwY9yNJPKEkmUJvnV7puD2Ae25z191zMeJg77EYejH5TwdJxHSohE53n46BEfffB9fIHj7YHeR2tQ0WGqN9fXdCHytXff4YMPfmBsyInbuwNteKQ4va+D90SbXehE6dRn52fc3R1xJPrY0w0dt4dbdsOGs+2GGCL9ZsecR/Az1Y3WDl1VXFRttbIbnRKg5jnhY6Na63sFHxFRNdxFIzIGVTbqNFr0PjDOM9M0kXNWTQmxDS+yUtS9p03AxQRSPaiD9AqGa8muLqBeW7ZNafmH3eevNtRvO5aWK2P5UFWdMSeL4XOusdy8QZ0ZaakQnAApLEYkBOunlooPwTa8qD66FLqhI4+TpRroG62AAdjFd84Ru6i5fcn4oLLZwzAwJ0edk1pfyknfgFqfUiqx7zgc70gpcf/BJZeXZ9xNI7f7I92wwYdAqcLV9TXhNGV3LSQXUm3z8CwPlDWfbm8pJ5+72QL32qZfv9rn+QP53H+5k9drPqmXSmvhjayyDBN1gvOC1KyjpLP2lRenhr7vOg7HI2w2FLQp5fHjt7m9vjYQttMUT9Sz7vdHLh/cNzBVv3fseuYpIaIW0gcNyUt2xNDpWqqqQuOiJxc915xn/fTWg3B2eU5OiasXVzjvePzkDSoTV7c3jNNRnYsVM3zQ6LIguFyRJZIsHI8zKQOiTTdNg1AsfeuiZzN0tFHaE7bRxRFjtKxo5fU34RPlqrnVmONU708EcU27n0USXf3A/5ph/4rltXVBNRFNTV8c4K1l1mr9IuRcTZgjUMVR6uuspSaa2QxB2/QNdfdOLa5IplQhlZltt8MHp6oo0oyPhmBN8z3GSOx6QLnjMUbNn4JGEd57NtsNU7LN7xyx6xmnvZZ5Nj1Pn32s3XsILmj/eT909ClRpRJjz27YcDzuqTmrtl6tzGkm5cJuu+U4Tkxzfm1hrendabZtIXjLue2xdQiogXGfWh+f9RANXGJ5XXuPapwExzrpt9aq7+EBr4u9Wiqkr4E5Z52dN00gQm9KQ8kVksCUK3OtnJ3tqOI4zom57hlToR96QuiYxmnVSxAQUe9YgOwg9IqtBK8djl03EC8Hxmni9vaOcZwIznHMiU9urnjj3j3EVY7HiRfPhYuLHUPccSBTktJuNVrDjI9FOiKkkhG0Bp+zN5Td7rOVnB1atlbGaNNWFGt9FlurBmLLmqoWZKnrI1Cd6hnUaT7Bttp9lmXDr6vgx9jjY5ZONc5ZSnMOXVjRBfMvhZTahRHEBapEHHVlnzkWKaQGhKRUF8kuF8QktJV+Wyy/HzYb0r5NTnVgQohS86LSM04jwzDgQ8AFZ0CWx5vEci5qAMQ5626b6PqOOc88ff6MKU88eOM+1IKPnm7oSBbOllKZ5wkfAhfn51xfXTEeDyp80UUE4fFbj3n+4gUhKm6QclYCibAsmAWUbJf2xDicQn6tl/uzHt88u+30Bg6JPSYn/+5iZBgiMUTSlJjntBgUwRSOxPrCTQuuzYocp5ngYD8eOaaZTTdwzKgmg3MUcYR+4NmLV4xjYrvruL3d80Y/ULLeU900eflCVSrJGG4eFQtxHQSnwptdF/UemZcu1tk55cTzly94/PhNDvuR27tbpjHT7wbyrMKaIivb01mzjwRhzplcZQWOETP8svw7OmeEH7dcQ7E28+L082abr9ei3oCG+6kq6bpVuxyOKjqNt6VyremsGYsGZos0ydkvPr5a5l4FXFhqnKW2psUWrhcyGt5nTONdtBEGtPa8lt8ALKcPgTwnTQUEShJizEaxNVpu9sxUCHArE5uwIYojiLacghqM4zSyudhRvc1GE0VyRRQ9LSXjfaAfIlNOlKCe7emLK/bHPT4oJzxG7ZOf93dsuk49f04UIsF3jMeR3Xbgwf1Lrm5ubAM1enBnKYby/JXXHRXHEBDTykulULBwtC1WbH+0kN88dTV2mvolyxXdKsHdvBD2WmcyZ8pe3DBsB6ZpZEzJUiUNu52reJ8R73Sog2ATgvR9Mg6pauClVA4yqqjI4JnmmXeevM3hcOCT6ytiCDojEMfzZ8/Jta0BMZl17RBYuBvVUYpGQN2gDT74jPPCMECMspRIvTim48SDxw9xHi7vnZPLzP54x72H93nrydv8+//3ffOqgBjDv3pq7pjKRBcDMWyIHaQy6ZUXVo3CoFiQFMzRqHbka5BMrRTJSys2ZmBAvTy2zkRUvLM6B1IXQ4zl98G7ZVajVI2av+z4Sjf+EILpzCufIYh+sYrW7sUDHrwPxM4tiiZNtooTr+Zo4OBaKnFgXU1+iQR8UAFDBWEyJVXL3TJdGBAqVYqGsFb/LzmzGTrdiZbphk478FxostWV4uHZ82ekkm0AQkdK0/9s72tiLLuucr+19z7nVlX/d9ttO7ZjYZRMQgITBhFCETbtCDktRwkMEMrAihQpQkRRpEgkTFCEImAGDJANE4QihPhLBplAWsKJmBgQYIxAsvSesf1iV7fb1T9Vt+45Z++93uBba59bdrf9/JK4/J5rRy1XbtW995x99vr/1rdg3bdIfY8yrqCR8N5hnDhyKXIAY84ZW5ubgAh2rl+HjCMAthUTdxDMYoEknIXz8xZG5y0lYyzsO9cwb4yhjw94f42DTliDD5Fjt2OKLYdRrSxFq8fMcikFu8td3Ni7aXvOJ6BaqVQC2hBItkYfVDwAbES4YQINn67oICHg8quvMjkqFJY8FoYVjfiCbrbzHGo7C8x+l1qRYH31kRN1JbAJ5tjWFsYhcwS5Avv7A17e3sa9d9+FvhOcveMMcj6O6zdu4NzZO7GxWGAYpzl/Yp5VqYWIP1hsvZajSlauS11k8jAIkuE+nMVZBNamQmBPe07Nc6tzjllgzEna8jow47We0IZhSVKMQARU3sWCvwHSV8eYLG6sGGvBpAVT5RSRqGxqYAa1YsrFurhmgYclBRs4RQF27vEh9IsORUfW14XdZLUCWgumzOTQOGUEDViE2MprAkUXInSasP39axjzxA48Cxk08PBlI/ggAiQidBH702CTZtlJxnn2AXvLCRtxC3msKBUYSkYuuzh5/BgAwd5yDydPnqLwv3YNfR+xv7+yfEWGhNhCklIrS5aJ7qzUiDKsEJRwT0sRo07FwiqudmAsoaRCgo8yVagJXYsvDwQFbcfZRaazBT7wF/69VZpg+J/Qmwst7PDPGzKFsRcqf8czVEEbXNk+w70yU2b+eq2KMmWUEFEmadx7mDIFIwT0fY88VYzGXjOOVJK7+zfR9Zznd2zrGJZ7S1RjcXbSjCZtFopOWVGqKedCxOmi69im3YWGR0nWz5tXA9xCzwApB4A5YMzCNEHrBA1VWnKvmnfWlK5gbWgIWjL6rfp0DherXwUhK6Ko8cRHbPYL5MCa9woDUOjKqiXTtHr5CLTqYjNsVJG6hFoVq9XEdl2zdjkXxDTTHIVSkJUbVkF3yyeceGY6Rpbytja2sOh7lGlCzhOmkpGrIHYRRdldN0/3rQgqrMuahu4DcGJrC11gwnIYJ1QBUuqQCy1TKYrdvT1E2SJSbX8f586ehWrFzRs3sb+/D4jQ7VU/BHMFZJgmhDUWHOo9aVagyiwcTv3E9mddy8h7I9EsoS6XjSgFgLOceh2/qgOPgrn5PMjMdVgS0A62k65oQwv5l1AZBADDlG1+BxVaqJ6+4nNRBXsejEbbQxT38nKpwGqAyIBSM7qO2fQQImqxkMC8HYkRioLLVy8b+i/hxPFTiKXgypWryLmg6zqIJOQycm+rDcwECURhnXjsvJSW8AwClDyhGpFTMZpe8vtVTGuMu60kZ/vhCPQYafRCYL6iAtZIRYy/c/areVYAf6aifWMWZ329Zf/eyy+/jM985jP4hV/4BTz66KP4kz/5EwDAtWvX8Pjjj+ORRx7B448/juvXr9sXK37rt34LFy5cwMWLF/Ef//Eft/1saf+Dd3NCa0WSiEW/wFa3gUXkGMiwPjTAzx9vEY2ddM36eOKrKpMuPlPcv9nT1YI1oTDaJXLPC/q+A6DYWPQ4d+YMTp86hb7vrYVYG1e+2BcGAcpEFzxCUKeCzcUCXYiNHxAgFiHZhBQvh7E9c0LXkbVmGMZm+d3dfmMyz+k32BdO7PmM8g9oBsqBke315gG4k6QzJHhWgLava5beN7eUfCCBtJ5c8u/0qmKj2LJE1ByHyfzdluPxBKyArdPBhCn4M1dttOLNh5G1i1AmJrP1+jsgpv1cPYc031dFwcbWAv2iw829m9jZ2cEwTug7DkNxfIlP4vE99/ev/w9ml51ZRw04VquzKKyBsNb+68+X97G211aNmWHh83PxfzEwaekdegBmBX6b9ZYWP8aIX//1X8eHPvQh7O7u4tOf/jR+5md+Bn/913+Nj370o/jc5z6HJ598Ek8++SS+/OUv47vf/S6ef/55/O3f/i3+7d/+Db/5m7+Jv/iLv7jlZ3sHPdTLEV43JUDieNxgnBcVqzKijCMpiVqyaHZZo3XE5TEjWv2TpZAyx0ZBLCkmxovGfY2Bs90C+PPxTY4u2uwX2OwXxkW/j+ObWzhx+iRyrViultjfH7C7t4vRYMW1VHRGFJIU6ERw9thxxGpxmvUcLG/uQTfJ9DJZylgUWC4H7MQb2NraxGq1wj333GNewmAtonFuUUaAow3HyabIiLYecqlKMpOiSGLgD4UxAVXCWN2lXhdgMesb1tiOgh/K2RVVdRe1vQrAY34DlJhnHCDGSiQc2RVsdBTcI+B7ggmjGqNNULLOVCG3fW4YdwqYwpqvJJH9yMthIqi1YBgyQiQPYtWKacoYcz6oOAMQkgDBynJFOQTT7j2Y9U4pQhEggfH3tMYXWbQSf2GZ+zxNgI3XhnJw6pQzwWMxIMaEYHz7Ldtv3lO0UWTBNV0LlQokJDeT7Xn5TIAklowVG8Lxg0J2z58/j/PnzwMAjh8/jgcffBDb29u4dOkS/vRP/xQA8MlPfhKf+cxn8OUvfxmXLl3CJz/5SYgIfuqnfgo3btzA5cuX22esL85/q014xdxCIdEbVClEGxsLlEERJUNqmVWlbXa0LOoBjHQQRDU6L6hNiFHEqoDNWfF54yVXdEGwubHAuZMnEVXRdx1SjBjHgfRaKSF00ZhtBCc2t3D62Ank02ewHFYYxxGrYUC0epm3rGwtNrFc7WLr+Cau7lyFqGAcRwyrERuLHnUkhqHUij5FTLlgGDM2Nzbw/ZdfxgPvfz+uXLmC6+EGSi7m4s8DKEMIVKDV0UzuybhlVF5/ZPktSsA4jih5mi0NfN9pnR3mvN6os86W5M1LCm0HsaqCgyUMRVAdfGUjRUUAa8P2tmePWb3SAqzhBpqVRMv/rGl6ohvFwx42s1RT8iIKVOZAZH/ClFgFGqeMUjy8MOda2RSjIhACLRtHYy4FwQA1EhSpi0ipw5QLcpnW5I/WFmJw8Ckj1oDQETsSYuBwkykjpR5d16EiGGXZrPzWsRdza622/AGqHsBGENcfkEJ0zDSv5XUO2q3W24rxX3rpJfznf/4nfvInfxJXr15twnz+/Hm89tprAIDt7e0DBJx33303tre3byn4PkIagQcOYg0NhQ9+MrLHPIwYphFjzoxXZQb/uLVQ2xSBtCaFEFKDNqrUlkX1jr8kAXli7/vpU6dwz7k70EEw7O1CMxNCMQbkkokfB7ARI4oU7C2XIIULWzcRE6pMrX4dwHKMqiJ0HW4ul6jCEldQQZcSus0t7K1uQKyfuxrkczVmCFaIKeLVq1dxxx134IWXXmqOIhlZifl2uKb3ehfTASHAurQ40LMoqbJDYFIyWAIOsiZYQHNp3a18vUBCbfJMEIuzedhQCdd16ioyFiuqRmjhKC92jGmLL0UEaj0P3n8hZrU1WHXHQoDq32uhDy9/5m7geZpjfb8sDuokZJosTXMziwsYCWGs9GGCqOLKByyNBgUsWUePvh74DAmBpB1KBVlLhUZ39y0A8K7N1KEiIk/FRmV5uBqMDJQEIMkgv4rCPSxMarf+fufxb3HWjL8AZvz+rdb/seDv7e3hC1/4Ar761a/elief5+KNscWbtXgWj5WFWjZJQLQHV5QneBoHTDb0oUJbMq3ZNq1rriU1g2o1eqtATW4BZzQFgUor4crx+NYxlCkjmVB1MaLkjG7RI2cOxwAAyZx+e2xjEzUIcmZzyQaAjX4BjBnjMGC1v49+0WMYB2ydOo6b+7sY89Rir42NBaRbMCEYo03VJcPttBrQRTZj7O7uIsWI83eexyuXL9PiFCadAG0UXm3QImD3XJpgiCmcMlakyB51LdUO85p5sJDIk1/uWanOXhkErenJed3cawgJxiV3kKpsJi7hc2vz5Sz2DyI2Iaa26yham5Iu9uxa3Greycw7z59DmFupPYFRiieFqgGyBIrCacVAS18EUzYxBORQG+Eq1IlfgXGil5SNXNTTSiLOWCRz4tDOJvMaYqhRYBhGU8Rq7eLS2r2JEGQPRow0OikEhNghDxkAIdIjxrVn48/LlaD1uMQfQjlvmiZ84QtfwMWLF/HII48AAM6dO9dc+MuXL+Ps2bMAaOFfeeWV9t5XXnnlltYeADKElts1dFWokOUVSv6XKhVZKiYUFJsdLeZKVwFqFWyix1bsUKVgqBkDCqSFOLW5lShAHZh4kQoULUzWBMGVa1dx/N77WDeugv1pYslqGG3aTCVXnikdVSBp5OG3ckoeR8TYocaE2kccO3sCl6+9imOL49i7toszZ09j9/pNTOOEPBVsLQKSgDxrECgCsrl112/sGnEnD8u973sfXt7exmQlvVymJlDBBL9l9FvcbWWwCiv1kKySnpExctXaYm4ICBARJZm8LwW9A3PpGY8WK9cRWaZS0Yt5DJVzARPYkQcoNATUMNOZNh6DIDbyGaw9G+EKCvn31IS4CZmn1hTNS2EnXIBIsPq4EIhk5a4YxRrYLORThdbA0CRUBIkEFRX6+hELqGbL3isQGLhJFYwjr6LAvNRKw6UqmNRjbislSrC/owIsWlEyuf0rQhNaCIE+tRYkAaActtqFyGy+QcUhFSl1OLa5gcG8YJ8vCTFPQ6lU0lvE+G+Z1VdV/MZv/AYefPBBPP744+31hx56CN/85jcBAN/85jfx8MMPH3hdVfGv//qvOHHixG0Fn6UOB2LYgzHXLWtFVvLR0dWzju/1vzc3bGtjEyePncDJYydwfOMYqwGBI5BEyevWx0QabgMBuVoWAwntDyvsLfcJzuk7pG4BuktmJdT4zBUcbW1AmT51UFXs76/Y0aWKru9w6uwpvPLqZRw/eQLXb+7ixImTCCkh9R1RXKWwXCMCx3C7m+ZJs93dm9AK7C33cO3aNfz4j/84S4slt2czD8BY31m3rnMoFKNb13mIB8yVr+4tWB3YZ8FPU+G/tZ9zrpjGzP70SjirOqDE4u51CHHLAdj9edNUzgVTnjBOE7EZrsRitMk08/WuD7W027Iz4AlFryRoC/9aJj54RSHMmXM7a1izulA0V9y9nnXvNRgghvPr5v23H1olodgZqFZJyBbbV8NcEJxnaENTii1bD9hZA/MxIZHJdxrpbZhXsNhYYGNj0WYz5jwxh5CLEYeuu/y3Xm9p8f/5n/8Z3/rWt/DBD34Qjz32GABy7X/uc5/DF7/4RfzlX/4l7rnnHvze7/0eAOBjH/sYnnrqKVy4cAGbm5v4+te/ftvPnqjgEaq0MhTlmVpehcmfgplQ00+453kWIWEzLdjSKhFd16OfRpRuwjiNKFpJXGidea0n2gS/NUsEwdXrO1jccR5RBDF2gNMwK0dXD+OKXXrRarHjiKEy2xxTh0XfWVwteG33NYxaMeQJx44fY0KnZEwl22gwd1u1uYsQ2PfRtSxVMeYJuWTciDdx7twduP+++3D9xg28cnkb0LkZiefYhW2tFz4AIXaYxmx1e1C4ooVDVdcUzmwHGi5d3R2z181L88SSK5hg+QRxIS/2N0EglQddLf5Vs2L+DKvFwRxQYmFDYApWgcbItK7c5kv186AH7oHTf/1nxZS91m0FNcsVec888z6k1M5Gmd1aoVVbk9MctnrvSHOv0I5nVYy1IFbBmEqLvRddjxRp9VUTJmRqTk+Sig9xYcI5S0bJGasVQUIpJWAcGh7CQzwiM9eTsU3j33aJ3o7J4Ue4XnrpJTz88MN48fv/A2XMiFXQCTGQ4m6dJViqEOKZaz0ARIEAXQXOpWPYSItWBokx2GADbUQbGRVDybg5LLE7rFAAlOAWysYbCxNUJzaP49ypM+h1A1BlQ8nNGyQH6TuUmptLHeMGXVMIhmnCa9d3sCpLVFTEjYiAQBBITLjn7ruxuehRa8ZyuYflzjWcOH0W//2/tlEUKDbdJ1oPv5ZCNJ4Qw761tdnm3Pddh2z8fa++ehX7yyW6vgcA7K8GSLS5dxZLa1WWASsgBuFxhuP12BAi6GzABy1WbvG7S7+XwcQ8L8ZdBTEBW5tkoc1jwTQoo6ziRCOK1An6lBAicx/VvJWZ6VZmeCq884/tvz48xcuNGpgAW/QdUiJoahzqzKBjf1eKNugx8xOsTSo4H2+xKdg4JtCimAbBuLQefFmz6HOcsbb8BVnfHbTdMtlLkbMfFn2PGFlKXa1WKCW079D2zllw+zTjMEotLAUGC1kVhC6XahTbpjpaOZS9IT/2Yw/i0qVLuO+++94gg4eK3OsXASOAmhUZTmnExI3WSoYdzAm9loyxFYUCS6vhnXjKYl1Qm0AakUKCdB2uL/dg1bzmWnGvFCERJbUal9gfe0gKGIYVpmFAmUacPXMa13evo6IipUhSxv2M5f4Ke/sDJi1MOnaKrhdoYE19yhUbHYkataMlz9OIxcaiuZbAfLZIGDJbr3FiP/s4Tjh16hTj+Twh9SQEOX36JEqeWmLQaaNL8Qqf98wDbqUgHCIhwkEaMbGxo+SMXMzbgKHy4D7o+pPTA4zEjWkmusW3KktBm1PPPuBo3PCCHILRoxOPwPsVvlYts15njEPL1CM0pROM9rzroiHlxpY7UCuX1eIKIxyUW6DNyuNkYNj8+oM3617muni/+eL7PZrIhUq31JGCWyun86i2zjsPOdxzg8gaPTYVjDeflUoGXs/jrD+WVl51N+xN1uE26fQ0u6uB8REnxCYEFWhm8q5UZnXnXiTXpmz9RK3s+UoJwVhyxjwCQVBHBQIbalZ5xDAZV7m5QfwcKx+h2PRSsPMqLhA7wWSY7qGsUJOi77vGvrK8tsJyGqCJLKwqFXGDvH4Qh+4Cy919LNIuNvsOG4uE1Wofp7dOtGx1ABqziluOEATjNLbS1Thl7Oxcw13n78CxrS0shz2krkOeRpw6dRI7OzsAmBvIXoaChVIpWKWqNiy3V0A496+DCGPJYRgoBH6krFtvrjGvW3yzU2xR4MFWp0ej6w7QyqlU8+Qq9pd7zIyL9z3EVv+HskNQ4VyIwGQDNmc4MQ83UZm5tV6HaPunaLkitnkLZkp2wx9YGCRhzhnN1cAZn9Du/C1cZ9ssP6HwB1ftw4s1CzWlIME8WFN66xagibOa8lKERAxBzYVwcgsHWyjmLnLwvMe7uEknRkGSBCkjB1qYKxaU2rn4E3HrYu8jaCcAxTYUAhRaC5Y/EgZLwEQQPLG32m9xm4YAb+3l5/HzS6lY9EahVCebezdhWq0QbRBHt+B467FM0CiQxDr5lDOCDdlwIoacycRaKrC/XCKFcySSKBVdF7G/t2JpKyaoJJRqCEaraIghusZpQkoR0zTi6tWrOLa5gXPnzuLatWuoteDUyVO4btN3SilsTYXHo7TALUGmjKdU2LDkib+us7i/Mknk7+Nb5rwK2k/r/3wPHR7tRBSY5cARhSI2uZa/FGt6msuIlL5owygZBggyCjzRQ8GlhcylIGS+J5ilo9BjjsGNKm2mNppd8dfd2IEla5ZT5hcO7MntlodHAlgCWw/saQtnYbgGOfhmNXYjzmUEIOxliQGoYpUqL+O5UnYvFjiQr7nVOlTBF9Bd71NCLWxsyZoBiaihoE4+2GAGKgYRBATUXBGKogZ2doVaiYmPHVALNkNACWxQGWrGqmQUoWsKmwUnEYSjCpM8PQTH4yZCSBjziIKCVZ6QVYHVQHG2PEKUgKErUGSzopagkg6AoKzGVmvuek6+rRAgcpxUFTbdeAcWXVRrnFFF6jmjLmd6DUEqx0yVgle2t3FHPY+zZ86jTkAeM+44exbDNOHqtR0LFdjfPU0ZXUgQMXMo3M2i1h5bFDmPSHGBKOSOc8pdciSgKV0nQxUBgpU36dIWMgxFoGaWWMvUGXy3NIHL2RKAcYFq7DZiPepsgLHOMwlYWEOWN7c47l2MP1HKHB5Nzpfo/lIFNFstHsQHumCpmPcYAfg1WwmQeqwaTsEsqFkdiQwrxHgZmCj0ZLF5DXWthu+hFbxrlDmP0BRiaVFU62MA9wOq0MgW8o1+gaoVJVdShxsPn6sWhXEbmO9f4RWCN1dMhyv4wsRH6ipihpWTCop6Y8p8gwCsNRJm5QGIoChhrMxucnprlECknSQUgBRWNUONxsvyW5ZJ18bg28cOG90CUEEpI7PhMWMcM4ZxwmLRY6qKrCzlDSPx17nyMz1ZWC2+RaULnxLZgkjlRX79JD8rMQAAFGpJREFUnDNHaNnhrDnDM+JuWIJRSBUp8MGaXc8a+auvXsU0TThz+jRuXL+Gjc1jSMMIiQHf337V+AGLlfEse229/GwuYg06q2K1UpRcEIKwtNbyK2s23a7J8yJeQvN6uVswjqKe2WJj9OcmzKzraDXpOTdj/rDZRE4IyrkYU4677ARcQT0rb/j2ulY69HRES0Ci/X3zWfR19wD3yqXlGhqF1tyIAJhrnUJCnyLymHnmLBxlCGo5KpkBVfDraRrBvrAJASwy0HYNIsCiT80j43msbcZfsXyWv1/sXnkNzsP4A5bzfpSLiTa2wCYaauTRhLhYjdhcRcB+tkBMjLGkFE+8kHlmqgUFgkkniHaoARjLgAbTFDp+Hgs1qxwiNmKPzbTANHF0dNd3iCGiZsX+MGJ/yChEF2EcB+yP7OdnPGsgEQVqJhEon3fgP1Hsr/Zx4vgm+i6h6/uZDQikeXIcdkqxSVuIEalLraattUIDE4/b20tc39nB/ffdCzbrZJw6eRI3bu5iuRygqmQnFk4HlhCRlUw90nj5gWEqmAyR6Jarils8LjFBYPVkbqpi+OTAFXosWjGjJWFhmVjooqyFz52STAp6T7kLaalg/oan2WJWw98DjN3dkHryraEQX1fagp8fbdRYIRiwxz6D+AYghsxxbv5uY8Gx5AW0ZohEsiLVyqYfT0aqNj1xYGKuK80wXwdBZdq8PWok1v9TEFPcFeNAkM6UyeCjyjqteHu0DeBsiMW18OvN1iHz6qvhx406SJXuuLrQr8VkMK1o2VDHmldkI4WwOjHo/uTKoYY1CLvnIt08t15uAYIASQKkAtP+iFFWSLFDLxFByY8nJwO6/SWGYcTeuGIGPRM/Hbz22rzCChQe/gAbpmn9Avv7Syx6ssJM00TuPKth50LLlWJcA53YvYtj9HlIcs6IkQxAwzTgf/738zh75izuPH8nXn1tB6dOnMRy73JTlIRw0qL0XUISwVQVUy5NtlvvCqyctiY46xYyGa8BrK4ucITcDFlF+yy3xrynrutY6RjHlqBtHIehGmUU31fVa9rcx+jsRwcMmVsEu0CV9nxlXQF4XqNlzdctvtAzi4GdfrEilNC8TXqWHkrYcA1VILCZaWvRY18HE3wqJF5LaK48wUmzhykq8BHswOxxWJeNDQ4Bqyy5QIQVHD4bKmYFLDSzEVp2jlxkXg96ev06XMEfK6QWhKQG1lBrckBDg7kbBLhL4+gzQjtjCChuzwVuaij8BlBxPEBLfsh8VjzjKwWYhhHXV9fRpR5hq0OvgigRJza3kCRiD0vs7u5RAVUyCA21UqnAiRg4ECSkaGWpgqpGxVwV4zQi54LlamCpx9ByKn5QDXpqe+CHZ5xGpErh6bpk3pIYISnwyvbLyKXgzJmzCAg4deIEbu7eRBDBmAtCzqyhi2CxWJDcEuAobCe7MEG+VQbbm034HDDvt7oQBTgLjHe8+ceQqorX6Wi6Wc0EdhtW/w4/wLVZQi+7eUKu1sp5gFg3Dty/JtzN8pmrrzPpiJcf+QGBUF07B2EtKcbj5NG3eTg6Y/UBQUoRmxv03hiWoCUkxTSMc0nSM4psxNH1c+3Xwj4G9mGQoyGEiMViE7UKVsOIcSpwZ6mqlcCDJaxqRa3Zwq53cYyfp4KgilpmqC6zbR4X+aabsFZa2BTjmtsrB/DmKoK+6zEMK2TzIGpAKweKST7ZeIIlC20+fAYEBbUMGFfLRrwQIhl4Q4jYAAEusY/AyMREFlI7iyhSSBQsDdjbW2KaMkqg1lmtCoJUMqUm4PjxY5gysBomDBPVV7VMtdaKFJPx+UeIDVEErBRlGqHWCgTB5tYWrl3bwTRlnDl9BmdPn0LfRVy5es16uxnf09txS2xuite+1zLv4qbRv1Ud62/AoGZaLLYNASXnNYts1jpIU7AO1mmt1P7dsPKTHVji6V2hRHSJ2WzP8QjoWrdWXY/nMXcReqK0GnCDutR6FIIgBEUMCSUXjKuKGpmUKWVOJGKtjMk98DZvekhaCkLgsJfOujdVTUCNK7Ddn5G8QHxsNst0dtUMN9b2ahgJj95YLLC5uQHjmUIp+3PC1aooXga2QiWf3FuA8Q9V8Es25lqZ3RRae5+yCoNKsh7rZIJ917NsBQ+khYJfih28AIk2VRTqfRvN3FuUAGKfGcM7z7xjujtQudRaoNVn3ldSIUnAVr8JkQlxUgwdMHUFEyr6LmFrYxNQxTDsYxjY1SXgjPVhAIJE9H3P+X0oCF6hsC6tWqzhpk6IkfTMgVJh8TETW9HCAvIKKLquw82bN1FyxfHjW9hYLHDH2VO4doOvZVT0XQ+d2OlVWmwKhigWmkTDuatbMY+hgQausXSGWbFgv+NnuUV36PHczadWWgyzVVam87j3JsCeuRciDIPHtFCwi5NVlKCm0M1DcAVAXn3z8MRKaGtejIdNWoFprCiT9V+4t+H6ZC20aKg4wFz1QiCSC79BuZlwJCnIypieHcrMPapGoQbfwXZdrlimXAjcKkAIZGMidp9ju0sW20sT9lqa8oYnBNeU9q3W4fLqY22T14Jvj2E8QdOcObMsueTGNOrTdmu1g6CK1TCSZRbUpgdoiDwEw+w+Qt2lSkCmk+ZkjlkrD6Zjqm1vpVRErRDvVEnMYKdosahy+EXXobnuMSYMw4gTht3PNUOHvNZQYVVZ8yxp/RjjMRPu7LK8RjLIUvhLqdDC13de28HGooNCsbmxgZ3r7Pl3zj8AEC0GZrLEmyHbPI8QTaG0Zl87+FWLy6s9E09i+TMD0PgC0Ky42v2I5S8aA68qgJnkY2bXUXuv4QKMc9FHjYdoMf06KMntsymNkAKiklI9W7ehnwRVYJoyJ9NU3qM7QZ4XWI94/AjNfY1GoFIqavQuw8TPX/RUjsboUxyBKHNH3oGkuynfKkzu1pI9u4A8Zezt7aHnQUIQzjWYnEnITk0raVge4EDl4BbrcMt5YY1UQAI1XvXHN8du7vCHEKECjGWixhZmaUWBYJtQo2ClBRMEnruOhvoSS+GXQJw2UBE0kmK7CGKfgFhQc0GK7Lpj0kuQYoKWgoWFGZJHZuIFyHZogv2rJSPniT3/AKJfZ2A+IC4CigpCilZPVuQ8Nb45uNUxBeWloS4mANW8EAI8gs2u05KBEJkMioKrN66j7ztsbm7a51QjBqEFypqYndeCPsyWFuD+Z3sOEir31kNnsGacq0BCQQzWIqkJmiPZYlSByliaNOkUUO+Yoz9jzVezToHDUSh9/MKqzAF4iTdXXmP0wQFr9fwmrBaWdKlvaDlkKhN6fhGhso9CS5hd5EZMGJqyctn3//KcFbYZFyp7VD53gQ1JCdVc7fl6yIjEhxqq2H3PzWmhnXLKgCuJYvmk1ZRJGBuEcb0EVKkuGWhOmz3fH5hz70e5/EYAgXWaYv1yvUTh7hvBE1bL9JHUtql8cDNt0vosEYdoWlhs3HoOBGJJTqtib7VCVCYMs7vVQMucSkzGoZ+hkxIiHAiGcbLD5XIFr8eXUlssG8R457Qg54IUJ0iM6LsOpRSMOc+SDo/DTTEGj+H4WdlGN/d9hxBTo54ecyH3nAiGcWhEJA+8//149dWr2N1bsuOxUTdxX4ilYEPQ2FxebQqGS2bPzJJ5EkrLjjtq0D2V9Yc5hwoMybooyFkgUpFR7JDDWI/tmZtX4cNCUko82Z6Yk3DgsAgOnh1vTW2JRIHhKvj6MEwUSFkbQd2u2eMf/+R2IJsT7aEAG6AKhnGERAol26RtbyPgggmYYQ4knAEY1rapRXbevRQIQcv5iFUsWjeme65rFQNd2/u3kPvDFXy6lcH62NH2e6YrmjHVMSqFplaLw11DW8LHNiBFS/p5bA+0+E5gYB2BIb/E4KmAimDUighAKgdkuKMrxjQT/J8QhDMExb4W1ORxZcA4TiiZE2Nj7K0cQ5Rer4rNzS2ICJarPdbVK4jxD2EOcSAUUDW6aZmbNADWdMuoCLEgVsMJBNJbI7DTDCKYSsHOtRs4c/oM3nfP3Xj5lcu4duMmN5/BahPcLiXEroMOI0knJ3e/PXFmzwewvIrH1DOwpjoWp7olYvkVSsZcZ91ZD+uCowWFZUryJjJJOwNvOGpKvCEo0BWnvKwJprnoDShn2fN2tmCORKmm0FkmrPTZmkDPnSG8aaaGpP2Nf1UwBahQDKPNenA6sKIEboUIb3EmzRZ77T3BqqKv+1wxYk9et1Rp8oDq/AHalDOvixOe5odk9f43WYeb3DPfpJpLjTpn3x1oEyLdcomVXoEn6MBH4vVQBR+kNvoi67m333opiMIrCCzsI0/qT5vXBAComJqWnn8ZxQEsglUtGGtG6Vi6axh1NSDSpJBOEGMPVZb6xiFjsk662AukBkzWvdViTDvMaviAWlgWY2mHGXVFQOq1JZP8KmNMCIEkF450S1Fx+fI2+n6BRc/hnyIRQLHs9hxXwyyzk2bQYjkOgQo5t4RgRQiztcnZy7Dromg/NbeZHss0jdY1Z5seDv49rZeDX6jEtBY2HEV6Vs6SPPPHy9r38rpYJq0HjIgbFo68Mwh2EDgVmyqBYO2KpB25lpT02XQi5P6Pwk7DYWTcHWJE1/ft+WitBpAiH/+suJj85GDYtR3w0McYiPxqSlVoZnjVMCuts2vmK3grnD5w2GOyqxEpFhxMdohrWRd6IHYCico+erMuUpRcJgpTBgIfjjETXaANKHCEWRDW58dxQq1UGKVZD0saorZEGpMqgiyAap6bRRIQ+4gaA8pUULJiQxJi4sAECGPAYtYKUpECGy+qjK2EWdcUT9sCf3geowBmDfn/Y4xGUaWcJ5/I5+whD6nmeDiiCJZ7uxhsok6z9Gv/YK49H8xsIluYZL6lWAXBvYAQqUrp6rctbIIBBKube/xazT2eW5KrlPa7GXBjz2JtTzyccjZd5sxej0rXedvWQo0DW6x+O9K8xBjoNVVVoFj/Bdw1D618KhBj6qVIVmWZUcxjyyUjQpHMW6LQ05OhYnFuAe61hDUOAtuQUjJ8AKsf4lmYed5jjEjJuxArnFqQW+d7f/t1KIJfnM9NOSeUPdqAI6/EYJXRcAkhAqknbFV7hdaAkgvKVJEnlvoEwkyvu+dAMzgeIxvHBDPvSqtHJFpoQybpgc7ItXVvkqWTMGv/oIjJ6reiZo3J456sZlt0viLxmwkJIfK6o7XSiR0gjUBARIWVhUD8ACsPYsyqvDJy9BHD4N1wEiJiQivHeXEnJTIUOfZdDKLaxcD4me+GgF6NdLHFj373VDiBnYNiZayYmBPx0hs3g4fPwgSn7KZrHdD1iROIGh+BAbNsqCmjN/Nk1HIcIXIKb2Aehxz1plBaiUFhCIdZ2TCo53BRNd/QlF2XElIITLya8ALCbHy26oeVyWZyUIOUa26Hq3qtOETEZOGDMjypxev1zo5M5eIxfmhJCzSl4BOeyG/gGAcqV5ZbE0LgM+Ug1YxxyhZiwNCiPrxlDX+8tg5F8K9cuQIAeN897z+Mrz9aR+s9s65cuYIHHnjgDa8fCvXWarXCs88+izvvvJOkj0fraB2tH+oqpeDKlSv4iZ/4CWxsbLzh94ci+EfraB2tw11vnf47WkfraP1/t44E/2gdrffgOhTB/+53v4uPf/zjuHDhAp588snDuIS3vR566CFcvHgRjz32GD71qU8BuP2o8HfL+spXvoKPfvSj+MQnPtFe+2GMN38n163u4Q/+4A/wsz/7s3jsscfw2GOP4amnnmq/e+KJJ3DhwgV8/OMfx/e+973DuOQD60c5Zv4HWvoOr5yzPvzww/rCCy/oMAx68eJFfe65597py3jb6+d+7uf06tWrB177nd/5HX3iiSdUVfWJJ57Q3/3d3z2MS7vtevrpp/XZZ5/VRx99tL12u2v++7//e/3sZz+rtVb9l3/5F/3FX/zFQ7nm169b3cPv//7v6x//8R+/4W+fe+45vXjxog7DoC+88II+/PDDmnN+Jy/3DWt7e1ufffZZVVW9efOmPvLII/rcc88d+nN4xy3+M888gwceeAD3338/+r7Ho48+ikuXLr3Tl/FDWT4SHOCo8O985zuHfEUH10//9E/j1KlTB1673TXfbrz5Ya9b3cPt1qVLl/Doo4+i73vcf//9eOCBB/DMM8/8iK/wzdf58+fxoQ99CMAbx8wf5nN4xwX/9WO077rrLmxvb7/Tl/F/tT772c/iU5/6FP78z/8cAG47KvzdvN7uePN36/rGN76Bixcv4itf+Upzk9/tZ+sHGTP/w17vuODrLaqHbwUvfDesP/uzP8Pf/M3f4I/+6I/wjW98A//4j/942Jf0Q13/Lz2XX/7lX8bf/d3f4Vvf+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<h2 id="Class-Prediction">Class Prediction<a class="anchor-link" href="#Class-Prediction">&#182;</a></h2><p>Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.</p>
<p>To get the top $K$ largest values in a tensor use <a href="http://pytorch.org/docs/master/torch.html#torch.topk"><code>x.topk(k)</code></a>. This method returns both the highest <code>k</code> probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using <code>class_to_idx</code> which hopefully you added to the model or from an <code>ImageFolder</code> you used to load the data (<a href="#Save-the-checkpoint">see here</a>). Make sure to invert the dictionary so you get a mapping from index to class as well.</p>
<p>Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.</p>
<div class="highlight"><pre><span></span><span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
<span class="o">&gt;</span> <span class="p">[</span> <span class="mf">0.01558163</span>  <span class="mf">0.01541934</span>  <span class="mf">0.01452626</span>  <span class="mf">0.01443549</span>  <span class="mf">0.01407339</span><span class="p">]</span>
<span class="o">&gt;</span> <span class="p">[</span><span class="s1">&#39;70&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;45&#39;</span><span class="p">,</span> <span class="s1">&#39;62&#39;</span><span class="p">,</span> <span class="s1">&#39;55&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">image_path</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39; Predict the class (or classes) of an image using a trained deep learning model.</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># TODO: Implement the code to predict the class from an image file</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">image_path</span><span class="p">)</span>
    
    <span class="n">processed_image</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">images</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">processed_image</span><span class="p">)</span>
        <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1">#         images = images.float()</span>
        <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span>
        <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
        
        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
        <span class="n">ps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
        
        <span class="n">probabilities</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">ps</span><span class="p">,</span><span class="n">topk</span><span class="p">)</span>
        
        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">probabilities</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
            <span class="n">p</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
        
        <span class="n">map_</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">class_to_idx</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
        
        <span class="n">classes</span> <span class="o">=</span> <span class="p">[</span><span class="n">map_</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">)]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
        
        <span class="k">return</span> <span class="n">probabilities</span><span class="p">,</span> <span class="n">classes</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img_p</span> <span class="o">=</span> <span class="n">test_dir</span> <span class="o">+</span> <span class="s2">&quot;/100/image_07896.jpg&quot;</span>
<span class="n">probs</span><span class="p">,</span> <span class="n">classes</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">img_p</span><span class="p">,</span><span class="n">model</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;PROBABILITIES: &quot;</span><span class="p">,</span> <span class="n">probs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;LABELS: &quot;</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>
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<pre>PROBABILITIES:  tensor([[ 0.9762,  0.0120,  0.0030,  0.0024,  0.0015]], device=&#39;cuda:0&#39;)
LABELS:  [&#39;100&#39;, &#39;17&#39;, &#39;71&#39;, &#39;18&#39;, &#39;82&#39;]
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<h2 id="Sanity-Checking">Sanity Checking<a class="anchor-link" href="#Sanity-Checking">&#182;</a></h2><p>Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use <code>matplotlib</code> to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:</p>
<p><img src='assets/inference_example.png' width=300px></p>
<p>You can convert from the class integer encoding to actual flower names with the <code>cat_to_name.json</code> file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the <code>imshow</code> function defined above.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Display an image along with the top 5 classes</span>
<span class="n">img_path</span> <span class="o">=</span> <span class="s1">&#39;flowers/test/100/image_07896.jpg&#39;</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
<span class="n">np_img</span> <span class="o">=</span> <span class="n">process_image</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">imshow</span><span class="p">(</span><span class="n">np_img</span><span class="p">)</span>

<span class="n">probs</span><span class="p">,</span> <span class="n">class_</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span><span class="n">model</span><span class="p">)</span>

<span class="n">class_names</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="n">class_</span><span class="p">:</span>
    <span class="n">class_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">cat_to_name</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

<span class="nb">type</span><span class="p">(</span><span class="n">class_names</span><span class="p">)</span>
<span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">class_names</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">vals</span> <span class="o">=</span> <span class="p">[</span><span class="n">class_names</span><span class="p">[</span><span class="n">k</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="n">probs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Probs list:&quot;</span> <span class="p">,</span> <span class="n">probs</span><span class="p">)</span>

<span class="n">prob</span> <span class="o">=</span> <span class="n">probs</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="c1"># print(prob.shape)</span>

<span class="c1"># print(&quot;Vals list:&quot; , vals)</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">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">vals</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">prob</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span> <span class="p">)</span>
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<pre>torch.Size([1, 5])
Probs list: tensor([[ 0.9762,  0.0120,  0.0030,  0.0024,  0.0015]], device=&#39;cuda:0&#39;)
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fa184b40c88&gt;</pre>
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