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ChatGPT_강의_Training.html
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<!DOCTYPE html>
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}
#sel-dragBar-1:hover, #sel-dragBar-2:hover, #sel-dragBar-3:hover,
#sel-dragBar-1:active, #sel-dragBar-2:active, #sel-dragBar-3:active {
background: rgba(43, 43, 43, 0.675);
}
.dark-mode #sel-dragBar-1:hover,
.dark-mode #sel-dragBar-2:hover,
.dark-mode #sel-dragBar-3:hover,
.dark-mode #sel-dragBar-1:active,
.dark-mode #sel-dragBar-2:active,
.dark-mode #sel-dragBar-3:active {
background: rgba(255, 255, 255, 0.675);
}
#sel-scrollbar-1, #sel-track-1, #sel-dragBar-1,
#sel-scrollbar-2, #sel-track-2, #sel-dragBar-2,
#sel-scrollbar-3, #sel-track-3, #sel-dragBar-3 {
-webkit-user-select: none;
user-select: none;
}
/* Spinner for when it's translating a page */
.TnITTtw-cta-button-layout {
position: relative;
display: inline;
}
.TnITTtw-spinner {
display: none;
text-align: center;
position: absolute;
top: 0;
width: 100%;
user-select: none;
-webkit-user-select: none;
}
@media screen and (max-width: 800px) {
.TnITTtw-spinner {
top: 28px;
}
}
.TnITTtw-spinner.in-text {
display: block;
background: transparent;
position: initial;
}
.TnITTtw-spinner.left {
text-align: left;
}
.TnITTtw-spinner > div {
width: 12px;
height: 12px;
background-color: rgb(0, 71, 46);
border-radius: 100%;
display: inline-block;
-webkit-animation: sk-bouncedelay 1.4s infinite ease-in-out both;
animation: sk-bouncedelay 1.4s infinite ease-in-out both;
}
.TnITTtw-spinner.in-text > div {
background-color: #000;
}
.TnITTtw-spinner .TnITTtw-bounce1 {
-webkit-animation-delay: -0.32s;
animation-delay: -0.32s;
}
.TnITTtw-spinner .TnITTtw-bounce2 {
-webkit-animation-delay: -0.16s;
animation-delay: -0.16s;
}
@-webkit-keyframes sk-bouncedelay {
0%, 80%, 100% { -webkit-transform: scale(0) }
40% { -webkit-transform: scale(1.0) }
}
@keyframes sk-bouncedelay {
0%, 80%, 100% {
-webkit-transform: scale(0);
transform: scale(0);
} 40% {
-webkit-transform: scale(1.0);
transform: scale(1.0);
}
}</style><style type="text/css">/*
* contextMenu.js v 1.4.0
* Author: Sudhanshu Yadav
* s-yadav.github.com
* Copyright (c) 2013 Sudhanshu Yadav.
* Dual licensed under the MIT and GPL licenses
**/
.iw-contextMenu {
box-shadow: 0px 2px 3px rgba(0, 0, 0, 0.10) !important;
border: 1px solid #c8c7cc !important;
border-radius: 11px !important;
display: none;
z-index: 1000000132;
max-width: 300px !important;
width: auto !important;
}
.dark-mode .iw-contextMenu,
.TnITTtw-dark-mode.iw-contextMenu,
.TnITTtw-dark-mode .iw-contextMenu {
border-color: #747473 !important;
}
.iw-cm-menu {
background: #fff !important;
color: #000 !important;
margin: 0px !important;
padding: 0px !important;
overflow: visible !important;
}
.dark-mode .iw-cm-menu,
.TnITTtw-dark-mode.iw-cm-menu,
.TnITTtw-dark-mode .iw-cm-menu {
background: #525251 !important;
color: #FFF !important;
}
.iw-curMenu {
}
.iw-cm-menu li {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Helvetica Neue", Helvetica, Arial, Ubuntu, sans-serif !important;
list-style: none !important;
padding: 10px !important;
padding-right: 20px !important;
border-bottom: 1px solid #c8c7cc !important;
font-weight: 400 !important;
cursor: pointer !important;
position: relative !important;
font-size: 14px !important;
margin: 0 !important;
line-height: inherit !important;
border-radius: 0 !important;
display: block !important;
}
.dark-mode .iw-cm-menu li, .TnITTtw-dark-mode .iw-cm-menu li {
border-bottom-color: #747473 !important;
}
.iw-cm-menu li:first-child {
border-top-left-radius: 11px !important;
border-top-right-radius: 11px !important;
}
.iw-cm-menu li:last-child {
border-bottom-left-radius: 11px !important;
border-bottom-right-radius: 11px !important;
border-bottom: none !important;
}
.iw-mOverlay {
position: absolute !important;
width: 100% !important;
height: 100% !important;
top: 0px !important;
left: 0px !important;
background: #FFF !important;
opacity: .5 !important;
}
.iw-contextMenu li.iw-mDisable {
opacity: 0.3 !important;
cursor: default !important;
}
.iw-mSelected {
background-color: #F6F6F6 !important;
}
.dark-mode .iw-mSelected, .TnITTtw-dark-mode .iw-mSelected {
background-color: #676766 !important;
}
.iw-cm-arrow-right {
width: 0 !important;
height: 0 !important;
border-top: 5px solid transparent !important;
border-bottom: 5px solid transparent !important;
border-left: 5px solid #000 !important;
position: absolute !important;
right: 5px !important;
top: 50% !important;
margin-top: -5px !important;
}
.dark-mode .iw-cm-arrow-right, .TnITTtw-dark-mode .iw-cm-arrow-right {
border-left-color: #FFF !important;
}
.iw-mSelected > .iw-cm-arrow-right {
}
/*context menu css end */</style><style type="text/css">.ui_selector, .TnITTtw-ui_selector {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Helvetica Neue", Helvetica, Arial, Ubuntu, sans-serif;
display: inline-block;
}
.ui_selector .select, .TnITTtw-ui_selector .TnITTtw-select {
color: #000;
text-align: center;
font-weight: 600;
font-size: 14px;
border: 1px solid rgba(200, 199, 204, 0.5);
padding: 10px 15px;
width: 201px;
-webkit-user-select: none;
cursor: pointer;
border-radius: 11px;
display: inline-block;
background-image: -webkit-linear-gradient(top, #FAFAFA, #F6F6F6);
background-image: -moz-linear-gradient(top, #FAFAFA, #F6F6F6);
background-size: auto 48px;
background-position: 0px -11px;
box-shadow: 0 0.5px 1px rgba(0, 0, 0, 0.10);
text-overflow: ellipsis;
overflow: hidden;
white-space: nowrap;
}
.dark-mode .ui_selector .select,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-select {
background-image: -webkit-linear-gradient(top, #4A4A49, #40403F);
background-image: -moz-linear-gradient(top, #4A4A49, #40403F);
color: #FFF;
border-color: #747473;
}
.ui_selector .select:hover,
.TnITTtw-ui_selector .TnITTtw-select:hover {
color: #424242;
background-image: -webkit-linear-gradient(top, #FAFAFA, #FAFAFA);
background-image: -moz-linear-gradient(top, #FAFAFA, #FAFAFA);
}
.ui_selector .select:active,
.TnITTtw-ui_selector .TnITTtw-select:active {
color: #6d6d72;
background-image: -webkit-linear-gradient(top, #F6F6F6, #F6F6F6);
background-image: -moz-linear-gradient(top, #F6F6F6, #F6F6F6);
}
.dark-mode .ui_selector .select:hover,
.dark-mode .ui_selector .select:active,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-select:hover,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-select:active {
background-image: -webkit-linear-gradient(top, #4A4A49, #4A4A49);
background-image: -moz-linear-gradient(top, #4A4A49, #4A4A49);
color: #FFF;
border-color: #747473;
}
.ui_selector .select .detected-ico,
.TnITTtw-ui_selector .TnITTtw-select .TnITTtw-detected-ico {
display: inline-block;
}
.ui_selector .active, .ui_selector .active:hover, .ui_selector .active:active,
.TnITTtw-ui_selector .TnITTtw-active, .TnITTtw-ui_selector .TnITTtw-active:hover, .TnITTtw-ui_selector .TnITTtw-active:active {
box-shadow: 0 -1px 15px -10px rgba(0, 0, 0, 0.85) !important;
border-bottom-left-radius: 0;
border-bottom-right-radius: 0;
background: #F3F3F3;
}
.sliding-text,
.TnITTtw-sliding-text {
position: relative;
}
.ui_selector .options,
.TnITTtw-ui_selector .TnITTtw-options {
margin-left: 0px;
margin-top: -4px;
background: #fff;
border: 1px solid rgba(200, 199, 204, 0.5);
width: 231px;
overflow: hidden;
max-height: 313px;
position: absolute;
font-size: 12px;
box-shadow: 0 2px 10px rgb(0 0 0 / 25%);
border-bottom-left-radius: 6px;
border-bottom-right-radius: 6px;
display: none;
}
.ui_selector .options.standalone,
.TnITTtw-ui_selector .TnITTtw-options.TnITTtw-standalone {
border-radius: 11px;
margin-top: 16px;
}
/* for now, it can only be on the top */
.ui_selector .options-arrow,
.TnITTtw-ui_selector .TnITTtw-options-arrow {
display: none;
position: absolute;
width: 32px !important;
height: 18px !important;
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/tt-dropdown-arrow.png);
background-size: 32px 18px;
transform: rotate(180deg);
margin-top: -1px;
}
.dark-mode .ui_selector .options-arrow,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options-arrow {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/tt-dropdown-arrow-dark.png);
}
.dark-mode .ui_selector .options,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options {
background: #525251;
}
.ui_selector .options ul,
.TnITTtw-ui_selector .TnITTtw-options ul {
list-style: none;
margin: 0;
padding: 0;
width: 232px;
}
.ui_selector .options ul li,
.TnITTtw-ui_selector .TnITTtw-options ul li {
padding: 10px 0px;
-webkit-user-select: none;
text-align: center;
font-size: 17px;
-webkit-transition: all 600ms cubic-bezier(0.23, 1, 0.32, 1);
margin: 0 10px;
border-radius: 6px;
position: relative;
}
.dark-mode .options ul li,
.TnITTtw-dark-mode .TnITTtw-options ul li {
color: #fff;
}
.ui_selector .options ul li:last-child,
.TnITTtw-ui_selector .TnITTtw-options ul li:last-child {
margin-bottom: 16px;
}
.ui_selector .options ul li.option:first-child,
.ui_selector .options ul li.option_selected:first-child,
.TnITTtw-ui_selector .TnITTtw-options ul li.TnITTtw-option:first-child,
.TnITTtw-ui_selector .TnITTtw-options ul li.TnITTtw-option_selected:first-child {
margin-top: 16px;
}
.ui_selector .options ul li.whenHover,
.TnITTtw-ui_selector .TnITTtw-options ul li.TnITTtw-whenHover {
cursor: pointer;
background: #f3f3f3;
text-align: center;
}
.dark-mode .ui_selector .options ul li.whenHover,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options ul li.TnITTtw-whenHover {
background: rgba(255, 255, 255, 0.5);
}
.ui_selector .options ul li.option_selected,
.TnITTtw-ui_selector .TnITTtw-options ul li.TnITTtw-option_selected {
cursor: pointer;
background-image: linear-gradient(145deg, #01EF92, #00D8FB),
linear-gradient(35deg, rgba(1, 239, 146, 0.25), rgba(0, 216, 251, 0.25)) !important;
color: #fff;
font-weight: 600;
}
.ui_selector .options ul .group,
.ui_selector .options ul .group:hover,
.ui_selector .options ul .group.whenHover,
.TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group,
.TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group:hover,
.TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group.whenHover {
padding: 16px 10px;
color: #8e8e93;
font-weight: 600;
text-transform: uppercase;
font-size: 12px;
text-align: center;
cursor: default;
background: #FFF;
}
.dark-mode .ui_selector .options ul .group,
.dark-mode .ui_selector .options ul .group:hover,
.dark-mode .ui_selector .options ul .group.whenHover,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group:hover,
.TnITTtw-dark-mode .TnITTtw-ui_selector .TnITTtw-options ul .TnITTtw-group.TnITTtw-whenHover {
color: #98989D;
background: #525251;
}
.group-element,
.TnITTtw-group-element {
width: 153px;
}
.options .dd-search,
.TnITTtw-options .TnITTtw-dd-search {
border-bottom: 1px solid rgba(200, 199, 204, 0.5);
}
.dark-mode .options .dd-search,
.TnITTtw-dark-mode .TnITTtw-options .TnITTtw-dd-search {
border-bottom-color: #747473;
}
.dd-search .dd-input,
.TnITTtw-dd-search .TnITTtw-dd-input {
padding: 16px;
padding-left: calc(16px * 3);
width: 168px;
border: none;
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Helvetica Neue", Helvetica, Arial, Ubuntu, sans-serif;
text-align: left;
color: #000;
font-size: 17px;
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/searchfield-icon.png);
background-position: 16px;
background-size: 16px;
background-repeat: no-repeat;
margin: 0;
height: auto;
}
.dark-mode .dd-search .dd-input,
.TnITTtw-dark-mode .TnITTtw-dd-search .TnITTtw-dd-input {
color: #FFF;
background-color: #525251;
}
.dd-search .dd-input:focus,
.TnITTtw-dd-search .TnITTtw-dd-input:focus {
-webkit-transition: all 275ms cubic-bezier(0.23, 1, 0.32, 1);
outline: none;
text-align: left;
}
.dd-input::-webkit-input-placeholder,
.TnITTtw-dd-input::-webkit-input-placeholder {
color: #8e8e93;
}
.search-failed-plaque,
.TnITTtw-search-failed-plaque {
text-align: center;
padding: 20px;
color: #8e8e93;
font-size: 17px;
font-weight: 600;
}
.dark-mode .search-failed-plaque,
.TnITTtw-dark-mode .TnITTtw-search-failed-plaque {
color: #98989D;
}
.rm-recent,
.TnITTtw-rm-recent {
position: absolute;
width: 10px;
height: 10px;
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/regular-lang-remove.png);
background-size: 10px 10px;
top: 15px;
right: 10px;
}
.rm-recent:hover,
.TnITTtw-rm-recent:hover {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/hover-lang-remove.png);
}
.rm-recent:active,
.TnITTtw-rm-recent:active {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/down-lang-remove.png);
}
.option_selected .rm-recent,
.TnITTtw-option_selected .TnITTtw-rm-recent {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/down-active-lang-remove.png);
}
.option_selected .rm-recent:hover,
.TnITTtw-option_selected .TnITTtw-rm-recent:hover {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/hover-active-lang-remove.png);
}
.option_selected .rm-recent:active,
.TnITTtw-option_selected .TnITTtw-rm-recent:active {
background-image: url(chrome-extension://ihmgiclibbndffejedjimfjmfoabpcke/res/images/ui/down-active-lang-remove.png);
}
.TnITTtw-hidden {
display: none;
}</style><style type="text/css">@-webkit-keyframes load4 {
0%,
100% {
box-shadow: 0 -3em 0 0.2em, 2em -2em 0 0em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 0;
}
12.5% {
box-shadow: 0 -3em 0 0, 2em -2em 0 0.2em, 3em 0 0 0, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 -1em;
}
25% {
box-shadow: 0 -3em 0 -0.5em, 2em -2em 0 0, 3em 0 0 0.2em, 2em 2em 0 0, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 -1em;
}
37.5% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0em 0 0, 2em 2em 0 0.2em, 0 3em 0 0em, -2em 2em 0 -1em, -3em 0em 0 -1em, -2em -2em 0 -1em;
}
50% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 0em, 0 3em 0 0.2em, -2em 2em 0 0, -3em 0em 0 -1em, -2em -2em 0 -1em;
}
62.5% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 0, -2em 2em 0 0.2em, -3em 0 0 0, -2em -2em 0 -1em;
}
75% {
box-shadow: 0em -3em 0 -1em, 2em -2em 0 -1em, 3em 0em 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 0, -3em 0em 0 0.2em, -2em -2em 0 0;
}
87.5% {
box-shadow: 0em -3em 0 0, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 0, -3em 0em 0 0, -2em -2em 0 0.2em;
}
}
@keyframes load4 {
0%,
100% {
box-shadow: 0 -3em 0 0.2em, 2em -2em 0 0em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 0;
}
12.5% {
box-shadow: 0 -3em 0 0, 2em -2em 0 0.2em, 3em 0 0 0, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 -1em;
}
25% {
box-shadow: 0 -3em 0 -0.5em, 2em -2em 0 0, 3em 0 0 0.2em, 2em 2em 0 0, 0 3em 0 -1em, -2em 2em 0 -1em, -3em 0 0 -1em, -2em -2em 0 -1em;
}
37.5% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0em 0 0, 2em 2em 0 0.2em, 0 3em 0 0em, -2em 2em 0 -1em, -3em 0em 0 -1em, -2em -2em 0 -1em;
}
50% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 0em, 0 3em 0 0.2em, -2em 2em 0 0, -3em 0em 0 -1em, -2em -2em 0 -1em;
}
62.5% {
box-shadow: 0 -3em 0 -1em, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 0, -2em 2em 0 0.2em, -3em 0 0 0, -2em -2em 0 -1em;
}
75% {
box-shadow: 0em -3em 0 -1em, 2em -2em 0 -1em, 3em 0em 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 0, -3em 0em 0 0.2em, -2em -2em 0 0;
}
87.5% {
box-shadow: 0em -3em 0 0, 2em -2em 0 -1em, 3em 0 0 -1em, 2em 2em 0 -1em, 0 3em 0 -1em, -2em 2em 0 0, -3em 0em 0 0, -2em -2em 0 0.2em;
}
}</style><style type="text/css">/* This is not a zero-length file! */</style></head><body class="antialiased" data-new-gr-c-s-check-loaded="14.1114.0" data-gr-ext-installed=""><div id="prompt-wrapper"></div><div id="__next"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class="overflow-hidden w-full h-full relative flex z-0"><div class="relative flex h-full max-w-full flex-1 overflow-hidden"><div class="flex h-full max-w-full flex-1 flex-col"><main class="relative h-full w-full transition-width flex flex-col overflow-auto items-stretch flex-1"><div class="flex-1 overflow-hidden"><div class="h-full overflow-auto dark:bg-gray-800"><div class="flex flex-col text-sm dark:bg-gray-800"><div class="flex flex-wrap items-center justify-center gap-1 border-b border-black/10 bg-gray-50 p-3 text-gray-500 dark:border-gray-900/50 dark:bg-gray-700 dark:text-gray-300 sticky top-0 z-50"><div class="flex items-center justify-center gap-1"><span>Shared Chat</span><span class="px-1">•</span></div>Model: Default</div><div class="mx-auto w-full p-4 md:max-w-2xl lg:max-w-xl lg:px-0 xl:max-w-3xl"><div class="mb-1 border-b border-gray-100 pt-3 sm:mb-2 sm:pb-6 sm:pt-8"><h1 class="max-w-md text-3xl font-semibold leading-tight text-gray-700 dark:text-gray-100 sm:text-4xl">ChatGPT_강의_Training</h1><div class="pt-3 text-base text-gray-400 sm:pt-4">July 7, 2023</div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 dark:bg-gray-800"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-[38rem] xl:max-w-3xl md:py-6 lg:px-0 pl-0 pr-4 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(121, 137, 255);"><svg stroke="currentColor" fill="none" stroke-width="1.5" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-6 w-6" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex items-start overflow-x-auto whitespace-pre-wrap break-words flex-col gap-4"><div class="empty:hidden">[Seed Fixed]
===INSTRUCT===
Take on the role of an AI developer in PyTorch and Python and code for me.
===TASK===
For reproducibility, please write code to clamp the randomness of the numpy, pytorch, cuda, and random libraries.
Please write in Korean language.</div></div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 bg-gray-50 dark:bg-[#444654] sm:AIPRM__conversation__response"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-[38rem] xl:max-w-3xl md:py-6 lg:px-0 pl-0 pr-4 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(224, 108, 43);"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" stroke-width="1.5" class="h-6 w-6" role="img"><title>ChatGPT</title><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 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w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex items-start overflow-x-auto whitespace-pre-wrap break-words flex-col gap-4"><div class="markdown prose w-full break-words dark:prose-invert light AIPRM__conversation__response"><p>다음은 주요 라이브러리의 난수 생성을 고정하기 위한 코드입니다. 이 코드를 사용하면 결과의 재현성을 보장할 수 있습니다.</p><pre><div class="bg-black rounded-md mb-4"><div class="flex items-center relative text-gray-200 bg-gray-800 px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>python</span><button class="flex ml-auto gap-2"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-4 w-4" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path><rect x="8" y="2" width="8" height="4" rx="1" ry="1"></rect></svg>Copy code</button></div><div class="p-4 overflow-y-auto"><code class="!whitespace-pre hljs language-python"><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> random
<span class="hljs-keyword">import</span> os
<span class="hljs-comment"># 랜덤 시드 설정</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">fix_seed</span>(<span class="hljs-params">seed</span>):
<span class="hljs-comment"># numpy</span>
np.random.seed(seed)
<span class="hljs-comment"># Torch</span>
torch.manual_seed(seed)
<span class="hljs-comment"># CPU 기반 연산을 위한 Torch</span>
torch.manual_seed(seed)
<span class="hljs-comment"># 멀티 쓰레딩을 위한 Torch</span>
torch.manual_seed(seed)
<span class="hljs-comment"># CUDA 기반 연산을 위한 Torch</span>
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = <span class="hljs-literal">True</span>
torch.backends.cudnn.benchmark = <span class="hljs-literal">False</span>
<span class="hljs-comment"># random</span>
random.seed(seed)
<span class="hljs-comment"># Python의 환경을 위한 seed 설정</span>
os.environ[<span class="hljs-string">'PYTHONHASHSEED'</span>] = <span class="hljs-built_in">str</span>(seed)
<span class="hljs-comment"># 설정을 적용합니다.</span>
fix_seed(<span class="hljs-number">42</span>)
</code></div></div></pre><p>위의 코드는 모든 Python, Numpy, PyTorch, 그리고 CUDA 연산에 대한 랜덤한 요소를 제거합니다. 이렇게 하면 실험의 결과가 재현 가능해집니다. 다만, 코드의 성능이 조금 감소할 수 있습니다. 여기서 <code>seed</code>는 임의로 설정할 수 있으며, 이 값을 변경하면 다른 순서의 "랜덤"한 숫자를 얻을 수 있습니다.</p></div></div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 dark:bg-gray-800"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-[38rem] xl:max-w-3xl md:py-6 lg:px-0 pl-0 pr-4 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(121, 137, 255);"><svg stroke="currentColor" fill="none" stroke-width="1.5" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-6 w-6" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M20 21v-2a4 4 0 0 0-4-4H8a4 4 0 0 0-4 4v2"></path><circle cx="12" cy="7" r="4"></circle></svg></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex items-start overflow-x-auto whitespace-pre-wrap break-words flex-col gap-4"><div class="empty:hidden">[Create Dataset]
===INSTRUCT===
Take on the role of an AI developer in PyTorch and Python and code for me.
Create a dataset class based on a deep learning framework for binary classification tasks, including dicom image processing and augmentation.
This code creates a custom dataset class "RSNA_Dataset" that inherits from PyTorch's "Dataset".
Please create a dataset by reading the csv file ('rsna_data.csv') that includes the image path.
I have already separated the data into 'train' and 'valid' as the 'mode' column of 'rsna_data.csv'.
Define a compose transform function to perform data preprocessing and augmentation using the 'albumentations' library.
Depending on the 'mode', the augmentation is applied differently.
With 'train' mode augmentation, we do Resize(224x224), min_max_normalization, and tensor transformation.
With 'valid' mode augmentation, we do Resize(224x224), min_max_normalization, and tensor transformation.
===INFO===
csv_file = '/content/drive/MyDrive/Med_ChatGPT_tutorial_Dataset/rsna_data.csv'
target_class = 'cancer'
===TASK===
Please proceed step by step according to the following procedure:
1. Read the csv file.
2. In the initial step, filter the data frame according to its 'mode'.
3. Read the file path using the 'path' column of the dataframe, and the label information using the 'cancer' column.
4. Apply a Modality Lookup Table (LUT) to the pixel array in the DICOM file.
5. Apply augmentation according to the 'mode'.
6. Returns the image read and its label.
Please write in Korean language.</div></div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 bg-gray-50 dark:bg-[#444654] sm:AIPRM__conversation__response"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-[38rem] xl:max-w-3xl md:py-6 lg:px-0 pl-0 pr-4 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(224, 108, 43);"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" stroke-width="1.5" class="h-6 w-6" role="img"><title>ChatGPT</title><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 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15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex items-start overflow-x-auto whitespace-pre-wrap break-words flex-col gap-4"><div class="markdown prose w-full break-words dark:prose-invert light AIPRM__conversation__response"><p>다음 코드는 PyTorch의 'Dataset'을 상속받는 사용자 정의 데이터셋 클래스 'RSNA_Dataset'을 생성하는 것입니다. 이 클래스는 이진 분류 작업을 위한 데이터셋을 구축하며, DICOM 이미지 처리 및 Augmentation이 포함되어 있습니다.</p><pre><div class="bg-black rounded-md mb-4"><div class="flex items-center relative text-gray-200 bg-gray-800 px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>python</span><button class="flex ml-auto gap-2"><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" class="h-4 w-4" height="1em" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path><rect x="8" y="2" width="8" height="4" rx="1" ry="1"></rect></svg>Copy code</button></div><div class="p-4 overflow-y-auto"><code class="!whitespace-pre hljs language-python"><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
<span class="hljs-keyword">import</span> pydicom
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> Dataset
<span class="hljs-keyword">from</span> albumentations.pytorch <span class="hljs-keyword">import</span> ToTensorV2
<span class="hljs-keyword">from</span> albumentations <span class="hljs-keyword">import</span> Compose, Resize, Normalize
<span class="hljs-keyword">import</span> albumentations <span class="hljs-keyword">as</span> A
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">class</span> <span class="hljs-title class_">RSNA_Dataset</span>(<span class="hljs-title class_ inherited__">Dataset</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, csv_file, mode, target_class</span>):
<span class="hljs-comment"># csv 파일 읽기</span>
self.dataframe = pd.read_csv(csv_file)
<span class="hljs-comment"># 초기 단계에서 'mode'에 따라 데이터 프레임 필터링</span>
self.dataframe = self.dataframe[self.dataframe[<span class="hljs-string">'mode'</span>] == mode]
<span class="hljs-comment"># 라벨 컬럼명</span>
self.target_class = target_class
<span class="hljs-comment"># 'mode'에 따른 Augmentation 설정</span>
<span class="hljs-keyword">if</span> mode == <span class="hljs-string">'train'</span>:
self.transform = Compose([
Resize(<span class="hljs-number">224</span>, <span class="hljs-number">224</span>),
Normalize(),
ToTensorV2()
])
<span class="hljs-keyword">elif</span> mode == <span class="hljs-string">'valid'</span>:
self.transform = Compose([
Resize(<span class="hljs-number">224</span>, <span class="hljs-number">224</span>),