From 3908c799e04e60754da61640352a6d0fc8bd3206 Mon Sep 17 00:00:00 2001 From: suhasid098 <77293073+suhasid098@users.noreply.github.com> Date: Wed, 8 Jun 2022 18:23:48 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20suhasid0?= =?UTF-8?q?98/tf=5Ffrontend@66e565e622d4bcd75cc55b6654445f79227d62f2=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- assets/index.ae5cb2bb.css | 1 + .../{index.d0d2148b.js => index.c5e8215f.js} | 1986 ++++------------- assets/index.e96ea732.css | 1 - index.html | 4 +- 4 files changed, 484 insertions(+), 1508 deletions(-) create mode 100644 assets/index.ae5cb2bb.css rename assets/{index.d0d2148b.js => index.c5e8215f.js} (69%) delete mode 100644 assets/index.e96ea732.css diff --git a/assets/index.ae5cb2bb.css b/assets/index.ae5cb2bb.css new file mode 100644 index 0000000..736ba9e --- /dev/null +++ b/assets/index.ae5cb2bb.css @@ -0,0 +1 @@ +@charset 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.5);--el-button-hover-text-color:var(--el-color-primary);--el-button-hover-bg-color:var(--el-color-primary-light-9);--el-button-hover-border-color:var(--el-color-primary-light-7);--el-button-active-text-color:var(--el-button-hover-text-color);--el-button-active-border-color:var(--el-color-primary);--el-button-active-bg-color:var(--el-button-hover-bg-color)}.el-button{display:inline-flex;justify-content:center;align-items:center;line-height:1;height:32px;white-space:nowrap;cursor:pointer;background-color:var(--el-button-bg-color);border:var(--el-border);border-color:var(--el-button-border-color);color:var(--el-button-text-color);-webkit-appearance:none;text-align:center;box-sizing:border-box;outline:0;transition:.1s;font-weight:var(--el-button-font-weight);-webkit-user-select:none;user-select:none;vertical-align:middle;padding:8px 15px;font-size:var(--el-font-size-base);border-radius:var(--el-border-radius-base)}.el-button>span{display:inline-flex;align-items:center}.el-button+.el-button{margin-left:12px}.el-button.is-round{padding:8px 15px}.el-button:focus,.el-button:hover{color:var(--el-button-hover-text-color);border-color:var(--el-button-hover-border-color);background-color:var(--el-button-hover-bg-color);outline:0}.el-button:active{color:var(--el-button-active-text-color);border-color:var(--el-button-active-border-color);background-color:var(--el-button-active-bg-color);outline:0}.el-button::-moz-focus-inner{border:0}.el-button [class*=el-icon]+span{margin-left:6px}.el-button [class*=el-icon] svg{vertical-align:bottom}.el-button.is-plain{--el-button-hover-text-color:var(--el-color-primary);--el-button-hover-bg-color:var(--el-fill-color-blank);--el-button-hover-border-color:var(--el-color-primary)}.el-button.is-active{color:var(--el-button-active-text-color);border-color:var(--el-button-active-border-color);background-color:var(--el-button-active-bg-color);outline:0}.el-button.is-disabled,.el-button.is-disabled:focus,.el-button.is-disabled:hover{color:var(--el-button-disabled-text-color);cursor:not-allowed;background-image:none;background-color:var(--el-button-disabled-bg-color);border-color:var(--el-button-disabled-border-color)}.el-button.is-loading{position:relative;pointer-events:none}.el-button.is-loading:before{z-index:1;pointer-events:none;content:"";position:absolute;left:-1px;top:-1px;right:-1px;bottom:-1px;border-radius:inherit;background-color:var(--el-mask-color-extra-light)}.el-button.is-round{border-radius:var(--el-border-radius-round)}.el-button.is-circle{border-radius:50%;padding:8px}.el-button__text--expand{letter-spacing:.3em;margin-right:-.3em}.el-button--primary{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-primary);--el-button-border-color:var(--el-color-primary);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-primary-light-3);--el-button-hover-border-color:var(--el-color-primary-light-3);--el-button-active-bg-color:var(--el-color-primary-dark-2);--el-button-active-border-color:var(--el-color-primary-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-primary-light-5);--el-button-disabled-border-color:var(--el-color-primary-light-5)}.el-button--primary.is-plain{--el-button-text-color:var(--el-color-primary);--el-button-bg-color:var(--el-color-primary-light-9);--el-button-border-color:var(--el-color-primary-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-primary);--el-button-hover-border-color:var(--el-color-primary);--el-button-active-text-color:var(--el-color-white)}.el-button--primary.is-plain.is-disabled,.el-button--primary.is-plain.is-disabled:active,.el-button--primary.is-plain.is-disabled:focus,.el-button--primary.is-plain.is-disabled:hover{color:var(--el-color-primary-light-5);background-color:var(--el-color-primary-light-9);border-color:var(--el-color-primary-light-8)}.el-button--success{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-success);--el-button-border-color:var(--el-color-success);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-success-light-3);--el-button-hover-border-color:var(--el-color-success-light-3);--el-button-active-bg-color:var(--el-color-success-dark-2);--el-button-active-border-color:var(--el-color-success-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-success-light-5);--el-button-disabled-border-color:var(--el-color-success-light-5)}.el-button--success.is-plain{--el-button-text-color:var(--el-color-success);--el-button-bg-color:var(--el-color-success-light-9);--el-button-border-color:var(--el-color-success-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-success);--el-button-hover-border-color:var(--el-color-success);--el-button-active-text-color:var(--el-color-white)}.el-button--success.is-plain.is-disabled,.el-button--success.is-plain.is-disabled:active,.el-button--success.is-plain.is-disabled:focus,.el-button--success.is-plain.is-disabled:hover{color:var(--el-color-success-light-5);background-color:var(--el-color-success-light-9);border-color:var(--el-color-success-light-8)}.el-button--warning{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-warning);--el-button-border-color:var(--el-color-warning);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-warning-light-3);--el-button-hover-border-color:var(--el-color-warning-light-3);--el-button-active-bg-color:var(--el-color-warning-dark-2);--el-button-active-border-color:var(--el-color-warning-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-warning-light-5);--el-button-disabled-border-color:var(--el-color-warning-light-5)}.el-button--warning.is-plain{--el-button-text-color:var(--el-color-warning);--el-button-bg-color:var(--el-color-warning-light-9);--el-button-border-color:var(--el-color-warning-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-warning);--el-button-hover-border-color:var(--el-color-warning);--el-button-active-text-color:var(--el-color-white)}.el-button--warning.is-plain.is-disabled,.el-button--warning.is-plain.is-disabled:active,.el-button--warning.is-plain.is-disabled:focus,.el-button--warning.is-plain.is-disabled:hover{color:var(--el-color-warning-light-5);background-color:var(--el-color-warning-light-9);border-color:var(--el-color-warning-light-8)}.el-button--danger{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-danger);--el-button-border-color:var(--el-color-danger);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-danger-light-3);--el-button-hover-border-color:var(--el-color-danger-light-3);--el-button-active-bg-color:var(--el-color-danger-dark-2);--el-button-active-border-color:var(--el-color-danger-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-danger-light-5);--el-button-disabled-border-color:var(--el-color-danger-light-5)}.el-button--danger.is-plain{--el-button-text-color:var(--el-color-danger);--el-button-bg-color:var(--el-color-danger-light-9);--el-button-border-color:var(--el-color-danger-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-danger);--el-button-hover-border-color:var(--el-color-danger);--el-button-active-text-color:var(--el-color-white)}.el-button--danger.is-plain.is-disabled,.el-button--danger.is-plain.is-disabled:active,.el-button--danger.is-plain.is-disabled:focus,.el-button--danger.is-plain.is-disabled:hover{color:var(--el-color-danger-light-5);background-color:var(--el-color-danger-light-9);border-color:var(--el-color-danger-light-8)}.el-button--info{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-info);--el-button-border-color:var(--el-color-info);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-info-light-3);--el-button-hover-border-color:var(--el-color-info-light-3);--el-button-active-bg-color:var(--el-color-info-dark-2);--el-button-active-border-color:var(--el-color-info-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-info-light-5);--el-button-disabled-border-color:var(--el-color-info-light-5)}.el-button--info.is-plain{--el-button-text-color:var(--el-color-info);--el-button-bg-color:var(--el-color-info-light-9);--el-button-border-color:var(--el-color-info-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-info);--el-button-hover-border-color:var(--el-color-info);--el-button-active-text-color:var(--el-color-white)}.el-button--info.is-plain.is-disabled,.el-button--info.is-plain.is-disabled:active,.el-button--info.is-plain.is-disabled:focus,.el-button--info.is-plain.is-disabled:hover{color:var(--el-color-info-light-5);background-color:var(--el-color-info-light-9);border-color:var(--el-color-info-light-8)}.el-button--large{--el-button-size:40px;height:var(--el-button-size);padding:12px 19px;font-size:var(--el-font-size-base);border-radius:var(--el-border-radius-base)}.el-button--large [class*=el-icon]+span{margin-left:8px}.el-button--large.is-round{padding:12px 19px}.el-button--large.is-circle{width:var(--el-button-size);padding:12px}.el-button--small{--el-button-size:24px;height:var(--el-button-size);padding:5px 11px;font-size:12px;border-radius:calc(var(--el-border-radius-base) - 1px)}.el-button--small [class*=el-icon]+span{margin-left:4px}.el-button--small.is-round{padding:5px 11px}.el-button--small.is-circle{width:var(--el-button-size);padding:5px}.el-button--text{border-color:transparent;color:var(--el-color-primary);background:0 0;padding-left:0;padding-right:0}.el-button--text:focus,.el-button--text:hover{color:var(--el-color-primary-light-3);border-color:transparent;background-color:transparent}.el-button--text:active{color:var(--el-color-primary-dark-2);border-color:transparent;background-color:transparent}.el-button--text.is-disabled,.el-button--text.is-disabled:focus,.el-button--text.is-disabled:hover{border-color:transparent}.el-calendar{--el-calendar-border:var(--el-table-border, 1px solid var(--el-border-color-lighter));--el-calendar-header-border-bottom:var(--el-calendar-border);--el-calendar-selected-bg-color:var(--el-color-primary-light-9);--el-calendar-cell-width:85px;background-color:var(--el-fill-color-blank)}.el-calendar__header{display:flex;justify-content:space-between;padding:12px 20px;border-bottom:var(--el-calendar-header-border-bottom)}.el-calendar__title{color:var(--el-text-color);align-self:center}.el-calendar__body{padding:12px 20px 35px}.el-calendar-table{table-layout:fixed;width:100%}.el-calendar-table thead th{padding:12px 0;color:var(--el-text-color-regular);font-weight:400}.el-calendar-table:not(.is-range) td.next,.el-calendar-table:not(.is-range) td.prev{color:var(--el-text-color-placeholder)}.el-calendar-table td{border-bottom:var(--el-calendar-border);border-right:var(--el-calendar-border);vertical-align:top;transition:background-color var(--el-transition-duration-fast) ease}.el-calendar-table td.is-selected{background-color:var(--el-calendar-selected-bg-color)}.el-calendar-table td.is-today{color:var(--el-color-primary)}.el-calendar-table tr:first-child td{border-top:var(--el-calendar-border)}.el-calendar-table tr td:first-child{border-left:var(--el-calendar-border)}.el-calendar-table tr.el-calendar-table__row--hide-border td{border-top:none}.el-calendar-table .el-calendar-day{box-sizing:border-box;padding:8px;height:var(--el-calendar-cell-width)}.el-calendar-table 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i{cursor:pointer}.el-carousel__indicators{position:absolute;list-style:none;margin:0;padding:0;z-index:calc(var(--el-index-normal) + 1)}.el-carousel__indicators--horizontal{bottom:0;left:50%;transform:translate(-50%)}.el-carousel__indicators--vertical{right:0;top:50%;transform:translateY(-50%)}.el-carousel__indicators--outside{bottom:calc(var(--el-carousel-indicator-height) + var(--el-carousel-indicator-padding-vertical) * 2);text-align:center;position:static;transform:none}.el-carousel__indicators--outside .el-carousel__indicator:hover button{opacity:.64}.el-carousel__indicators--outside button{background-color:var(--el-carousel-indicator-out-color);opacity:.24}.el-carousel__indicators--labels{left:0;right:0;transform:none;text-align:center}.el-carousel__indicators--labels .el-carousel__button{height:auto;width:auto;padding:2px 18px;font-size:12px}.el-carousel__indicators--labels .el-carousel__indicator{padding:6px 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translate(-10px);opacity:0}.carousel-arrow-right-enter-from,.carousel-arrow-right-leave-active{transform:translateY(-50%) translate(10px);opacity:0}.el-cascader-panel{--el-cascader-menu-text-color:var(--el-text-color-regular);--el-cascader-menu-selected-text-color:var(--el-color-primary);--el-cascader-menu-fill:var(--el-bg-color-overlay);--el-cascader-menu-font-size:var(--el-font-size-base);--el-cascader-menu-radius:var(--el-border-radius-base);--el-cascader-menu-border:solid 1px var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color)}.el-cascader-panel{display:flex;border-radius:var(--el-cascader-menu-radius);font-size:var(--el-cascader-menu-font-size)}.el-cascader-panel.is-bordered{border:var(--el-cascader-menu-border);border-radius:var(--el-cascader-menu-radius)}.el-cascader-menu{min-width:180px;box-sizing:border-box;color:var(--el-cascader-menu-text-color);border-right:var(--el-cascader-menu-border)}.el-cascader-menu:last-child{border-right:none}.el-cascader-menu:last-child .el-cascader-node{padding-right:20px}.el-cascader-menu__wrap.el-scrollbar__wrap{height:204px}.el-cascader-menu__list{position:relative;min-height:100%;margin:0;padding:6px 0;list-style:none;box-sizing:border-box}.el-cascader-menu__hover-zone{position:absolute;top:0;left:0;width:100%;height:100%;pointer-events:none}.el-cascader-menu__empty-text{position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);display:flex;align-items:center;color:var(--el-cascader-color-empty)}.el-cascader-menu__empty-text .is-loading{margin-right:2px}.el-cascader-node{position:relative;display:flex;align-items:center;padding:0 30px 0 20px;height:34px;line-height:34px;outline:0}.el-cascader-node.is-selectable.in-active-path{color:var(--el-cascader-menu-text-color)}.el-cascader-node.in-active-path,.el-cascader-node.is-active,.el-cascader-node.is-selectable.in-checked-path{color:var(--el-cascader-menu-selected-text-color);font-weight:700}.el-cascader-node:not(.is-disabled){cursor:pointer}.el-cascader-node:not(.is-disabled):focus,.el-cascader-node:not(.is-disabled):hover{background:var(--el-cascader-node-background-hover)}.el-cascader-node.is-disabled{color:var(--el-cascader-node-color-disabled);cursor:not-allowed}.el-cascader-node__prefix{position:absolute;left:10px}.el-cascader-node__postfix{position:absolute;right:10px}.el-cascader-node__label{flex:1;text-align:left;padding:0 8px;white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.el-cascader-node>.el-radio{margin-right:0}.el-cascader-node>.el-radio .el-radio__label{padding-left:0}.el-cascader{--el-cascader-menu-text-color:var(--el-text-color-regular);--el-cascader-menu-selected-text-color:var(--el-color-primary);--el-cascader-menu-fill:var(--el-bg-color-overlay);--el-cascader-menu-font-size:var(--el-font-size-base);--el-cascader-menu-radius:var(--el-border-radius-base);--el-cascader-menu-border:solid 1px var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color);display:inline-block;position:relative;font-size:var(--el-font-size-base);line-height:32px;outline:0}.el-cascader:not(.is-disabled):hover .el-input__inner{cursor:pointer;box-shadow:0 0 0 1px var(--el-input-hover-border-color) inset}.el-cascader .el-input{cursor:pointer}.el-cascader .el-input .el-input__inner{text-overflow:ellipsis}.el-cascader .el-input .el-input__inner:focus{box-shadow:0 0 0 1px var(--el-input-focus-border-color,var(--el-color-primary)) inset}.el-cascader .el-input .el-input__suffix-inner .el-icon{height:calc(100% - 2px)}.el-cascader .el-input .el-input__suffix-inner .el-icon svg{vertical-align:middle}.el-cascader .el-input .icon-arrow-down{transition:transform var(--el-transition-duration);font-size:14px}.el-cascader .el-input .icon-arrow-down.is-reverse{transform:rotate(180deg)}.el-cascader .el-input .icon-circle-close:hover{color:var(--el-input-clear-hover-color,var(--el-text-color-secondary))}.el-cascader .el-input.is-focus .el-input__inner{box-shadow:0 0 0 1px var(--el-input-focus-border-color,var(--el-color-primary)) inset}.el-cascader--large{font-size:14px;line-height:40px}.el-cascader--small{font-size:12px;line-height:24px}.el-cascader.is-disabled .el-cascader__label{z-index:calc(var(--el-index-normal) + 1);color:var(--el-disabled-text-color)}.el-cascader__dropdown{--el-cascader-menu-text-color:var(--el-text-color-regular);--el-cascader-menu-selected-text-color:var(--el-color-primary);--el-cascader-menu-fill:var(--el-bg-color-overlay);--el-cascader-menu-font-size:var(--el-font-size-base);--el-cascader-menu-radius:var(--el-border-radius-base);--el-cascader-menu-border:solid 1px var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color)}.el-cascader__dropdown{font-size:var(--el-cascader-menu-font-size);border-radius:var(--el-cascader-menu-radius)}.el-cascader__dropdown.el-popper[role=tooltip]{background:var(--el-cascader-menu-fill);border:var(--el-cascader-menu-border);box-shadow:var(--el-cascader-menu-shadow)}.el-cascader__dropdown.el-popper[role=tooltip] .el-popper__arrow:before{border:var(--el-cascader-menu-border)}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent;border-left-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent;border-right-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent;border-bottom-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent;border-top-color:transparent}.el-cascader__dropdown.el-popper{box-shadow:var(--el-cascader-menu-shadow)}.el-cascader__tags{position:absolute;left:0;right:30px;top:50%;transform:translateY(-50%);display:flex;flex-wrap:wrap;line-height:normal;text-align:left;box-sizing:border-box}.el-cascader__tags .el-tag{display:inline-flex;align-items:center;max-width:100%;margin:2px 0 2px 6px;text-overflow:ellipsis;background:var(--el-cascader-tag-background)}.el-cascader__tags .el-tag:not(.is-hit){border-color:transparent}.el-cascader__tags .el-tag>span{flex:1;overflow:hidden;text-overflow:ellipsis}.el-cascader__tags .el-tag .el-icon-close{flex:none;background-color:var(--el-text-color-placeholder);color:var(--el-color-white)}.el-cascader__tags .el-tag .el-icon-close:hover{background-color:var(--el-text-color-secondary)}.el-cascader__collapse-tags{white-space:normal;z-index:var(--el-index-normal);display:flex;align-items:center;flex-wrap:wrap}.el-cascader__collapse-tag{line-height:inherit;height:inherit;display:flex}.el-cascader__suggestion-panel{border-radius:var(--el-cascader-menu-radius)}.el-cascader__suggestion-list{max-height:204px;margin:0;padding:6px 0;font-size:var(--el-font-size-base);color:var(--el-cascader-menu-text-color);text-align:center}.el-cascader__suggestion-item{display:flex;justify-content:space-between;align-items:center;height:34px;padding:0 15px;text-align:left;outline:0;cursor:pointer}.el-cascader__suggestion-item:focus,.el-cascader__suggestion-item:hover{background:var(--el-cascader-node-background-hover)}.el-cascader__suggestion-item.is-checked{color:var(--el-cascader-menu-selected-text-color);font-weight:700}.el-cascader__suggestion-item>span{margin-right:10px}.el-cascader__empty-text{margin:10px 0;color:var(--el-cascader-color-empty)}.el-cascader__search-input{flex:1;height:24px;min-width:60px;margin:2px 0 2px 11px;padding:0;color:var(--el-cascader-menu-text-color);border:none;outline:0;box-sizing:border-box}.el-cascader__search-input::placeholder{color:var(--el-text-color-placeholder)}.el-check-tag{background-color:var(--el-color-info-light-9);border-radius:var(--el-border-radius-base);color:var(--el-color-info);cursor:pointer;display:inline-block;font-size:var(--el-font-size-base);line-height:var(--el-font-size-base);padding:7px 15px;transition:var(--el-transition-all);font-weight:700}.el-check-tag:hover{background-color:var(--el-color-info-light-7)}.el-check-tag.is-checked{background-color:var(--el-color-primary-light-8);color:var(--el-color-primary-light-1)}.el-check-tag.is-checked:hover{background-color:var(--el-color-primary-light-7)}.el-checkbox-button{--el-checkbox-button-checked-bg-color:var(--el-color-primary);--el-checkbox-button-checked-text-color:var(--el-color-white);--el-checkbox-button-checked-border-color:var(--el-color-primary)}.el-checkbox-button{position:relative;display:inline-block}.el-checkbox-button__inner{display:inline-block;line-height:1;font-weight:var(--el-checkbox-font-weight);white-space:nowrap;vertical-align:middle;cursor:pointer;background:var(--el-button-bg-color,var(--el-fill-color-blank));border:var(--el-border);border-left:0;color:var(--el-button-text-color,var(--el-text-color-regular));-webkit-appearance:none;text-align:center;box-sizing:border-box;outline:0;margin:0;position:relative;transition:var(--el-transition-all);-webkit-user-select:none;user-select:none;padding:8px 15px;font-size:var(--el-font-size-base);border-radius:0}.el-checkbox-button__inner.is-round{padding:8px 15px}.el-checkbox-button__inner:hover{color:var(--el-color-primary)}.el-checkbox-button__inner [class*=el-icon-]{line-height:.9}.el-checkbox-button__inner [class*=el-icon-]+span{margin-left:5px}.el-checkbox-button__original{opacity:0;outline:0;position:absolute;margin:0;z-index:-1}.el-checkbox-button.is-checked .el-checkbox-button__inner{color:var(--el-checkbox-button-checked-text-color);background-color:var(--el-checkbox-button-checked-bg-color);border-color:var(--el-checkbox-button-checked-border-color);box-shadow:-1px 0 0 0 var(--el-color-primary-light-7)}.el-checkbox-button.is-checked:first-child .el-checkbox-button__inner{border-left-color:var(--el-checkbox-button-checked-border-color)}.el-checkbox-button.is-disabled .el-checkbox-button__inner{color:var(--el-disabled-text-color);cursor:not-allowed;background-image:none;background-color:var(--el-button-disabled-bg-color,var(--el-fill-color-blank));border-color:var(--el-button-disabled-border-color,var(--el-border-color-light));box-shadow:none}.el-checkbox-button.is-disabled:first-child .el-checkbox-button__inner{border-left-color:var(--el-button-disabled-border-color,var(--el-border-color-light))}.el-checkbox-button:first-child .el-checkbox-button__inner{border-left:var(--el-border);border-radius:var(--el-border-radius-base) 0 0 var(--el-border-radius-base);box-shadow:none!important}.el-checkbox-button.is-focus .el-checkbox-button__inner{border-color:var(--el-checkbox-button-checked-border-color)}.el-checkbox-button:last-child .el-checkbox-button__inner{border-radius:0 var(--el-border-radius-base) var(--el-border-radius-base) 0}.el-checkbox-button--large .el-checkbox-button__inner{padding:12px 19px;font-size:var(--el-font-size-base);border-radius:0}.el-checkbox-button--large .el-checkbox-button__inner.is-round{padding:12px 19px}.el-checkbox-button--small .el-checkbox-button__inner{padding:5px 11px;font-size:12px;border-radius:0}.el-checkbox-button--small .el-checkbox-button__inner.is-round{padding:5px 11px}.el-checkbox-group{font-size:0;line-height:0}.el-checkbox{--el-checkbox-font-size:14px;--el-checkbox-font-weight:var(--el-font-weight-primary);--el-checkbox-text-color:var(--el-text-color-regular);--el-checkbox-input-height:14px;--el-checkbox-input-width:14px;--el-checkbox-border-radius:var(--el-border-radius-small);--el-checkbox-bg-color:var(--el-fill-color-blank);--el-checkbox-input-border:var(--el-border);--el-checkbox-disabled-border-color:var(--el-border-color);--el-checkbox-disabled-input-fill:var(--el-fill-color-light);--el-checkbox-disabled-icon-color:var(--el-text-color-placeholder);--el-checkbox-disabled-checked-input-fill:var(--el-border-color-extra-light);--el-checkbox-disabled-checked-input-border-color:var(--el-border-color);--el-checkbox-disabled-checked-icon-color:var(--el-text-color-placeholder);--el-checkbox-checked-text-color:var(--el-color-primary);--el-checkbox-checked-input-border-color:var(--el-color-primary);--el-checkbox-checked-bg-color:var(--el-color-primary);--el-checkbox-checked-icon-color:var(--el-color-white);--el-checkbox-input-border-color-hover:var(--el-color-primary)}.el-checkbox{color:var(--el-checkbox-text-color);font-weight:var(--el-checkbox-font-weight);font-size:var(--el-font-size-base);position:relative;cursor:pointer;display:inline-flex;align-items:center;white-space:nowrap;-webkit-user-select:none;user-select:none;margin-right:30px;height:32px}.el-checkbox.is-bordered{padding:0 15px 0 9px;border-radius:var(--el-border-radius-base);border:var(--el-border);box-sizing:border-box}.el-checkbox.is-bordered.is-checked{border-color:var(--el-color-primary)}.el-checkbox.is-bordered.is-disabled{border-color:var(--el-border-color-lighter);cursor:not-allowed}.el-checkbox.is-bordered.el-checkbox--large{padding:0 19px 0 11px;border-radius:var(--el-border-radius-base)}.el-checkbox.is-bordered.el-checkbox--large .el-checkbox__label{font-size:var(--el-font-size-base)}.el-checkbox.is-bordered.el-checkbox--large .el-checkbox__inner{height:14px;width:14px}.el-checkbox.is-bordered.el-checkbox--small{padding:0 11px 0 7px;border-radius:calc(var(--el-border-radius-base) - 1px)}.el-checkbox.is-bordered.el-checkbox--small .el-checkbox__label{font-size:12px}.el-checkbox.is-bordered.el-checkbox--small .el-checkbox__inner{height:12px;width:12px}.el-checkbox.is-bordered.el-checkbox--small .el-checkbox__inner:after{height:6px;width:2px}.el-checkbox__input{white-space:nowrap;cursor:pointer;outline:0;display:inline-flex;position:relative}.el-checkbox__input.is-disabled .el-checkbox__inner{background-color:var(--el-checkbox-disabled-input-fill);border-color:var(--el-checkbox-disabled-border-color);cursor:not-allowed}.el-checkbox__input.is-disabled .el-checkbox__inner:after{cursor:not-allowed;border-color:var(--el-checkbox-disabled-icon-color)}.el-checkbox__input.is-disabled .el-checkbox__inner+.el-checkbox__label{cursor:not-allowed}.el-checkbox__input.is-disabled.is-checked .el-checkbox__inner{background-color:var(--el-checkbox-disabled-checked-input-fill);border-color:var(--el-checkbox-disabled-checked-input-border-color)}.el-checkbox__input.is-disabled.is-checked .el-checkbox__inner:after{border-color:var(--el-checkbox-disabled-checked-icon-color)}.el-checkbox__input.is-disabled.is-indeterminate .el-checkbox__inner{background-color:var(--el-checkbox-disabled-checked-input-fill);border-color:var(--el-checkbox-disabled-checked-input-border-color)}.el-checkbox__input.is-disabled.is-indeterminate .el-checkbox__inner:before{background-color:var(--el-checkbox-disabled-checked-icon-color);border-color:var(--el-checkbox-disabled-checked-icon-color)}.el-checkbox__input.is-disabled+span.el-checkbox__label{color:var(--el-disabled-text-color);cursor:not-allowed}.el-checkbox__input.is-checked .el-checkbox__inner{background-color:var(--el-checkbox-checked-bg-color);border-color:var(--el-checkbox-checked-input-border-color)}.el-checkbox__input.is-checked .el-checkbox__inner:after{transform:rotate(45deg) scaleY(1)}.el-checkbox__input.is-checked+.el-checkbox__label{color:var(--el-checkbox-checked-text-color)}.el-checkbox__input.is-focus .el-checkbox__inner{border-color:var(--el-checkbox-input-border-color-hover)}.el-checkbox__input.is-indeterminate .el-checkbox__inner{background-color:var(--el-checkbox-checked-bg-color);border-color:var(--el-checkbox-checked-input-border-color)}.el-checkbox__input.is-indeterminate .el-checkbox__inner:before{content:"";position:absolute;display:block;background-color:var(--el-checkbox-checked-icon-color);height:2px;transform:scale(.5);left:0;right:0;top:5px}.el-checkbox__input.is-indeterminate .el-checkbox__inner:after{display:none}.el-checkbox__inner{display:inline-block;position:relative;border:var(--el-checkbox-input-border);border-radius:var(--el-checkbox-border-radius);box-sizing:border-box;width:var(--el-checkbox-input-width);height:var(--el-checkbox-input-height);background-color:var(--el-checkbox-bg-color);z-index:var(--el-index-normal);transition:border-color .25s cubic-bezier(.71,-.46,.29,1.46),background-color .25s cubic-bezier(.71,-.46,.29,1.46)}.el-checkbox__inner:hover{border-color:var(--el-checkbox-input-border-color-hover)}.el-checkbox__inner:after{box-sizing:content-box;content:"";border:1px solid var(--el-checkbox-checked-icon-color);border-left:0;border-top:0;height:7px;left:4px;position:absolute;top:1px;transform:rotate(45deg) scaleY(0);width:3px;transition:transform .15s ease-in 50ms;transform-origin:center}.el-checkbox__original{opacity:0;outline:0;position:absolute;margin:0;width:0;height:0;z-index:-1}.el-checkbox__label{display:inline-block;padding-left:8px;line-height:1;font-size:var(--el-checkbox-font-size)}.el-checkbox.el-checkbox--large{height:40px}.el-checkbox.el-checkbox--large .el-checkbox__label{font-size:14px}.el-checkbox.el-checkbox--large .el-checkbox__inner{width:14px;height:14px}.el-checkbox.el-checkbox--small{height:24px}.el-checkbox.el-checkbox--small .el-checkbox__label{font-size:12px}.el-checkbox.el-checkbox--small 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solid var(--el-collapse-border-color)}.el-collapse-item__content{padding-bottom:25px;font-size:var(--el-collapse-content-font-size);color:var(--el-collapse-content-text-color);line-height:1.7692307692}.el-collapse-item:last-child{margin-bottom:-1px}.el-color-predefine{display:flex;font-size:12px;margin-top:8px;width:280px}.el-color-predefine__colors{display:flex;flex:1;flex-wrap:wrap}.el-color-predefine__color-selector{margin:0 0 8px 8px;width:20px;height:20px;border-radius:4px;cursor:pointer}.el-color-predefine__color-selector:nth-child(10n+1){margin-left:0}.el-color-predefine__color-selector.selected{box-shadow:0 0 3px 2px var(--el-color-primary)}.el-color-predefine__color-selector>div{display:flex;height:100%;border-radius:3px}.el-color-predefine__color-selector.is-alpha{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAIAAADZF8uwAAAAGUlEQVQYV2M4gwH+YwCGIasIUwhT25BVBADtzYNYrHvv4gAAAABJRU5ErkJggg==)}.el-color-hue-slider{position:relative;box-sizing:border-box;width:280px;height:12px;background-color:red;padding:0 2px;float:right}.el-color-hue-slider__bar{position:relative;background:linear-gradient(to right,red 0,#ff0 17%,#0f0 33%,#0ff 50%,#00f 67%,#f0f 83%,red 100%);height:100%}.el-color-hue-slider__thumb{position:absolute;cursor:pointer;box-sizing:border-box;left:0;top:0;width:4px;height:100%;border-radius:1px;background:#fff;border:1px solid var(--el-border-color-lighter);box-shadow:0 0 2px #0009;z-index:1}.el-color-hue-slider.is-vertical{width:12px;height:180px;padding:2px 0}.el-color-hue-slider.is-vertical .el-color-hue-slider__bar{background:linear-gradient(to right,red 0,#ff0 17%,#0f0 33%,#0ff 50%,#00f 67%,#f0f 83%,red 100%)}.el-color-hue-slider.is-vertical .el-color-hue-slider__thumb{left:0;top:0;width:100%;height:4px}.el-color-svpanel{position:relative;width:280px;height:180px}.el-color-svpanel__black,.el-color-svpanel__white{position:absolute;top:0;left:0;right:0;bottom:0}.el-color-svpanel__white{background:linear-gradient(to right,#fff,rgba(255,255,255,0))}.el-color-svpanel__black{background:linear-gradient(to top,#000,rgba(0,0,0,0))}.el-color-svpanel__cursor{position:absolute}.el-color-svpanel__cursor>div{cursor:head;width:4px;height:4px;box-shadow:0 0 0 1.5px #fff,inset 0 0 1px 1px #0000004d,0 0 1px 2px #0006;border-radius:50%;transform:translate(-2px,-2px)}.el-color-alpha-slider{position:relative;box-sizing:border-box;width:280px;height:12px;background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAIAAADZF8uwAAAAGUlEQVQYV2M4gwH+YwCGIasIUwhT25BVBADtzYNYrHvv4gAAAABJRU5ErkJggg==)}.el-color-alpha-slider__bar{position:relative;background:linear-gradient(to right,rgba(255,255,255,0) 0,#fff 100%);height:100%}.el-color-alpha-slider__thumb{position:absolute;cursor:pointer;box-sizing:border-box;left:0;top:0;width:4px;height:100%;border-radius:1px;background:#fff;border:1px solid var(--el-border-color-lighter);box-shadow:0 0 2px #0009;z-index:1}.el-color-alpha-slider.is-vertical{width:20px;height:180px}.el-color-alpha-slider.is-vertical .el-color-alpha-slider__bar{background:linear-gradient(to bottom,rgba(255,255,255,0) 0,#fff 100%)}.el-color-alpha-slider.is-vertical .el-color-alpha-slider__thumb{left:0;top:0;width:100%;height:4px}.el-color-dropdown{width:300px}.el-color-dropdown__main-wrapper{margin-bottom:6px}.el-color-dropdown__main-wrapper:after{content:"";display:table;clear:both}.el-color-dropdown__btns{margin-top:12px;text-align:right}.el-color-dropdown__value{float:left;line-height:26px;font-size:12px;color:#000;width:160px}.el-color-picker{display:inline-block;position:relative;line-height:normal}.el-color-picker.is-disabled .el-color-picker__trigger{cursor:not-allowed}.el-color-picker--large{height:40px}.el-color-picker--large .el-color-picker__trigger{height:40px;width:40px}.el-color-picker--large .el-color-picker__mask{height:38px;width:38px}.el-color-picker--small{height:24px}.el-color-picker--small .el-color-picker__trigger{height:24px;width:24px}.el-color-picker--small .el-color-picker__mask{height:22px;width:22px}.el-color-picker--small .el-color-picker__empty,.el-color-picker--small .el-color-picker__icon{transform:scale(.8)}.el-color-picker__mask{height:38px;width:38px;border-radius:4px;position:absolute;top:1px;left:1px;z-index:1;cursor:not-allowed;background-color:#ffffffb3}.el-color-picker__trigger{display:inline-flex;justify-content:center;align-items:center;box-sizing:border-box;height:32px;width:32px;padding:4px;border:1px solid var(--el-border-color);border-radius:4px;font-size:0;position:relative;cursor:pointer}.el-color-picker__color{position:relative;display:block;box-sizing:border-box;border:1px solid var(--el-text-color-secondary);border-radius:var(--el-border-radius-small);width:100%;height:100%;text-align:center}.el-color-picker__color.is-alpha{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAIAAADZF8uwAAAAGUlEQVQYV2M4gwH+YwCGIasIUwhT25BVBADtzYNYrHvv4gAAAABJRU5ErkJggg==)}.el-color-picker__color-inner{display:inline-flex;justify-content:center;align-items:center;width:100%;height:100%}.el-color-picker .el-color-picker__empty{font-size:12px;color:var(--el-text-color-secondary)}.el-color-picker .el-color-picker__icon{display:inline-flex;justify-content:center;align-items:center;color:#fff;font-size:12px}.el-color-picker__panel{position:absolute;z-index:10;padding:6px;box-sizing:content-box;background-color:#fff;border-radius:var(--el-border-radius-base);box-shadow:var(--el-box-shadow-light)}.el-color-picker__panel.el-popper{border:1px solid var(--el-border-color-lighter)}.el-container{display:flex;flex-direction:row;flex:1;flex-basis:auto;box-sizing:border-box;min-width:0}.el-container.is-vertical{flex-direction:column}.el-date-table{font-size:12px;-webkit-user-select:none;user-select:none}.el-date-table.is-week-mode .el-date-table__row:hover .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table.is-week-mode .el-date-table__row:hover td.available:hover{color:var(--el-datepicker-text-color)}.el-date-table.is-week-mode .el-date-table__row:hover td:first-child .el-date-table-cell{margin-left:5px;border-top-left-radius:15px;border-bottom-left-radius:15px}.el-date-table.is-week-mode .el-date-table__row:hover td:last-child .el-date-table-cell{margin-right:5px;border-top-right-radius:15px;border-bottom-right-radius:15px}.el-date-table.is-week-mode .el-date-table__row.current .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table td{width:32px;height:30px;padding:4px 0;box-sizing:border-box;text-align:center;cursor:pointer;position:relative}.el-date-table td .el-date-table-cell{height:30px;padding:3px 0;box-sizing:border-box}.el-date-table td .el-date-table-cell .el-date-table-cell__text{width:24px;height:24px;display:block;margin:0 auto;line-height:24px;position:absolute;left:50%;transform:translate(-50%);border-radius:50%}.el-date-table td.next-month,.el-date-table td.prev-month{color:var(--el-datepicker-off-text-color)}.el-date-table td.today{position:relative}.el-date-table td.today .el-date-table-cell__text{color:var(--el-color-primary);font-weight:700}.el-date-table td.today.end-date .el-date-table-cell__text,.el-date-table td.today.start-date .el-date-table-cell__text{color:#fff}.el-date-table td.available:hover{color:var(--el-datepicker-hover-text-color)}.el-date-table td.in-range .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table td.in-range .el-date-table-cell:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-date-table td.current:not(.disabled) .el-date-table-cell__text{color:#fff;background-color:var(--el-datepicker-active-color)}.el-date-table td.end-date .el-date-table-cell,.el-date-table td.start-date .el-date-table-cell{color:#fff}.el-date-table td.end-date .el-date-table-cell__text,.el-date-table td.start-date .el-date-table-cell__text{background-color:var(--el-datepicker-active-color)}.el-date-table td.start-date .el-date-table-cell{margin-left:5px;border-top-left-radius:15px;border-bottom-left-radius:15px}.el-date-table td.end-date .el-date-table-cell{margin-right:5px;border-top-right-radius:15px;border-bottom-right-radius:15px}.el-date-table td.disabled .el-date-table-cell{background-color:var(--el-fill-color-light);opacity:1;cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-date-table td.selected .el-date-table-cell{margin-left:5px;margin-right:5px;background-color:var(--el-datepicker-inrange-bg-color);border-radius:15px}.el-date-table td.selected .el-date-table-cell:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-date-table td.selected .el-date-table-cell__text{background-color:var(--el-datepicker-active-color);color:#fff;border-radius:15px}.el-date-table td.week{font-size:80%;color:var(--el-datepicker-header-text-color)}.el-date-table th{padding:5px;color:var(--el-datepicker-header-text-color);font-weight:400;border-bottom:solid 1px var(--el-border-color-lighter)}.el-month-table{font-size:12px;margin:-1px;border-collapse:collapse}.el-month-table td{text-align:center;padding:8px 0;cursor:pointer}.el-month-table td div{height:48px;padding:6px 0;box-sizing:border-box}.el-month-table td.today .cell{color:var(--el-color-primary);font-weight:700}.el-month-table td.today.end-date .cell,.el-month-table td.today.start-date .cell{color:#fff}.el-month-table td.disabled .cell{background-color:var(--el-fill-color-light);cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-month-table td.disabled .cell:hover{color:var(--el-text-color-placeholder)}.el-month-table td .cell{width:60px;height:36px;display:block;line-height:36px;color:var(--el-datepicker-text-color);margin:0 auto;border-radius:18px}.el-month-table td .cell:hover{color:var(--el-datepicker-hover-text-color)}.el-month-table td.in-range div{background-color:var(--el-datepicker-inrange-bg-color)}.el-month-table td.in-range div:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-month-table td.end-date div,.el-month-table td.start-date div{color:#fff}.el-month-table td.end-date .cell,.el-month-table td.start-date .cell{color:#fff;background-color:var(--el-datepicker-active-color)}.el-month-table td.start-date div{border-top-left-radius:24px;border-bottom-left-radius:24px}.el-month-table td.end-date div{border-top-right-radius:24px;border-bottom-right-radius:24px}.el-month-table td.current:not(.disabled) .cell{color:var(--el-datepicker-active-color)}.el-year-table{font-size:12px;margin:-1px;border-collapse:collapse}.el-year-table .el-icon{color:var(--el-datepicker-icon-color)}.el-year-table td{text-align:center;padding:20px 3px;cursor:pointer}.el-year-table td.today .cell{color:var(--el-color-primary);font-weight:700}.el-year-table td.disabled .cell{background-color:var(--el-fill-color-light);cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-year-table td.disabled .cell:hover{color:var(--el-text-color-placeholder)}.el-year-table td .cell{width:48px;height:32px;display:block;line-height:32px;color:var(--el-datepicker-text-color);margin:0 auto}.el-year-table td .cell:hover{color:var(--el-datepicker-hover-text-color)}.el-year-table td.current:not(.disabled) .cell{color:var(--el-datepicker-active-color)}.el-time-spinner.has-seconds .el-time-spinner__wrapper{width:33.3%}.el-time-spinner__wrapper{max-height:192px;overflow:auto;display:inline-block;width:50%;vertical-align:top;position:relative}.el-time-spinner__wrapper.el-scrollbar__wrap:not(.el-scrollbar__wrap--hidden-default){padding-bottom:15px}.el-time-spinner__wrapper.is-arrow{box-sizing:border-box;text-align:center;overflow:hidden}.el-time-spinner__wrapper.is-arrow .el-time-spinner__list{transform:translateY(-32px)}.el-time-spinner__wrapper.is-arrow .el-time-spinner__item:hover:not(.is-disabled):not(.is-active){background:#fff;cursor:default}.el-time-spinner__arrow{font-size:12px;color:var(--el-text-color-secondary);position:absolute;left:0;width:100%;z-index:var(--el-index-normal);text-align:center;height:30px;line-height:30px;cursor:pointer}.el-time-spinner__arrow:hover{color:var(--el-color-primary)}.el-time-spinner__arrow.arrow-up{top:10px}.el-time-spinner__arrow.arrow-down{bottom:10px}.el-time-spinner__input.el-input{width:70%}.el-time-spinner__input.el-input .el-input__inner{padding:0;text-align:center}.el-time-spinner__list{padding:0;margin:0;list-style:none;text-align:center}.el-time-spinner__list:after,.el-time-spinner__list:before{content:"";display:block;width:100%;height:80px}.el-time-spinner__item{height:32px;line-height:32px;font-size:12px;color:var(--el-text-color-regular)}.el-time-spinner__item:hover:not(.is-disabled):not(.is-active){background:var(--el-fill-color-light);cursor:pointer}.el-time-spinner__item.is-active:not(.is-disabled){color:var(--el-text-color-primary);font-weight:700}.el-time-spinner__item.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-picker__popper{--el-datepicker-border-color:var(--el-disabled-border-color)}.el-picker__popper.el-popper[role=tooltip]{background:var(--el-bg-color-overlay);border:1px solid var(--el-datepicker-border-color);box-shadow:var(--el-box-shadow-light)}.el-picker__popper.el-popper[role=tooltip] .el-popper__arrow:before{border:1px solid var(--el-datepicker-border-color)}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent;border-left-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent;border-right-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent;border-bottom-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent;border-top-color:transparent}.el-date-editor{--el-date-editor-width:220px;--el-date-editor-monthrange-width:300px;--el-date-editor-daterange-width:350px;--el-date-editor-datetimerange-width:400px;--el-input-text-color:var(--el-text-color-regular);--el-input-border:var(--el-border);--el-input-hover-border:var(--el-border-color-hover);--el-input-focus-border:var(--el-color-primary);--el-input-transparent-border:0 0 0 1px transparent inset;--el-input-border-color:var(--el-border-color);--el-input-border-radius:var(--el-border-radius-base);--el-input-bg-color:var(--el-fill-color-blank);--el-input-icon-color:var(--el-text-color-placeholder);--el-input-placeholder-color:var(--el-text-color-placeholder);--el-input-hover-border-color:var(--el-border-color-hover);--el-input-clear-hover-color:var(--el-text-color-secondary);--el-input-focus-border-color:var(--el-color-primary);position:relative;display:inline-block;text-align:left}.el-date-editor.el-input,.el-date-editor.el-input__inner{width:var(--el-date-editor-width)}.el-date-editor--monthrange.el-input,.el-date-editor--monthrange.el-input__inner{width:var(--el-date-editor-monthrange-width)}.el-date-editor--daterange.el-input,.el-date-editor--daterange.el-input__inner,.el-date-editor--timerange.el-input,.el-date-editor--timerange.el-input__inner{width:var(--el-date-editor-daterange-width)}.el-date-editor--datetimerange.el-input,.el-date-editor--datetimerange.el-input__inner{width:var(--el-date-editor-datetimerange-width)}.el-date-editor--dates .el-input__inner{text-overflow:ellipsis;white-space:nowrap}.el-date-editor .close-icon,.el-date-editor .clear-icon{cursor:pointer}.el-date-editor .clear-icon:hover{color:var(--el-text-color-secondary)}.el-date-editor .el-range__icon{height:inherit;font-size:14px;color:var(--el-text-color-placeholder);float:left}.el-date-editor .el-range__icon svg{vertical-align:middle}.el-date-editor .el-range-input{-webkit-appearance:none;appearance:none;border:none;outline:0;display:inline-block;height:100%;margin:0;padding:0;width:39%;text-align:center;font-size:var(--el-font-size-base);color:var(--el-text-color-regular);background-color:transparent}.el-date-editor .el-range-input::placeholder{color:var(--el-text-color-placeholder)}.el-date-editor .el-range-separator{flex:1;display:inline-flex;justify-content:center;align-items:center;height:100%;padding:0 5px;margin:0;font-size:14px;word-break:keep-all;color:var(--el-text-color-primary)}.el-date-editor .el-range__close-icon{font-size:14px;color:var(--el-text-color-placeholder);height:inherit;width:unset;cursor:pointer}.el-date-editor .el-range__close-icon:hover{color:var(--el-text-color-secondary)}.el-date-editor .el-range__close-icon svg{vertical-align:middle}.el-date-editor .el-range__close-icon--hidden{opacity:0;visibility:hidden}.el-range-editor.el-input__inner{display:inline-flex;align-items:center;padding:3px 10px}.el-range-editor .el-range-input{line-height:1}.el-range-editor.is-active,.el-range-editor.is-active:hover{box-shadow:0 0 0 1px var(--el-input-focus-border-color) inset}.el-range-editor--large{line-height:40px}.el-range-editor--large.el-input__inner{height:40px}.el-range-editor--large .el-range-separator{line-height:40px;font-size:14px}.el-range-editor--large .el-range-input{font-size:14px}.el-range-editor--small{line-height:24px}.el-range-editor--small.el-input__inner{height:24px}.el-range-editor--small .el-range-separator{line-height:24px;font-size:12px}.el-range-editor--small .el-range-input{font-size:12px}.el-range-editor.is-disabled{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color);color:var(--el-disabled-text-color);cursor:not-allowed}.el-range-editor.is-disabled:focus,.el-range-editor.is-disabled:hover{border-color:var(--el-disabled-border-color)}.el-range-editor.is-disabled input{background-color:var(--el-disabled-bg-color);color:var(--el-disabled-text-color);cursor:not-allowed}.el-range-editor.is-disabled input::placeholder{color:var(--el-text-color-placeholder)}.el-range-editor.is-disabled .el-range-separator{color:var(--el-disabled-text-color)}.el-picker-panel{color:var(--el-text-color-regular);background:var(--el-bg-color-overlay);border-radius:var(--el-border-radius-base);line-height:30px}.el-picker-panel .el-time-panel{margin:5px 0;border:solid 1px var(--el-datepicker-border-color);background-color:var(--el-color-white);box-shadow:var(--el-box-shadow-light)}.el-picker-panel__body-wrapper:after,.el-picker-panel__body:after{content:"";display:table;clear:both}.el-picker-panel__content{position:relative;margin:15px}.el-picker-panel__footer{border-top:1px solid var(--el-datepicker-inner-border-color);padding:4px 12px;text-align:right;background-color:var(--el-bg-color-overlay);position:relative;font-size:0}.el-picker-panel__shortcut{display:block;width:100%;border:0;background-color:transparent;line-height:28px;font-size:14px;color:var(--el-datepicker-text-color);padding-left:12px;text-align:left;outline:0;cursor:pointer}.el-picker-panel__shortcut:hover{color:var(--el-datepicker-hover-text-color)}.el-picker-panel__shortcut.active{background-color:#e6f1fe;color:var(--el-datepicker-active-color)}.el-picker-panel__btn{border:1px solid var(--el-fill-color-darker);color:var(--el-text-color-primary);line-height:24px;border-radius:2px;padding:0 20px;cursor:pointer;background-color:transparent;outline:0;font-size:12px}.el-picker-panel__btn[disabled]{color:var(--el-text-color-disabled);cursor:not-allowed}.el-picker-panel__icon-btn{font-size:12px;color:var(--el-datepicker-icon-color);border:0;background:0 0;cursor:pointer;outline:0;margin-top:8px}.el-picker-panel__icon-btn:hover{color:var(--el-datepicker-hover-text-color)}.el-picker-panel__icon-btn.is-disabled{color:var(--el-text-color-disabled)}.el-picker-panel__icon-btn.is-disabled:hover{cursor:not-allowed}.el-picker-panel__icon-btn .el-icon{cursor:pointer;font-size:inherit}.el-picker-panel__link-btn{vertical-align:middle}.el-picker-panel [slot=sidebar],.el-picker-panel__sidebar{position:absolute;top:0;bottom:0;width:110px;border-right:1px solid var(--el-datepicker-inner-border-color);box-sizing:border-box;padding-top:6px;background-color:var(--el-bg-color-overlay);overflow:auto}.el-picker-panel [slot=sidebar]+.el-picker-panel__body,.el-picker-panel__sidebar+.el-picker-panel__body{margin-left:110px}.el-date-picker{--el-datepicker-text-color:var(--el-text-color-regular);--el-datepicker-off-text-color:var(--el-text-color-placeholder);--el-datepicker-header-text-color:var(--el-text-color-regular);--el-datepicker-icon-color:var(--el-text-color-primary);--el-datepicker-border-color:var(--el-disabled-border-color);--el-datepicker-inner-border-color:var(--el-border-color-light);--el-datepicker-inrange-bg-color:var(--el-border-color-extra-light);--el-datepicker-inrange-hover-bg-color:var(--el-border-color-extra-light);--el-datepicker-active-color:var(--el-color-primary);--el-datepicker-hover-text-color:var(--el-color-primary)}.el-date-picker{width:322px}.el-date-picker.has-sidebar.has-time{width:434px}.el-date-picker.has-sidebar{width:438px}.el-date-picker.has-time .el-picker-panel__body-wrapper{position:relative}.el-date-picker .el-picker-panel__content{width:292px}.el-date-picker table{table-layout:fixed;width:100%}.el-date-picker__editor-wrap{position:relative;display:table-cell;padding:0 5px}.el-date-picker__time-header{position:relative;border-bottom:1px solid var(--el-datepicker-inner-border-color);font-size:12px;padding:8px 5px 5px;display:table;width:100%;box-sizing:border-box}.el-date-picker__header{margin:12px;text-align:center}.el-date-picker__header--bordered{margin-bottom:0;padding-bottom:12px;border-bottom:solid 1px var(--el-border-color-lighter)}.el-date-picker__header--bordered+.el-picker-panel__content{margin-top:0}.el-date-picker__header-label{font-size:16px;font-weight:500;padding:0 5px;line-height:22px;text-align:center;cursor:pointer;color:var(--el-text-color-regular)}.el-date-picker__header-label:hover{color:var(--el-datepicker-hover-text-color)}.el-date-picker__header-label.active{color:var(--el-datepicker-active-color)}.el-date-picker__prev-btn{float:left}.el-date-picker__next-btn{float:right}.el-date-picker__time-wrap{padding:10px;text-align:center}.el-date-picker__time-label{float:left;cursor:pointer;line-height:30px;margin-left:10px}.el-date-picker .el-time-panel{position:absolute}.el-date-range-picker{--el-datepicker-text-color:var(--el-text-color-regular);--el-datepicker-off-text-color:var(--el-text-color-placeholder);--el-datepicker-header-text-color:var(--el-text-color-regular);--el-datepicker-icon-color:var(--el-text-color-primary);--el-datepicker-border-color:var(--el-disabled-border-color);--el-datepicker-inner-border-color:var(--el-border-color-light);--el-datepicker-inrange-bg-color:var(--el-border-color-extra-light);--el-datepicker-inrange-hover-bg-color:var(--el-border-color-extra-light);--el-datepicker-active-color:var(--el-color-primary);--el-datepicker-hover-text-color:var(--el-color-primary)}.el-date-range-picker{width:646px}.el-date-range-picker.has-sidebar{width:756px}.el-date-range-picker table{table-layout:fixed;width:100%}.el-date-range-picker .el-picker-panel__body{min-width:513px}.el-date-range-picker 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var(--el-datepicker-border-color)}.el-time-panel{border-radius:2px;position:relative;width:180px;left:0;z-index:var(--el-index-top);-webkit-user-select:none;user-select:none;box-sizing:content-box}.el-time-panel__content{font-size:0;position:relative;overflow:hidden}.el-time-panel__content:after,.el-time-panel__content:before{content:"";top:50%;position:absolute;margin-top:-16px;height:32px;z-index:-1;left:0;right:0;box-sizing:border-box;padding-top:6px;text-align:left;border-top:1px solid var(--el-border-color-light);border-bottom:1px solid var(--el-border-color-light)}.el-time-panel__content:after{left:50%;margin-left:12%;margin-right:12%}.el-time-panel__content:before{padding-left:50%;margin-right:12%;margin-left:12%}.el-time-panel__content.has-seconds:after{left:66.6666666667%}.el-time-panel__content.has-seconds:before{padding-left:33.3333333333%}.el-time-panel__footer{border-top:1px solid 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.el-descriptions__cell{padding-bottom:8px}.el-descriptions__label.el-descriptions__cell.is-bordered-label{font-weight:700;color:var(--el-text-color-regular);background:var(--el-descriptions-item-bordered-label-background)}.el-descriptions__label:not(.is-bordered-label){color:var(--el-text-color-primary);margin-right:16px}.el-descriptions__label.el-descriptions__cell:not(.is-bordered-label).is-vertical-label{padding-bottom:6px}.el-descriptions__content.el-descriptions__cell.is-bordered-content{color:var(--el-text-color-primary)}.el-descriptions__content:not(.is-bordered-label){color:var(--el-text-color-regular)}.el-descriptions--large .el-descriptions__label:not(.is-bordered-label){margin-right:16px}.el-descriptions--large .el-descriptions__label.el-descriptions__cell:not(.is-bordered-label).is-vertical-label{padding-bottom:8px}.el-descriptions--small .el-descriptions__label:not(.is-bordered-label){margin-right:12px}.el-descriptions--small 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.el-icon{font-size:inherit;vertical-align:text-bottom}.el-drawer__body{flex:1;padding:var(--el-drawer-padding-primary);overflow:auto}.el-drawer__body>*{box-sizing:border-box}.el-drawer.ltr,.el-drawer.rtl{height:100%;top:0;bottom:0}.el-drawer.btt,.el-drawer.ttb{width:100%;left:0;right:0}.el-drawer.ltr{left:0}.el-drawer.rtl{right:0}.el-drawer.ttb{top:0}.el-drawer.btt{bottom:0}.el-drawer-fade-enter-active,.el-drawer-fade-leave-active{transition:all var(--el-transition-duration)}.el-drawer-fade-enter-active,.el-drawer-fade-enter-from,.el-drawer-fade-enter-to,.el-drawer-fade-leave-active,.el-drawer-fade-leave-from,.el-drawer-fade-leave-to{overflow:hidden!important}.el-drawer-fade-enter-from,.el-drawer-fade-leave-to{opacity:0}.el-drawer-fade-enter-to,.el-drawer-fade-leave-from{opacity:1}.el-drawer-fade-enter-from .rtl,.el-drawer-fade-leave-to .rtl{transform:translate(100%)}.el-drawer-fade-enter-from .ltr,.el-drawer-fade-leave-to .ltr{transform:translate(-100%)}.el-drawer-fade-enter-from 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.el-message-icon--warning{color:var(--el-message-text-color)}.el-message--error{--el-message-bg-color:var(--el-color-error-light-9);--el-message-border-color:var(--el-color-error-light-8);--el-message-text-color:var(--el-color-error)}.el-message--error .el-message__content,.el-message .el-message-icon--error{color:var(--el-message-text-color)}.el-message__icon{margin-right:10px}.el-message .el-message__badge{position:absolute;top:-8px;right:-8px}.el-message__content{padding:0;font-size:14px;line-height:1}.el-message__content:focus{outline-width:0}.el-message .el-message__closeBtn{position:absolute;top:50%;right:15px;transform:translateY(-50%);cursor:pointer;color:var(--el-message-close-icon-color);font-size:var(--el-message-close-size)}.el-message .el-message__closeBtn:focus{outline-width:0}.el-message .el-message__closeBtn:hover{color:var(--el-message-close-hover-color)}.el-message-fade-enter-from,.el-message-fade-leave-to{opacity:0;transform:translate(-50%,-100%)}.el-notification{--el-notification-width:330px;--el-notification-padding:14px 26px 14px 13px;--el-notification-radius:8px;--el-notification-shadow:var(--el-box-shadow-light);--el-notification-border-color:var(--el-border-color-lighter);--el-notification-icon-size:24px;--el-notification-close-font-size:var(--el-message-close-size, 16px);--el-notification-group-margin-left:13px;--el-notification-group-margin-right:8px;--el-notification-content-font-size:var(--el-font-size-base);--el-notification-content-color:var(--el-text-color-regular);--el-notification-title-font-size:16px;--el-notification-title-color:var(--el-text-color-primary);--el-notification-close-color:var(--el-text-color-secondary);--el-notification-close-hover-color:var(--el-text-color-regular)}.el-notification{display:flex;width:var(--el-notification-width);padding:var(--el-notification-padding);border-radius:var(--el-notification-radius);box-sizing:border-box;border:1px solid var(--el-notification-border-color);position:fixed;background-color:var(--el-bg-color-overlay);box-shadow:var(--el-notification-shadow);transition:opacity var(--el-transition-duration),transform var(--el-transition-duration),left var(--el-transition-duration),right var(--el-transition-duration),top .4s,bottom var(--el-transition-duration);overflow-wrap:anywhere;overflow:hidden;z-index:9999}.el-notification.right{right:16px}.el-notification.left{left:16px}.el-notification__group{margin-left:var(--el-notification-group-margin-left);margin-right:var(--el-notification-group-margin-right)}.el-notification__title{font-weight:700;font-size:var(--el-notification-title-font-size);line-height:var(--el-notification-icon-size);color:var(--el-notification-title-color);margin:0}.el-notification__content{font-size:var(--el-notification-content-font-size);line-height:24px;margin:6px 0 0;color:var(--el-notification-content-color);text-align:justify}.el-notification__content p{margin:0}.el-notification .el-notification__icon{height:var(--el-notification-icon-size);width:var(--el-notification-icon-size);font-size:var(--el-notification-icon-size)}.el-notification .el-notification__closeBtn{position:absolute;top:18px;right:15px;cursor:pointer;color:var(--el-notification-close-color);font-size:var(--el-notification-close-font-size)}.el-notification .el-notification__closeBtn:hover{color:var(--el-notification-close-hover-color)}.el-notification .el-notification--success{--el-notification-icon-color:var(--el-color-success);color:var(--el-notification-icon-color)}.el-notification .el-notification--info{--el-notification-icon-color:var(--el-color-info);color:var(--el-notification-icon-color)}.el-notification .el-notification--warning{--el-notification-icon-color:var(--el-color-warning);color:var(--el-notification-icon-color)}.el-notification .el-notification--error{--el-notification-icon-color:var(--el-color-error);color:var(--el-notification-icon-color)}.el-notification-fade-enter-from.right{right:0;transform:translate(100%)}.el-notification-fade-enter-from.left{left:0;transform:translate(-100%)}.el-notification-fade-leave-to{opacity:0}.el-overlay{position:fixed;top:0;right:0;bottom:0;left:0;z-index:2000;height:100%;background-color:var(--el-overlay-color-lighter);overflow:auto}.el-overlay .el-overlay-root{height:0}.el-page-header{display:flex;line-height:24px}.el-page-header__left{display:flex;cursor:pointer;margin-right:40px;position:relative}.el-page-header__left:after{content:"";position:absolute;width:1px;height:16px;right:-20px;top:50%;transform:translateY(-50%);background-color:var(--el-border-color)}.el-page-header__icon{font-size:18px;margin-right:6px;display:flex;align-items:center}.el-page-header__icon .el-icon{font-size:inherit}.el-page-header__title{font-size:14px;font-weight:500}.el-page-header__content{font-size:18px;color:var(--el-text-color-primary)}.el-pagination{--el-pagination-font-size:14px;--el-pagination-bg-color:var(--el-fill-color-blank);--el-pagination-text-color:var(--el-text-color-primary);--el-pagination-border-radius:3px;--el-pagination-button-color:var(--el-text-color-primary);--el-pagination-button-width:32px;--el-pagination-button-height:32px;--el-pagination-button-disabled-color:var(--el-text-color-placeholder);--el-pagination-button-disabled-bg-color:var(--el-fill-color-blank);--el-pagination-button-bg-color:var(--el-fill-color);--el-pagination-hover-color:var(--el-color-primary);--el-pagination-height-extra-small:24px;--el-pagination-line-height-extra-small:var(--el-pagination-height-extra-small);white-space:nowrap;padding:2px 5px;color:var(--el-pagination-text-color);font-weight:400;display:flex;align-items:center}.el-pagination:after,.el-pagination:before{display:table;content:""}.el-pagination:after{clear:both}.el-pagination button,.el-pagination span:not([class*=suffix]){display:flex;justify-content:center;align-items:center;font-size:var(--el-pagination-font-size);min-width:var(--el-pagination-button-width);height:var(--el-pagination-button-height);line-height:var(--el-pagination-button-height);box-sizing:border-box}.el-pagination .el-input__inner{text-align:center;-moz-appearance:textfield;line-height:normal}.el-pagination .el-select .el-input{width:128px}.el-pagination button{border:none;padding:0 6px;background:0 0}.el-pagination button:focus{outline:0}.el-pagination button:hover{color:var(--el-pagination-hover-color)}.el-pagination button:disabled{color:var(--el-pagination-button-disabled-color);background-color:var(--el-pagination-button-disabled-bg-color);cursor:not-allowed}.el-pagination .btn-next,.el-pagination .btn-prev{background:center center no-repeat;background-size:16px;background-color:var(--el-pagination-bg-color);cursor:pointer;margin:0;color:var(--el-pagination-button-color)}.el-pagination .btn-next .el-icon,.el-pagination .btn-prev .el-icon{display:block;font-size:12px;font-weight:700;width:inherit}.el-pagination .el-pager li.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-pagination--small .btn-next,.el-pagination--small .btn-prev,.el-pagination--small .el-pager li,.el-pagination--small .el-pager li.btn-quicknext,.el-pagination--small .el-pager li.btn-quickprev,.el-pagination--small .el-pager li:last-child{border-color:transparent;font-size:var(--el-font-size-extra-small);line-height:var(--el-pagination-line-height-extra-small);height:var(--el-pagination-height-extra-small);min-width:24px}.el-pagination--small .arrow.is-disabled{visibility:hidden}.el-pagination--small .more:before,.el-pagination--small li.more:before{line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small button,.el-pagination--small span:not([class*=suffix]){height:var(--el-pagination-height-extra-small);line-height:var(--el-pagination-line-height-extra-small);font-size:var(--el-font-size-extra-small)}.el-pagination--small .el-pagination__editor{height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-pagination__editor.el-input .el-input__inner{height:var(--el-pagination-height-extra-small)}.el-pagination--small .el-input--small,.el-pagination--small .el-input__inner{height:var(--el-pagination-height-extra-small)!important;line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-input__suffix,.el-pagination--small .el-input__suffix .el-input__suffix-inner,.el-pagination--small .el-input__suffix .el-input__suffix-inner i.el-select__caret{line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-select .el-input{width:100px}.el-pagination__sizes{margin:0 16px 0 0;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__sizes+button.btn-prev[type=button]{margin-left:0}.el-pagination__sizes+.el-pager .number:first-child{margin-left:0}.el-pagination__sizes+.el-pager .number:last-child{margin-right:0}.el-pagination__total{margin-right:16px;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__total+button.btn-prev[type=button]{margin-left:0}.el-pagination__total+.el-pager .number:first-child{margin-left:0}.el-pagination__total+.el-pager .number:last-child{margin-right:0}.el-pagination__total[disabled=true]{color:var(--el-text-color-placeholder)}.el-pagination__jump{margin-left:16px;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__jump .el-input__inner{padding:0 3px}.el-pagination__jump[disabled=true]{color:var(--el-text-color-placeholder)}.el-pagination__rightwrapper{flex:1;display:flex;align-items:center;justify-content:flex-end}.el-pagination__editor{line-height:18px;margin:0 8px;height:var(--el-pagination-button-height);min-width:56px;text-align:center;box-sizing:border-box;border-radius:var(--el-pagination-border-radius)}.el-pagination__editor.el-input{width:50px}.el-pagination__editor.el-input .el-input__inner{height:var(--el-pagination-button-height)}.el-pagination__editor .el-input__inner::-webkit-inner-spin-button,.el-pagination__editor .el-input__inner::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.el-pagination.is-background .btn-next,.el-pagination.is-background .btn-prev,.el-pagination.is-background .el-pager li{margin:0 4px;background-color:var(--el-pagination-button-bg-color);color:var(--el-text-color-regular);min-width:32px;border-radius:2px}.el-pagination.is-background .btn-next.is-disabled,.el-pagination.is-background .btn-prev.is-disabled,.el-pagination.is-background .el-pager li.is-disabled{color:var(--el-text-color-placeholder);background-color:var(--el-disabled-bg-color)}.el-pagination.is-background .btn-next.is-first,.el-pagination.is-background .btn-prev.is-first,.el-pagination.is-background .el-pager li.is-first{margin-left:0}.el-pagination.is-background .btn-next.is-last,.el-pagination.is-background .btn-prev.is-last,.el-pagination.is-background .el-pager li.is-last{margin-right:0}.el-pagination.is-background .btn-next,.el-pagination.is-background .btn-prev{padding:0}.el-pagination.is-background .btn-next:disabled,.el-pagination.is-background .btn-prev:disabled{color:var(--el-text-color-placeholder);background-color:var(--el-disabled-bg-color)}.el-pagination.is-background .btn-next:hover:not([disabled]),.el-pagination.is-background .btn-prev:hover:not([disabled]){color:var(--el-pagination-hover-color)}.el-pagination.is-background .el-pager li:not(.is-disabled):hover{color:var(--el-pagination-hover-color)}.el-pagination.is-background .el-pager li:not(.is-disabled).is-active{background-color:var(--el-color-primary);color:var(--el-color-white);font-weight:700}.el-pagination.is-background.el-pagination--small .btn-next,.el-pagination.is-background.el-pagination--small .btn-prev,.el-pagination.is-background.el-pagination--small .el-pager li{min-width:24px}.el-pager{-webkit-user-select:none;user-select:none;list-style:none;font-size:0;padding:0;margin:0;display:flex;align-items:center}.el-pager li{padding:0 4px;background:var(--el-pagination-bg-color);display:flex;justify-content:center;align-items:center;font-size:var(--el-pagination-font-size);min-width:var(--el-pagination-button-width);height:var(--el-pagination-button-height);line-height:var(--el-pagination-button-height);box-sizing:border-box;cursor:pointer;text-align:center}.el-pager li.btn-quickprev:hover,.el-pager li.btn-quicknext:hover{cursor:pointer}.el-pager li.btn-quicknext,.el-pager li.btn-quickprev{line-height:32px;color:var(--el-pagination-button-color)}.el-pager li.btn-quicknext.is-disabled,.el-pager li.btn-quickprev.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-pager li.btn-quicknext svg,.el-pager li.btn-quickprev svg{pointer-events:none}.el-pager li.is-active+li{border-left:0}.el-pager li:focus-visible{outline:1px solid var(--el-pagination-hover-color)}.el-pager li:hover{color:var(--el-pagination-hover-color)}.el-pager li.is-active{color:var(--el-pagination-hover-color);cursor:default}.el-pager+button.btn-next[type=button]{margin-right:0}.el-popconfirm__main{display:flex;align-items:center}.el-popconfirm__icon{margin-right:5px}.el-popconfirm__action{text-align:right;margin-top:8px}.el-popover{--el-popover-bg-color:var(--el-color-white);--el-popover-font-size:var(--el-font-size-base);--el-popover-border-color:var(--el-border-color-lighter);--el-popover-padding:12px;--el-popover-padding-large:18px 20px;--el-popover-title-font-size:16px;--el-popover-title-text-color:var(--el-text-color-primary);--el-popover-border-radius:4px}.el-popover.el-popper{background:var(--el-popover-bg-color);min-width:150px;border-radius:var(--el-popover-border-radius);border:1px solid var(--el-popover-border-color);padding:var(--el-popover-padding);z-index:var(--el-index-popper);color:var(--el-text-color-regular);line-height:1.4;text-align:justify;font-size:var(--el-popover-font-size);box-shadow:var(--el-box-shadow-light);word-break:break-all}.el-popover.el-popper--plain{padding:var(--el-popover-padding-large)}.el-popover__title{color:var(--el-popover-title-text-color);font-size:var(--el-popover-title-font-size);line-height:1;margin-bottom:12px}.el-popover__reference:focus:hover,.el-popover__reference:focus:not(.focusing){outline-width:0}.el-popover.el-popper:focus,.el-popover.el-popper:focus:active{outline-width:0}.el-progress{position:relative;line-height:1;display:flex;align-items:center}.el-progress__text{font-size:14px;color:var(--el-text-color-regular);margin-left:5px;min-width:50px;line-height:1}.el-progress__text i{vertical-align:middle;display:block}.el-progress--circle,.el-progress--dashboard{display:inline-block}.el-progress--circle .el-progress__text,.el-progress--dashboard .el-progress__text{position:absolute;top:50%;left:0;width:100%;text-align:center;margin:0;transform:translateY(-50%)}.el-progress--circle .el-progress__text i,.el-progress--dashboard .el-progress__text i{vertical-align:middle;display:inline-block}.el-progress--without-text .el-progress__text{display:none}.el-progress--without-text .el-progress-bar{padding-right:0;margin-right:0;display:block}.el-progress--text-inside .el-progress-bar{padding-right:0;margin-right:0}.el-progress.is-success .el-progress-bar__inner{background-color:var(--el-color-success)}.el-progress.is-success .el-progress__text{color:var(--el-color-success)}.el-progress.is-warning .el-progress-bar__inner{background-color:var(--el-color-warning)}.el-progress.is-warning .el-progress__text{color:var(--el-color-warning)}.el-progress.is-exception .el-progress-bar__inner{background-color:var(--el-color-danger)}.el-progress.is-exception .el-progress__text{color:var(--el-color-danger)}.el-progress-bar{flex-grow:1;box-sizing:border-box}.el-progress-bar__outer{height:6px;border-radius:100px;background-color:var(--el-border-color-lighter);overflow:hidden;position:relative;vertical-align:middle}.el-progress-bar__inner{position:absolute;left:0;top:0;height:100%;background-color:var(--el-color-primary);text-align:right;border-radius:100px;line-height:1;white-space:nowrap;transition:width .6s ease}.el-progress-bar__inner:after{display:inline-block;content:"";height:100%;vertical-align:middle}.el-progress-bar__inner--indeterminate{transform:translateZ(0);animation:indeterminate 3s infinite}.el-progress-bar__innerText{display:inline-block;vertical-align:middle;color:#fff;font-size:12px;margin:0 5px}@keyframes progress{0%{background-position:0 0}to{background-position:32px 0}}@keyframes indeterminate{0%{left:-100%}to{left:100%}}.el-radio-button{--el-radio-button-checked-bg-color:var(--el-color-primary);--el-radio-button-checked-text-color:var(--el-color-white);--el-radio-button-checked-border-color:var(--el-color-primary);--el-radio-button-disabled-checked-fill:var(--el-border-color-extra-light)}.el-radio-button{position:relative;display:inline-block;outline:0}.el-radio-button__inner{display:inline-block;line-height:1;white-space:nowrap;vertical-align:middle;background:var(--el-button-bg-color,var(--el-fill-color-blank));border:var(--el-border);font-weight:var(--el-button-font-weight,var(--el-font-weight-primary));border-left:0;color:var(--el-button-text-color,var(--el-text-color-regular));-webkit-appearance:none;text-align:center;box-sizing:border-box;outline:0;margin:0;position:relative;cursor:pointer;transition:var(--el-transition-all);-webkit-user-select:none;user-select:none;padding:8px 15px;font-size:var(--el-font-size-base);border-radius:0}.el-radio-button__inner.is-round{padding:8px 15px}.el-radio-button__inner:hover{color:var(--el-color-primary)}.el-radio-button__inner [class*=el-icon-]{line-height:.9}.el-radio-button__inner [class*=el-icon-]+span{margin-left:5px}.el-radio-button:first-child .el-radio-button__inner{border-left:var(--el-border);border-radius:var(--el-border-radius-base) 0 0 var(--el-border-radius-base);box-shadow:none!important}.el-radio-button__original-radio{opacity:0;outline:0;position:absolute;z-index:-1}.el-radio-button__original-radio:checked+.el-radio-button__inner{color:var(--el-radio-button-checked-text-color,var(--el-color-white));background-color:var(--el-radio-button-checked-bg-color,var(--el-color-primary));border-color:var(--el-radio-button-checked-border-color,var(--el-color-primary));box-shadow:-1px 0 0 0 var(--el-radio-button-checked-border-color,var(--el-color-primary))}.el-radio-button__original-radio:disabled+.el-radio-button__inner{color:var(--el-disabled-text-color);cursor:not-allowed;background-image:none;background-color:var(--el-button-disabled-bg-color,var(--el-fill-color-blank));border-color:var(--el-button-disabled-border-color,var(--el-border-color-light));box-shadow:none}.el-radio-button__original-radio:disabled:checked+.el-radio-button__inner{background-color:var(--el-radio-button-disabled-checked-fill)}.el-radio-button:last-child .el-radio-button__inner{border-radius:0 var(--el-border-radius-base) var(--el-border-radius-base) 0}.el-radio-button:first-child:last-child .el-radio-button__inner{border-radius:var(--el-border-radius-base)}.el-radio-button--large .el-radio-button__inner{padding:12px 19px;font-size:var(--el-font-size-base);border-radius:0}.el-radio-button--large .el-radio-button__inner.is-round{padding:12px 19px}.el-radio-button--small .el-radio-button__inner{padding:5px 11px;font-size:12px;border-radius:0}.el-radio-button--small .el-radio-button__inner.is-round{padding:5px 11px}.el-radio-button:focus:not(.is-focus):not(:active):not(.is-disabled){box-shadow:0 0 2px 2px var(--el-radio-button-checked-border-color)}.el-radio-group{display:inline-flex;align-items:center;flex-wrap:wrap;font-size:0}.el-radio{--el-radio-font-size:var(--el-font-size-base);--el-radio-text-color:var(--el-text-color-regular);--el-radio-font-weight:var(--el-font-weight-primary);--el-radio-input-height:14px;--el-radio-input-width:14px;--el-radio-input-border-radius:var(--el-border-radius-circle);--el-radio-input-bg-color:var(--el-fill-color-blank);--el-radio-input-border:var(--el-border);--el-radio-input-border-color:var(--el-border-color);--el-radio-input-border-color-hover:var(--el-color-primary)}.el-radio{color:var(--el-radio-text-color);font-weight:var(--el-radio-font-weight);position:relative;cursor:pointer;display:inline-flex;align-items:center;white-space:nowrap;outline:0;font-size:var(--el-font-size-base);-webkit-user-select:none;user-select:none;margin-right:32px;height:32px}.el-radio.el-radio--large{height:40px}.el-radio.el-radio--small{height:24px}.el-radio.is-bordered{padding:0 15px 0 9px;border-radius:var(--el-border-radius-base);border:var(--el-border);box-sizing:border-box}.el-radio.is-bordered.is-checked{border-color:var(--el-color-primary)}.el-radio.is-bordered.is-disabled{cursor:not-allowed;border-color:var(--el-border-color-lighter)}.el-radio.is-bordered.el-radio--large{padding:0 19px 0 11px;border-radius:var(--el-border-radius-base)}.el-radio.is-bordered.el-radio--large .el-radio__label{font-size:var(--el-font-size-base)}.el-radio.is-bordered.el-radio--large .el-radio__inner{height:14px;width:14px}.el-radio.is-bordered.el-radio--small{padding:0 11px 0 7px;border-radius:var(--el-border-radius-base)}.el-radio.is-bordered.el-radio--small .el-radio__label{font-size:12px}.el-radio.is-bordered.el-radio--small .el-radio__inner{height:12px;width:12px}.el-radio:last-child{margin-right:0}.el-radio__input{white-space:nowrap;cursor:pointer;outline:0;display:inline-flex;position:relative;vertical-align:middle}.el-radio__input.is-disabled .el-radio__inner{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color);cursor:not-allowed}.el-radio__input.is-disabled .el-radio__inner:after{cursor:not-allowed;background-color:var(--el-disabled-bg-color)}.el-radio__input.is-disabled .el-radio__inner+.el-radio__label{cursor:not-allowed}.el-radio__input.is-disabled.is-checked .el-radio__inner{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color)}.el-radio__input.is-disabled.is-checked .el-radio__inner:after{background-color:var(--el-text-color-placeholder)}.el-radio__input.is-disabled+span.el-radio__label{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-radio__input.is-checked .el-radio__inner{border-color:var(--el-color-primary);background:var(--el-color-primary)}.el-radio__input.is-checked .el-radio__inner:after{transform:translate(-50%,-50%) scale(1)}.el-radio__input.is-checked+.el-radio__label{color:var(--el-color-primary)}.el-radio__input.is-focus .el-radio__inner{border-color:var(--el-radio-input-border-color-hover)}.el-radio__inner{border:var(--el-radio-input-border);border-radius:var(--el-radio-input-border-radius);width:var(--el-radio-input-width);height:var(--el-radio-input-height);background-color:var(--el-radio-input-bg-color);position:relative;cursor:pointer;display:inline-block;box-sizing:border-box}.el-radio__inner:hover{border-color:var(--el-radio-input-border-color-hover)}.el-radio__inner:after{width:4px;height:4px;border-radius:var(--el-radio-input-border-radius);background-color:var(--el-color-white);content:"";position:absolute;left:50%;top:50%;transform:translate(-50%,-50%) scale(0);transition:transform .15s ease-in}.el-radio__original{opacity:0;outline:0;position:absolute;z-index:-1;top:0;left:0;right:0;bottom:0;margin:0}.el-radio:focus:not(.is-focus):not(:active):not(.is-disabled) .el-radio__inner{box-shadow:0 0 2px 2px var(--el-radio-input-border-color-hover)}.el-radio__label{font-size:var(--el-radio-font-size);padding-left:8px}.el-radio.el-radio--large .el-radio__label{font-size:14px}.el-radio.el-radio--large .el-radio__inner{width:14px;height:14px}.el-radio.el-radio--small .el-radio__label{font-size:12px}.el-radio.el-radio--small .el-radio__inner{width:12px;height:12px}.el-rate{--el-rate-height:20px;--el-rate-font-size:var(--el-font-size-base);--el-rate-icon-size:18px;--el-rate-icon-margin:6px;--el-rate-void-color:var(--el-border-color-darker);--el-rate-fill-color:#f7ba2a;--el-rate-disabled-void-color:var(--el-fill-color);--el-rate-text-color:var(--el-text-color-primary)}.el-rate{display:inline-flex;align-items:center;height:32px}.el-rate:active,.el-rate:focus{outline-width:0}.el-rate__item{cursor:pointer;display:inline-block;position:relative;font-size:0;vertical-align:middle;color:var(--el-rate-void-color)}.el-rate .el-rate__icon{position:relative;display:inline-block;font-size:var(--el-rate-icon-size);margin-right:var(--el-rate-icon-margin);transition:var(--el-transition-duration)}.el-rate .el-rate__icon.hover{transform:scale(1.15)}.el-rate .el-rate__icon .path2{position:absolute;left:0;top:0}.el-rate .el-rate__icon.is-active{color:var(--el-rate-fill-color)}.el-rate__decimal{position:absolute;top:0;left:0;display:inline-block;overflow:hidden;color:var(--el-rate-fill-color)}.el-rate__text{font-size:var(--el-rate-font-size);vertical-align:middle;color:var(--el-rate-text-color)}.el-rate--large{height:40px}.el-rate--small{height:24px}.el-rate.is-disabled .el-rate__item{cursor:auto;color:var(--el-rate-disabled-void-color)}.el-result{--el-result-padding:40px 30px;--el-result-icon-font-size:64px;--el-result-title-font-size:20px;--el-result-title-margin-top:20px;--el-result-subtitle-margin-top:10px;--el-result-extra-margin-top:30px}.el-result{display:flex;justify-content:center;align-items:center;flex-direction:column;text-align:center;box-sizing:border-box;padding:var(--el-result-padding)}.el-result__icon svg{width:var(--el-result-icon-font-size);height:var(--el-result-icon-font-size)}.el-result__title{margin-top:var(--el-result-title-margin-top)}.el-result__title p{margin:0;font-size:var(--el-result-title-font-size);color:var(--el-text-color-primary);line-height:1.3}.el-result__subtitle{margin-top:var(--el-result-subtitle-margin-top)}.el-result__subtitle p{margin:0;font-size:var(--el-font-size-base);color:var(--el-text-color-regular);line-height:1.3}.el-result__extra{margin-top:var(--el-result-extra-margin-top)}.el-result .icon-success{--el-result-color:var(--el-color-success);color:var(--el-result-color)}.el-result .icon-warning{--el-result-color:var(--el-color-warning);color:var(--el-result-color)}.el-result .icon-danger{--el-result-color:var(--el-color-danger);color:var(--el-result-color)}.el-result .icon-info{--el-result-color:var(--el-color-info);color:var(--el-result-color)}.el-result .icon-error{--el-result-color:var(--el-color-error);color:var(--el-result-color)}.el-row{display:flex;flex-wrap:wrap;position:relative;box-sizing:border-box}.el-row.is-justify-center{justify-content:center}.el-row.is-justify-end{justify-content:flex-end}.el-row.is-justify-space-between{justify-content:space-between}.el-row.is-justify-space-around{justify-content:space-around}.el-row.is-justify-space-evenly{justify-content:space-evenly}.el-row.is-align-middle{align-items:center}.el-row.is-align-bottom{align-items:flex-end}.el-scrollbar{--el-scrollbar-opacity:.3;--el-scrollbar-bg-color:var(--el-text-color-secondary);--el-scrollbar-hover-opacity:.5;--el-scrollbar-hover-bg-color:var(--el-text-color-secondary)}.el-scrollbar{overflow:hidden;position:relative;height:100%}.el-scrollbar__wrap{overflow:auto;height:100%}.el-scrollbar__wrap--hidden-default{scrollbar-width:none}.el-scrollbar__wrap--hidden-default::-webkit-scrollbar{display:none}.el-scrollbar__thumb{position:relative;display:block;width:0;height:0;cursor:pointer;border-radius:inherit;background-color:var(--el-scrollbar-bg-color,var(--el-text-color-secondary));transition:var(--el-transition-duration) background-color;opacity:var(--el-scrollbar-opacity,.3)}.el-scrollbar__thumb:hover{background-color:var(--el-scrollbar-hover-bg-color,var(--el-text-color-secondary));opacity:var(--el-scrollbar-hover-opacity,.5)}.el-scrollbar__bar{position:absolute;right:2px;bottom:2px;z-index:1;border-radius:4px}.el-scrollbar__bar.is-vertical{width:6px;top:2px}.el-scrollbar__bar.is-vertical>div{width:100%}.el-scrollbar__bar.is-horizontal{height:6px;left:2px}.el-scrollbar__bar.is-horizontal>div{height:100%}.el-scrollbar-fade-enter-active{transition:opacity .34s ease-out}.el-scrollbar-fade-leave-active{transition:opacity .12s ease-out}.el-scrollbar-fade-enter-from,.el-scrollbar-fade-leave-active{opacity:0}.el-select-dropdown{z-index:calc(var(--el-index-top) + 1);border-radius:var(--el-border-radius-base);box-sizing:border-box}.el-select-dropdown .el-scrollbar.is-empty .el-select-dropdown__list{padding:0}.el-select-dropdown__option-item:hover:not(.hover){background-color:transparent}.el-select-dropdown__empty{padding:10px 0;margin:0;text-align:center;color:var(--el-text-color-secondary);font-size:var(--el-select-font-size)}.el-select-dropdown__wrap{max-height:274px}.el-select-dropdown__list{list-style:none;margin:6px 0!important;padding:0!important;box-sizing:border-box}.el-select-dropdown__option-item{font-size:var(--el-select-font-size);padding:0 32px 0 20px;position:relative;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;color:var(--el-text-color-regular);height:34px;line-height:34px;box-sizing:border-box;cursor:pointer}.el-select-dropdown__option-item.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-select-dropdown__option-item.is-disabled:hover{background-color:var(--el-color-white)}.el-select-dropdown__option-item.is-selected{background-color:var(--el-fill-color-light);font-weight:700}.el-select-dropdown__option-item.is-selected:not(.is-multiple){color:var(--el-color-primary)}.el-select-dropdown__option-item.hover{background-color:var(--el-fill-color-light)!important}.el-select-dropdown__option-item:hover{background-color:var(--el-fill-color-light)}.el-select-dropdown.is-multiple .el-select-dropdown__option-item.is-selected{color:var(--el-color-primary);background-color:var(--el-bg-color-overlay)}.el-select-dropdown.is-multiple .el-select-dropdown__option-item.is-selected .el-icon{position:absolute;right:20px;top:0;height:inherit;font-size:12px}.el-select-dropdown.is-multiple .el-select-dropdown__option-item.is-selected .el-icon svg{height:inherit;vertical-align:middle}.el-select-group{margin:0;padding:0}.el-select-group__wrap{position:relative;list-style:none;margin:0;padding:0}.el-select-group__wrap:not(:last-of-type){padding-bottom:24px}.el-select-group__wrap:not(:last-of-type):after{content:"";position:absolute;display:block;left:20px;right:20px;bottom:12px;height:1px;background:var(--el-border-color-light)}.el-select-group__split-dash{position:absolute;left:20px;right:20px;height:1px;background:var(--el-border-color-light)}.el-select-group__title{padding-left:20px;font-size:12px;color:var(--el-color-info);line-height:30px}.el-select-group .el-select-dropdown__item{padding-left:20px}.el-select-v2{--el-select-border-color-hover:var(--el-border-color-hover);--el-select-disabled-border:var(--el-disabled-border-color);--el-select-font-size:var(--el-font-size-base);--el-select-close-hover-color:var(--el-text-color-secondary);--el-select-input-color:var(--el-text-color-placeholder);--el-select-multiple-input-color:var(--el-text-color-regular);--el-select-input-focus-border-color:var(--el-color-primary);--el-select-input-font-size:14px}.el-select-v2{display:inline-block;position:relative;vertical-align:middle;font-size:14px}.el-select-v2__wrapper{display:flex;align-items:center;flex-wrap:wrap;box-sizing:border-box;cursor:pointer;padding:1px 30px 1px 0;border:1px solid var(--el-border-color);border-radius:var(--el-border-radius-base);transition:border-color var(--el-transition-duration-fast) var(--el-ease-in-out-bezier-function)}.el-select-v2__wrapper:hover{border-color:var(--el-text-color-placeholder)}.el-select-v2__wrapper.is-filterable{cursor:text}.el-select-v2__wrapper.is-focused{border-color:var(--el-color-primary)}.el-select-v2__wrapper.is-hovering:not(.is-focused){border-color:var(--el-text-color-placeholder)}.el-select-v2__wrapper.is-disabled{cursor:not-allowed;background-color:var(--el-fill-color-light);color:var(--el-text-color-placeholder);border-color:var(--el-select-disabled-border)}.el-select-v2__wrapper.is-disabled:hover{border-color:var(--el-select-disabled-border)}.el-select-v2__wrapper.is-disabled.is-focus{border-color:var(--el-input-focus-border-color)}.el-select-v2__wrapper.is-disabled .is-transparent{opacity:1;-webkit-user-select:none;user-select:none}.el-select-v2__wrapper.is-disabled .el-select-v2__caret,.el-select-v2__wrapper.is-disabled .el-select-v2__combobox-input{cursor:not-allowed}.el-select-v2__wrapper 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for tf.audio namespace.",type:"Modules",docs:`Public API for tf.audio namespace. `},{name:"autodiff",path:"./tf/autodiff.md",desc:" modulePublic API for tf.autodiff namespace.",type:"Modules",docs:`Public API for tf.autodiff namespace. `},{name:"autograph",path:"./tf/autograph.md",desc:" moduleConversion of eager-style Python into TensorFlow graph code.",type:"Modules",docs:`Conversion of eager-style Python into TensorFlow graph code. @@ -233,7 +233,7 @@ reasonable default behavior. `},{name:"dtypes",path:"./tf/dtypes.md",desc:" modulePublic API for tf.dtypes namespace.",type:"Modules",docs:`Public API for tf.dtypes namespace. `},{name:"errors",path:"./tf/errors.md",desc:" moduleException types for TensorFlow errors.",type:"Modules",docs:`Exception types for TensorFlow errors. -`},{name:"estimator",path:"./tf/estimator.md",desc:" moduleEstimatorHigh level tools for working with models.",type:"Modules",docs:null},{name:"experimental",path:"./tf/experimental.md",desc:" modulePublic API for tf.experimental namespace.",type:"Modules",docs:`Public API for tf.experimental namespace. +`},{name:"estimator",path:"./tf/estimator.md",desc:" moduleEstimatorHigh level tools for working with models.",type:"Modules",docs:""},{name:"experimental",path:"./tf/experimental.md",desc:" modulePublic API for tf.experimental namespace.",type:"Modules",docs:`Public API for tf.experimental namespace. `},{name:"feature_column",path:"./tf/feature_column.md",desc:" modulePublic API for tf.feature_column namespace.",type:"Modules",docs:`Public API for tf.feature_column namespace. `},{name:"graph_util",path:"./tf/graph_util.md",desc:" moduleHelpers to manipulate a tensor graph in python.",type:"Modules",docs:`Helpers to manipulate a tensor graph in python. @@ -342,7 +342,7 @@ considered atoms: \`tf.nest.is_nested\` checks whether an object is a nested structure or an atom. For example: - >>> tf.nest.is_nested("1234") + \`\`\`>>> tf.nest.is_nested("1234") False >>> tf.nest.is_nested([1, 3, [4, 5]]) True @@ -364,6 +364,7 @@ For example: >>> tf.nest.is_nested(ones) False +\`\`\` Note: A proper structure shall form a tree. The user shall ensure there is no cyclic references within the items in the structure, i.e., no references in the structure of the input of these functions @@ -447,7 +448,7 @@ same number of pixels on both sides. Here is an example of \`'SAME'\` padding: ->>> in_height = 5 +\`\`\`>>> in_height = 5 >>> filter_height = 3 >>> stride_height = 2 >>> @@ -464,6 +465,7 @@ Here is an example of \`'SAME'\` padding: >>> output.shape[2] # output_width: ceil(2 / 1) 2 +\`\`\` ### Explicit padding Certain ops, like \`tf.nn.conv2d\`, also allow a list of explicit padding amounts @@ -477,7 +479,7 @@ have padding. For example: ->>> inp = tf.ones((1, 3, 3, 1)) +\`\`\`>>> inp = tf.ones((1, 3, 3, 1)) >>> filter = tf.ones((2, 2, 1, 1)) >>> strides = [1, 1] >>> padding = [[0, 0], [1, 2], [0, 1], [0, 0]] @@ -490,6 +492,7 @@ For example: >>> output2 = tf.nn.conv2d(inp, filter, strides, padding='VALID') >>> tf.debugging.assert_equal(output, output2) +\`\`\` `},{name:"profiler",path:"./tf/profiler.md",desc:" modulePublic API for tf.profiler namespace.",type:"Modules",docs:`Public API for tf.profiler namespace. `},{name:"quantization",path:"./tf/quantization.md",desc:" modulePublic API for tf.quantization namespace.",type:"Modules",docs:`Public API for tf.quantization namespace. `},{name:"queue",path:"./tf/queue.md",desc:" modulePublic API for tf.queue namespace.",type:"Modules",docs:`Public API for tf.queue namespace. @@ -679,20 +682,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. sess.run(v.initializer) sess.run(bad_sum) # May return 0.0 \`\`\` - `},{name:"DType",path:"./tf/dtypes/DType.md",desc:"Represents the type of the elements in a `Tensor`.",type:"Classes",docs:`Represents the type of the elements in a \`Tensor\`. - - \`DType\`'s are used to specify the output data type for operations which - require it, or to inspect the data type of existing \`Tensor\`'s. - - Examples: - - >>> tf.constant(1, dtype=tf.int64) - - >>> tf.constant(1.0).dtype - tf.float32 - - See \`tf.dtypes\` for a complete list of \`DType\`'s defined. - `},{name:"DeviceSpec",path:"./tf/DeviceSpec.md",desc:"Represents a (possibly partial",type:"Classes",docs:`Represents a (possibly partial) specification for a TensorFlow device. + `},{name:"DType",path:"./tf/dtypes/DType.md",desc:"Represents the type of the elements in a `Tensor`.",type:"Classes",docs:"Represents the type of the elements in a `Tensor`.\n\n `DType`'s are used to specify the output data type for operations which\n require it, or to inspect the data type of existing `Tensor`'s.\n\n Examples:\n\n ```>>> tf.constant(1, dtype=tf.int64)\n \n >>> tf.constant(1.0).dtype\n tf.float32\n\n```\n See `tf.dtypes` for a complete list of `DType`'s defined.\n "},{name:"DeviceSpec",path:"./tf/DeviceSpec.md",desc:"Represents a (possibly partial",type:"Classes",docs:`Represents a (possibly partial) specification for a TensorFlow device. \`DeviceSpec\`s are used throughout TensorFlow to describe where state is stored and computations occur. Using \`DeviceSpec\` allows you to parse device spec @@ -759,7 +749,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. For example, consider the function \`y = x * x\`. The gradient at \`x = 3.0\` can be computed as: - >>> x = tf.constant(3.0) + \`\`\`>>> x = tf.constant(3.0) >>> with tf.GradientTape() as g: ... g.watch(x) ... y = x * x @@ -767,9 +757,10 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> print(dy_dx) tf.Tensor(6.0, shape=(), dtype=float32) +\`\`\` GradientTapes can be nested to compute higher-order derivatives. For example, - >>> x = tf.constant(5.0) + \`\`\`>>> x = tf.constant(5.0) >>> with tf.GradientTape() as g: ... g.watch(x) ... with tf.GradientTape() as gg: @@ -782,13 +773,14 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> print(d2y_dx2) tf.Tensor(2.0, shape=(), dtype=float32) +\`\`\` By default, the resources held by a GradientTape are released as soon as GradientTape.gradient() method is called. To compute multiple gradients over the same computation, create a persistent gradient tape. This allows multiple calls to the gradient() method as resources are released when the tape object is garbage collected. For example: - >>> x = tf.constant(3.0) + \`\`\`>>> x = tf.constant(3.0) >>> with tf.GradientTape(persistent=True) as g: ... g.watch(x) ... y = x * x @@ -800,12 +792,13 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> print(dy_dx) tf.Tensor(6.0, shape=(), dtype=float32) +\`\`\` By default GradientTape will automatically watch any trainable variables that are accessed inside the context. If you want fine grained control over which variables are watched you can disable automatic tracking by passing \`watch_accessed_variables=False\` to the tape constructor: - >>> x = tf.Variable(2.0) + \`\`\`>>> x = tf.Variable(2.0) >>> w = tf.Variable(5.0) >>> with tf.GradientTape( ... watch_accessed_variables=False, persistent=True) as tape: @@ -820,6 +813,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> print(dz_dw) None +\`\`\` Note that when using models you should ensure that your variables exist when using \`watch_accessed_variables=False\`. Otherwise it's quite easy to make your first iteration not have any gradients: @@ -880,13 +874,13 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. \`tf.GraphKeys.GLOBAL_VARIABLES\`) for all variables that are created during the construction of a graph. The caller may define additional collections by specifying a new name. - `},{name:"IndexedSlices",path:"./tf/IndexedSlices.md",desc:"A sparse representation of a set of tensor slices at given indices.",type:"Classes",docs:"A sparse representation of a set of tensor slices at given indices.\n\n This class is a simple wrapper for a pair of `Tensor` objects:\n\n * `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.\n * `indices`: A 1-D integer `Tensor` with shape `[D0]`.\n\n An `IndexedSlices` is typically used to represent a subset of a larger\n tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.\n The values in `indices` are the indices in the first dimension of\n the slices that have been extracted from the larger tensor.\n\n The dense tensor `dense` represented by an `IndexedSlices` `slices` has\n\n ```python\n dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]\n ```\n\n The `IndexedSlices` class is used principally in the definition of\n gradients for operations that have sparse gradients\n (e.g. `tf.gather`).\n\n >>> v = tf.Variable([[0.,1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]])\n >>> with tf.GradientTape() as tape:\n ... r = tf.gather(v, [1,3])\n >>> index_slices = tape.gradient(r,v)\n >>> index_slices\n <...IndexedSlices object ...>\n >>> index_slices.indices.numpy()\n array([1, 3], dtype=int32)\n >>> index_slices.values.numpy()\n array([[1., 1., 1.],\n [1., 1., 1.]], dtype=float32)\n\n Contrast this representation with\n `tf.sparse.SparseTensor`,\n which uses multi-dimensional indices and scalar values.\n "},{name:"IndexedSlicesSpec",path:"./tf/IndexedSlicesSpec.md",desc:'Type specification for a tf.IndexedSlices.',type:"Classes",docs:"Type specification for a `tf.IndexedSlices`."},{name:"Module",path:"./tf/Module.md",desc:"Base neural network module class.",type:"Classes",docs:`Base neural network module class. + `},{name:"IndexedSlices",path:"./tf/IndexedSlices.md",desc:"A sparse representation of a set of tensor slices at given indices.",type:"Classes",docs:"A sparse representation of a set of tensor slices at given indices.\n\n This class is a simple wrapper for a pair of `Tensor` objects:\n\n * `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.\n * `indices`: A 1-D integer `Tensor` with shape `[D0]`.\n\n An `IndexedSlices` is typically used to represent a subset of a larger\n tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.\n The values in `indices` are the indices in the first dimension of\n the slices that have been extracted from the larger tensor.\n\n The dense tensor `dense` represented by an `IndexedSlices` `slices` has\n\n ```python\n dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]\n ```\n\n The `IndexedSlices` class is used principally in the definition of\n gradients for operations that have sparse gradients\n (e.g. `tf.gather`).\n\n ```>>> v = tf.Variable([[0.,1, 2], [2, 3, 4], [4, 5, 6], [6, 7, 8]])\n >>> with tf.GradientTape() as tape:\n ... r = tf.gather(v, [1,3])\n >>> index_slices = tape.gradient(r,v)\n >>> index_slices\n <...IndexedSlices object ...>\n >>> index_slices.indices.numpy()\n array([1, 3], dtype=int32)\n >>> index_slices.values.numpy()\n array([[1., 1., 1.],\n [1., 1., 1.]], dtype=float32)\n\n```\n Contrast this representation with\n `tf.sparse.SparseTensor`,\n which uses multi-dimensional indices and scalar values.\n "},{name:"IndexedSlicesSpec",path:"./tf/IndexedSlicesSpec.md",desc:'Type specification for a tf.IndexedSlices.',type:"Classes",docs:"Type specification for a `tf.IndexedSlices`."},{name:"Module",path:"./tf/Module.md",desc:"Base neural network module class.",type:"Classes",docs:`Base neural network module class. A module is a named container for \`tf.Variable\`s, other \`tf.Module\`s and functions which apply to user input. For example a dense layer in a neural network might be implemented as a \`tf.Module\`: - >>> class Dense(tf.Module): + \`\`\`>>> class Dense(tf.Module): ... def __init__(self, input_dim, output_size, name=None): ... super(Dense, self).__init__(name=name) ... self.w = tf.Variable( @@ -896,22 +890,25 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. ... y = tf.matmul(x, self.w) + self.b ... return tf.nn.relu(y) +\`\`\` You can use the Dense layer as you would expect: - >>> d = Dense(input_dim=3, output_size=2) + \`\`\`>>> d = Dense(input_dim=3, output_size=2) >>> d(tf.ones([1, 3])) +\`\`\` By subclassing \`tf.Module\` instead of \`object\` any \`tf.Variable\` or \`tf.Module\` instances assigned to object properties can be collected using the \`variables\`, \`trainable_variables\` or \`submodules\` property: - >>> d.variables + \`\`\`>>> d.variables (, ) +\`\`\` Subclasses of \`tf.Module\` can also take advantage of the \`_flatten\` method which can be used to implement tracking of any other types. @@ -924,7 +921,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. \`with self.name_scope:\` or you can annotate methods (apart from \`__init__\`) with \`@tf.Module.with_name_scope\`. - >>> class MLP(tf.Module): + \`\`\`>>> class MLP(tf.Module): ... def __init__(self, input_size, sizes, name=None): ... super(MLP, self).__init__(name=name) ... self.layers = [] @@ -938,6 +935,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. ... x = layer(x) ... return x +\`\`\` >>> module = MLP(input_size=5, sizes=[5, 5]) >>> module.variables (, @@ -951,7 +949,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. For instance, \`tf.OptionalSpec\` can be used to define a tf.function that takes \`tf.experimental.Optional\` as an input argument: - >>> @tf.function(input_signature=[tf.OptionalSpec( + \`\`\`>>> @tf.function(input_signature=[tf.OptionalSpec( ... tf.TensorSpec(shape=(), dtype=tf.int32, name=None))]) ... def maybe_square(optional): ... if optional.has_value(): @@ -962,196 +960,11 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> print(maybe_square(optional)) tf.Tensor(25, shape=(), dtype=int32) +\`\`\` Attributes: element_spec: A (nested) structure of \`TypeSpec\` objects that represents the type specification of the optional element. - `},{name:"RaggedTensor",path:"./tf/RaggedTensor.md",desc:"Represents a ragged tensor.",type:"Classes",docs:`Represents a ragged tensor. - - A \`RaggedTensor\` is a tensor with one or more *ragged dimensions*, which are - dimensions whose slices may have different lengths. For example, the inner - (column) dimension of \`rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]\` is ragged, - since the column slices (\`rt[0, :]\`, ..., \`rt[4, :]\`) have different lengths. - Dimensions whose slices all have the same length are called *uniform - dimensions*. The outermost dimension of a \`RaggedTensor\` is always uniform, - since it consists of a single slice (and so there is no possibility for - differing slice lengths). - - The total number of dimensions in a \`RaggedTensor\` is called its *rank*, - and the number of ragged dimensions in a \`RaggedTensor\` is called its - *ragged-rank*. A \`RaggedTensor\`'s ragged-rank is fixed at graph creation - time: it can't depend on the runtime values of \`Tensor\`s, and can't vary - dynamically for different session runs. - - Note that the \`__init__\` constructor is private. Please use one of the - following methods to construct a \`RaggedTensor\`: - - * \`tf.RaggedTensor.from_row_lengths\` - * \`tf.RaggedTensor.from_value_rowids\` - * \`tf.RaggedTensor.from_row_splits\` - * \`tf.RaggedTensor.from_row_starts\` - * \`tf.RaggedTensor.from_row_limits\` - * \`tf.RaggedTensor.from_nested_row_splits\` - * \`tf.RaggedTensor.from_nested_row_lengths\` - * \`tf.RaggedTensor.from_nested_value_rowids\` - - ### Potentially Ragged Tensors - - Many ops support both \`Tensor\`s and \`RaggedTensor\`s - (see [tf.ragged](https://www.tensorflow.org/api_docs/python/tf/ragged) for a - full listing). The term "potentially ragged tensor" may be used to refer to a - tensor that might be either a \`Tensor\` or a \`RaggedTensor\`. The ragged-rank - of a \`Tensor\` is zero. - - ### Documenting RaggedTensor Shapes - - When documenting the shape of a RaggedTensor, ragged dimensions can be - indicated by enclosing them in parentheses. For example, the shape of - a 3-D \`RaggedTensor\` that stores the fixed-size word embedding for each - word in a sentence, for each sentence in a batch, could be written as - \`[num_sentences, (num_words), embedding_size]\`. The parentheses around - \`(num_words)\` indicate that dimension is ragged, and that the length - of each element list in that dimension may vary for each item. - - ### Component Tensors - - Internally, a \`RaggedTensor\` consists of a concatenated list of values that - are partitioned into variable-length rows. In particular, each \`RaggedTensor\` - consists of: - - * A \`values\` tensor, which concatenates the variable-length rows into a - flattened list. For example, the \`values\` tensor for - \`[[3, 1, 4, 1], [], [5, 9, 2], [6], []]\` is \`[3, 1, 4, 1, 5, 9, 2, 6]\`. - - * A \`row_splits\` vector, which indicates how those flattened values are - divided into rows. In particular, the values for row \`rt[i]\` are stored - in the slice \`rt.values[rt.row_splits[i]:rt.row_splits[i+1]]\`. - - Example: - - >>> print(tf.RaggedTensor.from_row_splits( - ... values=[3, 1, 4, 1, 5, 9, 2, 6], - ... row_splits=[0, 4, 4, 7, 8, 8])) - - - ### Alternative Row-Partitioning Schemes - - In addition to \`row_splits\`, ragged tensors provide support for five other - row-partitioning schemes: - - * \`row_lengths\`: a vector with shape \`[nrows]\`, which specifies the length - of each row. - - * \`value_rowids\` and \`nrows\`: \`value_rowids\` is a vector with shape - \`[nvals]\`, corresponding one-to-one with \`values\`, which specifies - each value's row index. In particular, the row \`rt[row]\` consists of the - values \`rt.values[j]\` where \`value_rowids[j]==row\`. \`nrows\` is an - integer scalar that specifies the number of rows in the - \`RaggedTensor\`. (\`nrows\` is used to indicate trailing empty rows.) - - * \`row_starts\`: a vector with shape \`[nrows]\`, which specifies the start - offset of each row. Equivalent to \`row_splits[:-1]\`. - - * \`row_limits\`: a vector with shape \`[nrows]\`, which specifies the stop - offset of each row. Equivalent to \`row_splits[1:]\`. - - * \`uniform_row_length\`: A scalar tensor, specifying the length of every - row. This row-partitioning scheme may only be used if all rows have - the same length. - - Example: The following ragged tensors are equivalent, and all represent the - nested list \`[[3, 1, 4, 1], [], [5, 9, 2], [6], []]\`. - - >>> values = [3, 1, 4, 1, 5, 9, 2, 6] - >>> RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8]) - - >>> RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0]) - - >>> RaggedTensor.from_value_rowids( - ... values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5) - - >>> RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8]) - - >>> RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8]) - - >>> RaggedTensor.from_uniform_row_length(values, uniform_row_length=2) - - - ### Multiple Ragged Dimensions - - \`RaggedTensor\`s with multiple ragged dimensions can be defined by using - a nested \`RaggedTensor\` for the \`values\` tensor. Each nested \`RaggedTensor\` - adds a single ragged dimension. - - >>> inner_rt = RaggedTensor.from_row_splits( # =rt1 from above - ... values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8]) - >>> outer_rt = RaggedTensor.from_row_splits( - ... values=inner_rt, row_splits=[0, 3, 3, 5]) - >>> print(outer_rt.to_list()) - [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]] - >>> print(outer_rt.ragged_rank) - 2 - - The factory function \`RaggedTensor.from_nested_row_splits\` may be used to - construct a \`RaggedTensor\` with multiple ragged dimensions directly, by - providing a list of \`row_splits\` tensors: - - >>> RaggedTensor.from_nested_row_splits( - ... flat_values=[3, 1, 4, 1, 5, 9, 2, 6], - ... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list() - [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]] - - ### Uniform Inner Dimensions - - \`RaggedTensor\`s with uniform inner dimensions can be defined - by using a multidimensional \`Tensor\` for \`values\`. - - >>> rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3], tf.int32), - ... row_splits=[0, 2, 5]) - >>> print(rt.to_list()) - [[[1, 1, 1], [1, 1, 1]], - [[1, 1, 1], [1, 1, 1], [1, 1, 1]]] - >>> print(rt.shape) - (2, None, 3) - - ### Uniform Outer Dimensions - - \`RaggedTensor\`s with uniform outer dimensions can be defined by using - one or more \`RaggedTensor\` with a \`uniform_row_length\` row-partitioning - tensor. For example, a \`RaggedTensor\` with shape \`[2, 2, None]\` can be - constructed with this method from a \`RaggedTensor\` values with shape - \`[4, None]\`: - - >>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]]) - >>> print(values.shape) - (4, None) - >>> rt6 = tf.RaggedTensor.from_uniform_row_length(values, 2) - >>> print(rt6) - - >>> print(rt6.shape) - (2, 2, None) - - Note that \`rt6\` only contains one ragged dimension (the innermost - dimension). In contrast, if \`from_row_splits\` is used to construct a similar - \`RaggedTensor\`, then that \`RaggedTensor\` will have two ragged dimensions: - - >>> rt7 = tf.RaggedTensor.from_row_splits(values, [0, 2, 4]) - >>> print(rt7.shape) - (2, None, None) - - Uniform and ragged outer dimensions may be interleaved, meaning that a - tensor with any combination of ragged and uniform dimensions may be created. - For example, a RaggedTensor \`t4\` with shape \`[3, None, 4, 8, None, 2]\` could - be constructed as follows: - - \`\`\`python - t0 = tf.zeros([1000, 2]) # Shape: [1000, 2] - t1 = RaggedTensor.from_row_lengths(t0, [...]) # [160, None, 2] - t2 = RaggedTensor.from_uniform_row_length(t1, 8) # [20, 8, None, 2] - t3 = RaggedTensor.from_uniform_row_length(t2, 4) # [5, 4, 8, None, 2] - t4 = RaggedTensor.from_row_lengths(t3, [...]) # [3, None, 4, 8, None, 2] - \`\`\` - - `},{name:"RaggedTensorSpec",path:"./tf/RaggedTensorSpec.md",desc:'Type specification for a tf.RaggedTensor.',type:"Classes",docs:"Type specification for a `tf.RaggedTensor`."},{name:"RegisterGradient",path:"./tf/RegisterGradient.md",desc:"A decorator for registering the gradient function for an op type.",type:"Classes",docs:'A decorator for registering the gradient function for an op type.\n\n This decorator is only used when defining a new op type. For an op\n with `m` inputs and `n` outputs, the gradient function is a function\n that takes the original `Operation` and `n` `Tensor` objects\n (representing the gradients with respect to each output of the op),\n and returns `m` `Tensor` objects (representing the partial gradients\n with respect to each input of the op).\n\n For example, assuming that operations of type `"Sub"` take two\n inputs `x` and `y`, and return a single output `x - y`, the\n following gradient function would be registered:\n\n ```python\n @tf.RegisterGradient("Sub")\n def _sub_grad(unused_op, grad):\n return grad, tf.negative(grad)\n ```\n\n The decorator argument `op_type` is the string type of an\n operation. This corresponds to the `OpDef.name` field for the proto\n that defines the operation.\n '},{name:"SparseTensor",path:"./tf/sparse/SparseTensor.md",desc:"Represents a sparse tensor.",type:"Classes",docs:"Represents a sparse tensor.\n\n TensorFlow represents a sparse tensor as three separate dense tensors:\n `indices`, `values`, and `dense_shape`. In Python, the three tensors are\n collected into a `SparseTensor` class for ease of use. If you have separate\n `indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`\n object before passing to the ops below.\n\n Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`\n comprises the following components, where `N` and `ndims` are the number\n of values and number of dimensions in the `SparseTensor`, respectively:\n\n * `indices`: A 2-D int64 tensor of shape `[N, ndims]`, which specifies the\n indices of the elements in the sparse tensor that contain nonzero values\n (elements are zero-indexed). For example, `indices=[[1,3], [2,4]]` specifies\n that the elements with indexes of [1,3] and [2,4] have nonzero values.\n\n * `values`: A 1-D tensor of any type and shape `[N]`, which supplies the\n values for each element in `indices`. For example, given `indices=[[1,3],\n [2,4]]`, the parameter `values=[18, 3.6]` specifies that element [1,3] of\n the sparse tensor has a value of 18, and element [2,4] of the tensor has a\n value of 3.6.\n\n * `dense_shape`: A 1-D int64 tensor of shape `[ndims]`, which specifies the\n dense_shape of the sparse tensor. Takes a list indicating the number of\n elements in each dimension. For example, `dense_shape=[3,6]` specifies a\n two-dimensional 3x6 tensor, `dense_shape=[2,3,4]` specifies a\n three-dimensional 2x3x4 tensor, and `dense_shape=[9]` specifies a\n one-dimensional tensor with 9 elements.\n\n The corresponding dense tensor satisfies:\n\n ```python\n dense.shape = dense_shape\n dense[tuple(indices[i])] = values[i]\n ```\n\n By convention, `indices` should be sorted in row-major order (or equivalently\n lexicographic order on the tuples `indices[i]`). This is not enforced when\n `SparseTensor` objects are constructed, but most ops assume correct ordering.\n If the ordering of sparse tensor `st` is wrong, a fixed version can be\n obtained by calling `tf.sparse.reorder(st)`.\n\n Example: The sparse tensor\n\n ```python\n SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])\n ```\n\n represents the dense tensor\n\n ```python\n [[1, 0, 0, 0]\n [0, 0, 2, 0]\n [0, 0, 0, 0]]\n ```\n "},{name:"SparseTensorSpec",path:"./tf/SparseTensorSpec.md",desc:'Type specification for a tf.sparse.SparseTensor.',type:"Classes",docs:"Type specification for a `tf.sparse.SparseTensor`."},{name:"Tensor",path:"./tf/Tensor.md",desc:'A tf.Tensor represents a multidimensional array of elements.',type:"Classes",docs:`A \`tf.Tensor\` represents a multidimensional array of elements. + `},{name:"RaggedTensor",path:"./tf/RaggedTensor.md",desc:"Represents a ragged tensor.",type:"Classes",docs:"Represents a ragged tensor.\n\n A `RaggedTensor` is a tensor with one or more *ragged dimensions*, which are\n dimensions whose slices may have different lengths. For example, the inner\n (column) dimension of `rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is ragged,\n since the column slices (`rt[0, :]`, ..., `rt[4, :]`) have different lengths.\n Dimensions whose slices all have the same length are called *uniform\n dimensions*. The outermost dimension of a `RaggedTensor` is always uniform,\n since it consists of a single slice (and so there is no possibility for\n differing slice lengths).\n\n The total number of dimensions in a `RaggedTensor` is called its *rank*,\n and the number of ragged dimensions in a `RaggedTensor` is called its\n *ragged-rank*. A `RaggedTensor`'s ragged-rank is fixed at graph creation\n time: it can't depend on the runtime values of `Tensor`s, and can't vary\n dynamically for different session runs.\n\n Note that the `__init__` constructor is private. Please use one of the\n following methods to construct a `RaggedTensor`:\n\n * `tf.RaggedTensor.from_row_lengths`\n * `tf.RaggedTensor.from_value_rowids`\n * `tf.RaggedTensor.from_row_splits`\n * `tf.RaggedTensor.from_row_starts`\n * `tf.RaggedTensor.from_row_limits`\n * `tf.RaggedTensor.from_nested_row_splits`\n * `tf.RaggedTensor.from_nested_row_lengths`\n * `tf.RaggedTensor.from_nested_value_rowids`\n\n ### Potentially Ragged Tensors\n\n Many ops support both `Tensor`s and `RaggedTensor`s\n (see [tf.ragged](https://www.tensorflow.org/api_docs/python/tf/ragged) for a\n full listing). The term \"potentially ragged tensor\" may be used to refer to a\n tensor that might be either a `Tensor` or a `RaggedTensor`. The ragged-rank\n of a `Tensor` is zero.\n\n ### Documenting RaggedTensor Shapes\n\n When documenting the shape of a RaggedTensor, ragged dimensions can be\n indicated by enclosing them in parentheses. For example, the shape of\n a 3-D `RaggedTensor` that stores the fixed-size word embedding for each\n word in a sentence, for each sentence in a batch, could be written as\n `[num_sentences, (num_words), embedding_size]`. The parentheses around\n `(num_words)` indicate that dimension is ragged, and that the length\n of each element list in that dimension may vary for each item.\n\n ### Component Tensors\n\n Internally, a `RaggedTensor` consists of a concatenated list of values that\n are partitioned into variable-length rows. In particular, each `RaggedTensor`\n consists of:\n\n * A `values` tensor, which concatenates the variable-length rows into a\n flattened list. For example, the `values` tensor for\n `[[3, 1, 4, 1], [], [5, 9, 2], [6], []]` is `[3, 1, 4, 1, 5, 9, 2, 6]`.\n\n * A `row_splits` vector, which indicates how those flattened values are\n divided into rows. In particular, the values for row `rt[i]` are stored\n in the slice `rt.values[rt.row_splits[i]:rt.row_splits[i+1]]`.\n\n Example:\n\n ```>>> print(tf.RaggedTensor.from_row_splits(\n ... values=[3, 1, 4, 1, 5, 9, 2, 6],\n ... row_splits=[0, 4, 4, 7, 8, 8]))\n \n\n```\n ### Alternative Row-Partitioning Schemes\n\n In addition to `row_splits`, ragged tensors provide support for five other\n row-partitioning schemes:\n\n * `row_lengths`: a vector with shape `[nrows]`, which specifies the length\n of each row.\n\n * `value_rowids` and `nrows`: `value_rowids` is a vector with shape\n `[nvals]`, corresponding one-to-one with `values`, which specifies\n each value's row index. In particular, the row `rt[row]` consists of the\n values `rt.values[j]` where `value_rowids[j]==row`. `nrows` is an\n integer scalar that specifies the number of rows in the\n `RaggedTensor`. (`nrows` is used to indicate trailing empty rows.)\n\n * `row_starts`: a vector with shape `[nrows]`, which specifies the start\n offset of each row. Equivalent to `row_splits[:-1]`.\n\n * `row_limits`: a vector with shape `[nrows]`, which specifies the stop\n offset of each row. Equivalent to `row_splits[1:]`.\n\n * `uniform_row_length`: A scalar tensor, specifying the length of every\n row. This row-partitioning scheme may only be used if all rows have\n the same length.\n\n Example: The following ragged tensors are equivalent, and all represent the\n nested list `[[3, 1, 4, 1], [], [5, 9, 2], [6], []]`.\n\n ```>>> values = [3, 1, 4, 1, 5, 9, 2, 6]\n >>> RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])\n \n >>> RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])\n \n >>> RaggedTensor.from_value_rowids(\n ... values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5)\n \n >>> RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])\n \n >>> RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])\n \n >>> RaggedTensor.from_uniform_row_length(values, uniform_row_length=2)\n \n\n```\n ### Multiple Ragged Dimensions\n\n `RaggedTensor`s with multiple ragged dimensions can be defined by using\n a nested `RaggedTensor` for the `values` tensor. Each nested `RaggedTensor`\n adds a single ragged dimension.\n\n ```>>> inner_rt = RaggedTensor.from_row_splits( # =rt1 from above\n ... values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])\n >>> outer_rt = RaggedTensor.from_row_splits(\n ... values=inner_rt, row_splits=[0, 3, 3, 5])\n >>> print(outer_rt.to_list())\n [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]\n >>> print(outer_rt.ragged_rank)\n 2\n\n```\n The factory function `RaggedTensor.from_nested_row_splits` may be used to\n construct a `RaggedTensor` with multiple ragged dimensions directly, by\n providing a list of `row_splits` tensors:\n\n ```>>> RaggedTensor.from_nested_row_splits(\n ... flat_values=[3, 1, 4, 1, 5, 9, 2, 6],\n ... nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list()\n [[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]\n\n```\n ### Uniform Inner Dimensions\n\n `RaggedTensor`s with uniform inner dimensions can be defined\n by using a multidimensional `Tensor` for `values`.\n\n ```>>> rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3], tf.int32),\n ... row_splits=[0, 2, 5])\n >>> print(rt.to_list())\n [[[1, 1, 1], [1, 1, 1]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]\n >>> print(rt.shape)\n (2, None, 3)\n\n```\n ### Uniform Outer Dimensions\n\n `RaggedTensor`s with uniform outer dimensions can be defined by using\n one or more `RaggedTensor` with a `uniform_row_length` row-partitioning\n tensor. For example, a `RaggedTensor` with shape `[2, 2, None]` can be\n constructed with this method from a `RaggedTensor` values with shape\n `[4, None]`:\n\n ```>>> values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]])\n >>> print(values.shape)\n (4, None)\n >>> rt6 = tf.RaggedTensor.from_uniform_row_length(values, 2)\n >>> print(rt6)\n \n >>> print(rt6.shape)\n (2, 2, None)\n\n```\n Note that `rt6` only contains one ragged dimension (the innermost\n dimension). In contrast, if `from_row_splits` is used to construct a similar\n `RaggedTensor`, then that `RaggedTensor` will have two ragged dimensions:\n\n ```>>> rt7 = tf.RaggedTensor.from_row_splits(values, [0, 2, 4])\n >>> print(rt7.shape)\n (2, None, None)\n\n```\n Uniform and ragged outer dimensions may be interleaved, meaning that a\n tensor with any combination of ragged and uniform dimensions may be created.\n For example, a RaggedTensor `t4` with shape `[3, None, 4, 8, None, 2]` could\n be constructed as follows:\n\n ```python\n t0 = tf.zeros([1000, 2]) # Shape: [1000, 2]\n t1 = RaggedTensor.from_row_lengths(t0, [...]) # [160, None, 2]\n t2 = RaggedTensor.from_uniform_row_length(t1, 8) # [20, 8, None, 2]\n t3 = RaggedTensor.from_uniform_row_length(t2, 4) # [5, 4, 8, None, 2]\n t4 = RaggedTensor.from_row_lengths(t3, [...]) # [3, None, 4, 8, None, 2]\n ```\n\n "},{name:"RaggedTensorSpec",path:"./tf/RaggedTensorSpec.md",desc:'Type specification for a tf.RaggedTensor.',type:"Classes",docs:"Type specification for a `tf.RaggedTensor`."},{name:"RegisterGradient",path:"./tf/RegisterGradient.md",desc:"A decorator for registering the gradient function for an op type.",type:"Classes",docs:'A decorator for registering the gradient function for an op type.\n\n This decorator is only used when defining a new op type. For an op\n with `m` inputs and `n` outputs, the gradient function is a function\n that takes the original `Operation` and `n` `Tensor` objects\n (representing the gradients with respect to each output of the op),\n and returns `m` `Tensor` objects (representing the partial gradients\n with respect to each input of the op).\n\n For example, assuming that operations of type `"Sub"` take two\n inputs `x` and `y`, and return a single output `x - y`, the\n following gradient function would be registered:\n\n ```python\n @tf.RegisterGradient("Sub")\n def _sub_grad(unused_op, grad):\n return grad, tf.negative(grad)\n ```\n\n The decorator argument `op_type` is the string type of an\n operation. This corresponds to the `OpDef.name` field for the proto\n that defines the operation.\n '},{name:"SparseTensor",path:"./tf/sparse/SparseTensor.md",desc:"Represents a sparse tensor.",type:"Classes",docs:"Represents a sparse tensor.\n\n TensorFlow represents a sparse tensor as three separate dense tensors:\n `indices`, `values`, and `dense_shape`. In Python, the three tensors are\n collected into a `SparseTensor` class for ease of use. If you have separate\n `indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`\n object before passing to the ops below.\n\n Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`\n comprises the following components, where `N` and `ndims` are the number\n of values and number of dimensions in the `SparseTensor`, respectively:\n\n * `indices`: A 2-D int64 tensor of shape `[N, ndims]`, which specifies the\n indices of the elements in the sparse tensor that contain nonzero values\n (elements are zero-indexed). For example, `indices=[[1,3], [2,4]]` specifies\n that the elements with indexes of [1,3] and [2,4] have nonzero values.\n\n * `values`: A 1-D tensor of any type and shape `[N]`, which supplies the\n values for each element in `indices`. For example, given `indices=[[1,3],\n [2,4]]`, the parameter `values=[18, 3.6]` specifies that element [1,3] of\n the sparse tensor has a value of 18, and element [2,4] of the tensor has a\n value of 3.6.\n\n * `dense_shape`: A 1-D int64 tensor of shape `[ndims]`, which specifies the\n dense_shape of the sparse tensor. Takes a list indicating the number of\n elements in each dimension. For example, `dense_shape=[3,6]` specifies a\n two-dimensional 3x6 tensor, `dense_shape=[2,3,4]` specifies a\n three-dimensional 2x3x4 tensor, and `dense_shape=[9]` specifies a\n one-dimensional tensor with 9 elements.\n\n The corresponding dense tensor satisfies:\n\n ```python\n dense.shape = dense_shape\n dense[tuple(indices[i])] = values[i]\n ```\n\n By convention, `indices` should be sorted in row-major order (or equivalently\n lexicographic order on the tuples `indices[i]`). This is not enforced when\n `SparseTensor` objects are constructed, but most ops assume correct ordering.\n If the ordering of sparse tensor `st` is wrong, a fixed version can be\n obtained by calling `tf.sparse.reorder(st)`.\n\n Example: The sparse tensor\n\n ```python\n SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])\n ```\n\n represents the dense tensor\n\n ```python\n [[1, 0, 0, 0]\n [0, 0, 2, 0]\n [0, 0, 0, 0]]\n ```\n "},{name:"SparseTensorSpec",path:"./tf/SparseTensorSpec.md",desc:'Type specification for a tf.sparse.SparseTensor.',type:"Classes",docs:"Type specification for a `tf.sparse.SparseTensor`."},{name:"Tensor",path:"./tf/Tensor.md",desc:'A tf.Tensor represents a multidimensional array of elements.',type:"Classes",docs:`A \`tf.Tensor\` represents a multidimensional array of elements. All elements are of a single known data type. @@ -1171,7 +984,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. TensorFlow defaults to eager execution. In the example below, the matrix multiplication results are calculated immediately. - >>> # Compute some values using a Tensor + \`\`\`>>> # Compute some values using a Tensor >>> c = tf.constant([[1.0, 2.0], [3.0, 4.0]]) >>> d = tf.constant([[1.0, 1.0], [0.0, 1.0]]) >>> e = tf.matmul(c, d) @@ -1180,16 +993,18 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. [[1. 3.] [3. 7.]], shape=(2, 2), dtype=float32) +\`\`\` Note that during eager execution, you may discover your \`Tensors\` are actually of type \`EagerTensor\`. This is an internal detail, but it does give you access to a useful function, \`numpy\`: - >>> type(e) + \`\`\`>>> type(e) >>> print(e.numpy()) [[1. 3.] [3. 7.]] +\`\`\` In TensorFlow, \`tf.function\`s are a common way to define graph execution. A Tensor's shape (that is, the rank of the Tensor and the size of @@ -1227,7 +1042,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. Example 1: Plain reading and writing. - >>> ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False) + \`\`\`>>> ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False) >>> ta = ta.write(0, 10) >>> ta = ta.write(1, 20) >>> ta = ta.write(2, 30) @@ -1242,9 +1057,10 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. +\`\`\` Example 2: Fibonacci sequence algorithm that writes in a loop then returns. - >>> @tf.function + \`\`\`>>> @tf.function ... def fibonacci(n): ... ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True) ... ta = ta.unstack([0., 1.]) @@ -1258,6 +1074,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. +\`\`\` Example 3: A simple loop interacting with a \`tf.Variable\`. >>> v = tf.Variable(1) @@ -1318,24 +1135,26 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. and shape of the variable. After construction, the type and shape of the variable are fixed. The value can be changed using one of the assign methods. - >>> v = tf.Variable(1.) + \`\`\`>>> v = tf.Variable(1.) >>> v.assign(2.) >>> v.assign_add(0.5) +\`\`\` The \`shape\` argument to \`Variable\`'s constructor allows you to construct a variable with a less defined shape than its \`initial_value\`: - >>> v = tf.Variable(1., shape=tf.TensorShape(None)) + \`\`\`>>> v = tf.Variable(1., shape=tf.TensorShape(None)) >>> v.assign([[1.]]) dtype=float32, numpy=array([[1.]], ...)> +\`\`\` Just like any \`Tensor\`, variables created with \`Variable()\` can be used as inputs to operations. Additionally, all the operators overloaded for the \`Tensor\` class are carried over to variables. - >>> w = tf.Variable([[1.], [2.]]) + \`\`\`>>> w = tf.Variable([[1.], [2.]]) >>> x = tf.constant([[3., 4.]]) >>> tf.matmul(w, x) >> tf.sigmoid(w + x) +\`\`\` When building a machine learning model it is often convenient to distinguish between variables holding trainable model parameters and other variables such as a \`step\` variable used to count training steps. To make this easier, the variable constructor supports a \`trainable=\` parameter. \`tf.GradientTape\` watches trainable variables by default: - >>> with tf.GradientTape(persistent=True) as tape: + \`\`\`>>> with tf.GradientTape(persistent=True) as tape: ... trainable = tf.Variable(1.) ... non_trainable = tf.Variable(2., trainable=False) ... x1 = trainable * 2. @@ -1359,14 +1179,16 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> assert tape.gradient(x2, non_trainable) is None # Unwatched +\`\`\` Variables are automatically tracked when assigned to attributes of types inheriting from \`tf.Module\`. - >>> m = tf.Module() + \`\`\`>>> m = tf.Module() >>> m.v = tf.Variable([1.]) >>> m.trainable_variables (,) +\`\`\` This tracking then allows saving variable values to [training checkpoints](https://www.tensorflow.org/guide/checkpoint), or to [SavedModels](https://www.tensorflow.org/guide/saved_model) which include @@ -1375,17 +1197,18 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. Variables are often captured and manipulated by \`tf.function\`s. This works the same way the un-decorated function would have: - >>> v = tf.Variable(0.) + \`\`\`>>> v = tf.Variable(0.) >>> read_and_decrement = tf.function(lambda: v.assign_sub(0.1)) >>> read_and_decrement() >>> read_and_decrement() +\`\`\` Variables created inside a \`tf.function\` must be owned outside the function and be created only once: - >>> class M(tf.Module): + \`\`\`>>> class M(tf.Module): ... @tf.function ... def __call__(self, x): ... if not hasattr(self, "v"): # Or set self.v to None in __init__ @@ -1399,6 +1222,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> m.v +\`\`\` See the \`tf.function\` documentation for details. `},{name:"VariableAggregation",path:"./tf/VariableAggregation.md",desc:"Indicates how a distributed variable will be aggregated.",type:"Classes",docs:`Indicates how a distributed variable will be aggregated. @@ -1454,7 +1278,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. Examples: - >>> def make_variables(k, initializer): + \`\`\`>>> def make_variables(k, initializer): ... return (tf.Variable(initializer(shape=[k], dtype=tf.float32)), ... tf.Variable(initializer(shape=[k, k], dtype=tf.float32))) >>> v1, v2 = make_variables(3, tf.constant_initializer(2.)) @@ -1468,7 +1292,8 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.)) (, >> value = [0, 1, 2, 3, 4, 5, 6, 7] +\`\`\` + \`\`\`>>> value = [0, 1, 2, 3, 4, 5, 6, 7] >>> init = tf.constant_initializer(value) >>> # Fitting shape >>> tf.Variable(init(shape=[2, 4], dtype=tf.float32)) @@ -1486,6 +1311,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. ... TypeError: ...value has 8 elements, shape is (2, 3) with 6 elements... +\`\`\` Args: value: A Python scalar, list or tuple of values, or a N-dimensional numpy array. All elements of the initialized variable will be set to the @@ -1522,7 +1348,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. Examples: - >>> def make_variables(k, initializer): + \`\`\`>>> def make_variables(k, initializer): ... return (tf.Variable(initializer(shape=[k], dtype=tf.float32)), ... tf.Variable(initializer(shape=[k, k], dtype=tf.float32))) >>> v1, v2 = make_variables(3, @@ -1535,6 +1361,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.)) (, >> def make_variables(k, initializer): + \`\`\`>>> def make_variables(k, initializer): ... return (tf.Variable(initializer(shape=[k], dtype=tf.float32)), ... tf.Variable(initializer(shape=[k, k], dtype=tf.float32))) >>> v1, v2 = make_variables(3, tf.ones_initializer()) @@ -1565,6 +1392,7 @@ https://github.com/tensorflow/community/blob/master/rfcs/20200211-tf-types.md. >>> make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.)) (, >> # real number\n >>> x = tf.constant([-2.25, 3.25])\n >>> tf.abs(x)\n \n\n >>> # complex number\n >>> x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])\n >>> tf.abs(x)\n \n\n Args:\n x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`,\n `int32`, `int64`, `complex64` or `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` or `SparseTensor` of the same size, type and sparsity as `x`,\n with absolute values. Note, for `complex64` or `complex128` input, the\n returned `Tensor` will be of type `float32` or `float64`, respectively.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.abs(x.values, ...), x.dense_shape)`"},{name:"acos",path:"./tf/math/acos.md",desc:"Computes acos of x element-wise.",type:"Functions",docs:"Computes acos of x element-wise.\n\n Provided an input tensor, the `tf.math.acos` operation\n returns the inverse cosine of each element of the tensor.\n If `y = tf.math.cos(x)` then, `x = tf.math.acos(y)`.\n\n Input range is `[-1, 1]` and the output has a range of `[0, pi]`.\n\n For example:\n\n >>> x = tf.constant([1.0, -0.5, 3.4, 0.2, 0.0, -2], dtype = tf.float32)\n >>> tf.math.acos(x)\n \n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`,\n `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`,\n `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as x.\n "},{name:"acosh",path:"./tf/math/acosh.md",desc:"Computes inverse hyperbolic cosine of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic cosine of x element-wise.\n\n Given an input tensor, the function computes inverse hyperbolic cosine of every element.\n Input range is `[1, inf]`. It returns `nan` if the input lies outside the range.\n\n ```python\n x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"add",path:"./tf/math/add.md",desc:"Returns x + y element-wise.",type:"Functions",docs:`Returns x + y element-wise. +Note: The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.`},{name:"abs",path:"./tf/math/abs.md",desc:"Computes the absolute value of a tensor.",type:"Functions",docs:"Computes the absolute value of a tensor.\n\n Given a tensor of integer or floating-point values, this operation returns a\n tensor of the same type, where each element contains the absolute value of the\n corresponding element in the input.\n\n Given a tensor `x` of complex numbers, this operation returns a tensor of type\n `float32` or `float64` that is the absolute value of each element in `x`. For\n a complex number \\\\(a + bj\\\\), its absolute value is computed as\n \\\\(\\sqrt{a^2 + b^2}\\\\).\n\n For example:\n\n ```>>> # real number\n >>> x = tf.constant([-2.25, 3.25])\n >>> tf.abs(x)\n \n\n```\n ```>>> # complex number\n >>> x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])\n >>> tf.abs(x)\n \n\n```\n Args:\n x: A `Tensor` or `SparseTensor` of type `float16`, `float32`, `float64`,\n `int32`, `int64`, `complex64` or `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` or `SparseTensor` of the same size, type and sparsity as `x`,\n with absolute values. Note, for `complex64` or `complex128` input, the\n returned `Tensor` will be of type `float32` or `float64`, respectively.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.abs(x.values, ...), x.dense_shape)`"},{name:"acos",path:"./tf/math/acos.md",desc:"Computes acos of x element-wise.",type:"Functions",docs:"Computes acos of x element-wise.\n\n Provided an input tensor, the `tf.math.acos` operation\n returns the inverse cosine of each element of the tensor.\n If `y = tf.math.cos(x)` then, `x = tf.math.acos(y)`.\n\n Input range is `[-1, 1]` and the output has a range of `[0, pi]`.\n\n For example:\n\n ```>>> x = tf.constant([1.0, -0.5, 3.4, 0.2, 0.0, -2], dtype = tf.float32)\n >>> tf.math.acos(x)\n \n\n```\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`,\n `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`,\n `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as x.\n "},{name:"acosh",path:"./tf/math/acosh.md",desc:"Computes inverse hyperbolic cosine of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic cosine of x element-wise.\n\n Given an input tensor, the function computes inverse hyperbolic cosine of every element.\n Input range is `[1, inf]`. It returns `nan` if the input lies outside the range.\n\n ```python\n x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"add",path:"./tf/math/add.md",desc:"Returns x + y element-wise.",type:"Functions",docs:`Returns x + y element-wise. Example usages below. Add a scalar and a list: - >>> x = [1, 2, 3, 4, 5] + \`\`\`>>> x = [1, 2, 3, 4, 5] >>> y = 1 >>> tf.add(x, y) +\`\`\` Note that binary \`+\` operator can be used instead: - >>> x = tf.convert_to_tensor([1, 2, 3, 4, 5]) + \`\`\`>>> x = tf.convert_to_tensor([1, 2, 3, 4, 5]) >>> y = tf.convert_to_tensor(1) >>> x + y +\`\`\` Add a tensor and a list of same shape: - >>> x = [1, 2, 3, 4, 5] + \`\`\`>>> x = [1, 2, 3, 4, 5] >>> y = tf.constant([1, 2, 3, 4, 5]) >>> tf.add(x, y) +\`\`\` **Warning**: If one of the inputs (\`x\` or \`y\`) is a tensor and the other is a non-tensor, the non-tensor input will adopt (or get casted to) the data type of the tensor input. This can potentially cause unwanted overflow or underflow @@ -1661,11 +1492,12 @@ Note: The output of this function should be used. If it is not, a warning will b For example, - >>> x = tf.constant([1, 2], dtype=tf.int8) + \`\`\`>>> x = tf.constant([1, 2], dtype=tf.int8) >>> y = [2**7 + 1, 2**7 + 2] >>> tf.add(x, y) +\`\`\` When adding two input values of different shapes, \`Add\` follows NumPy broadcasting rules. The two input array shapes are compared element-wise. Starting with the trailing dimensions, the two dimensions either have to be @@ -1673,18 +1505,20 @@ Note: The output of this function should be used. If it is not, a warning will b For example, - >>> x = np.ones(6).reshape(1, 2, 1, 3) + \`\`\`>>> x = np.ones(6).reshape(1, 2, 1, 3) >>> y = np.ones(6).reshape(2, 1, 3, 1) >>> tf.add(x, y).shape.as_list() [2, 2, 3, 3] +\`\`\` Another example with two arrays of different dimension. - >>> x = np.ones([1, 2, 1, 4]) + \`\`\`>>> x = np.ones([1, 2, 1, 4]) >>> y = np.ones([3, 4]) >>> tf.add(x, y).shape.as_list() [1, 2, 3, 4] +\`\`\` The reduction version of this elementwise operation is \`tf.math.reduce_sum\` Args: @@ -1704,13 +1538,14 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> a = tf.constant([[3, 5], [4, 8]]) + \`\`\`>>> a = tf.constant([[3, 5], [4, 8]]) >>> b = tf.constant([[1, 6], [2, 9]]) >>> tf.math.add_n([a, b, a]) +\`\`\` Args: inputs: A list of \`tf.Tensor\` or \`tf.IndexedSlices\` objects, each with the same shape and type. \`tf.IndexedSlices\` objects will be converted into @@ -1729,7 +1564,7 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> A = tf.constant([2, 20, 30, 3, 6]) + \`\`\`>>> A = tf.constant([2, 20, 30, 3, 6]) >>> tf.math.argmax(A) # A[2] is maximum in tensor A >>> B = tf.constant([[2, 20, 30, 3, 6], [3, 11, 16, 1, 8], @@ -1742,6 +1577,7 @@ Note: The output of this function should be used. If it is not, a warning will b >>> tf.math.argmax(C) # Returns smallest index in case of ties +\`\`\` Args: input: A \`Tensor\`. axis: An integer, the axis to reduce across. Default to 0. @@ -1753,21 +1589,23 @@ Note: The output of this function should be used. If it is not, a warning will b A \`Tensor\` of type \`output_type\`. `},{name:"argmin",path:"./tf/math/argmin.md",desc:"Returns the index with the smallest value across axes of a tensor.",type:"Functions",docs:"Returns the index with the smallest value across axes of a tensor.\n\n Returns the smallest index in case of ties.\n\n Args:\n input: A `Tensor`. Must be one of the following types: `float32`, `float64`,\n `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`,\n `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`,\n `uint64`.\n axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n int32 or int64, must be in the range `-rank(input), rank(input))`.\n Describes which axis of the input Tensor to reduce across. For vectors,\n use axis = 0.\n output_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to\n `tf.int64`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `output_type`.\n\n Usage:\n ```python\n import tensorflow as tf\n a = [1, 10, 26.9, 2.8, 166.32, 62.3]\n b = tf.math.argmin(input = a)\n c = tf.keras.backend.eval(b)\n # c = 0\n # here a[0] = 1 which is the smallest element of a across axis 0\n ```\n "},{name:"argsort",path:"./tf/argsort.md",desc:"Returns the indices of a tensor that give its sorted order along an axis.",type:"Functions",docs:`Returns the indices of a tensor that give its sorted order along an axis. - >>> values = [1, 10, 26.9, 2.8, 166.32, 62.3] + \`\`\`>>> values = [1, 10, 26.9, 2.8, 166.32, 62.3] >>> sort_order = tf.argsort(values) >>> sort_order.numpy() array([0, 3, 1, 2, 5, 4], dtype=int32) +\`\`\` For a 1D tensor: - >>> sorted = tf.gather(values, sort_order) + \`\`\`>>> sorted = tf.gather(values, sort_order) >>> assert tf.reduce_all(sorted == tf.sort(values)) +\`\`\` For higher dimensions, the output has the same shape as \`values\`, but along the given axis, values represent the index of the sorted element in that slice of the tensor at the given position. - >>> mat = [[30,20,10], + \`\`\`>>> mat = [[30,20,10], ... [20,10,30], ... [10,30,20]] >>> indices = tf.argsort(mat) @@ -1776,13 +1614,15 @@ Note: The output of this function should be used. If it is not, a warning will b [1, 0, 2], [0, 2, 1]], dtype=int32) +\`\`\` If \`axis=-1\` these indices can be used to apply a sort using \`tf.gather\`: - >>> tf.gather(mat, indices, batch_dims=-1).numpy() + \`\`\`>>> tf.gather(mat, indices, batch_dims=-1).numpy() array([[10, 20, 30], [10, 20, 30], [10, 20, 30]], dtype=int32) +\`\`\` See also: * \`tf.sort\`: Sort along an axis. @@ -1809,82 +1649,7 @@ Note: The output of this function should be used. If it is not, a warning will b ValueError: If axis is not a constant scalar, or the direction is invalid. tf.errors.InvalidArgumentError: If the \`values.dtype\` is not a \`float\` or \`int\` type. - `},{name:"as_dtype",path:"./tf/dtypes/as_dtype.md",desc:"Converts the given `type_value` to a `DType`.",type:"Functions",docs:"Converts the given `type_value` to a `DType`.\n\n Note: `DType` values are interned. When passed a new `DType` object,\n `as_dtype` always returns the interned value.\n\n Args:\n type_value: A value that can be converted to a `tf.DType` object. This may\n currently be a `tf.DType` object, a [`DataType`\n enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),\n a string type name, or a [`numpy.dtype`](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html).\n\n Returns:\n A `DType` corresponding to `type_value`.\n\n Raises:\n TypeError: If `type_value` cannot be converted to a `DType`.\n "},{name:"as_string",path:"./tf/strings/as_string.md",desc:"Converts each entry in the given tensor to strings.",type:"Functions",docs:"Converts each entry in the given tensor to strings.\n\n Supports many numeric types and boolean.\n\n For Unicode, see the\n [https://www.tensorflow.org/tutorials/representation/unicode](Working with Unicode text)\n tutorial.\n\n Examples:\n\n >>> tf.strings.as_string([3, 2])\n \n >>> tf.strings.as_string([3.1415926, 2.71828], precision=2).numpy()\n array([b'3.14', b'2.72'], dtype=object)\n\n Args:\n input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`, `complex64`, `complex128`, `bool`, `variant`.\n precision: An optional `int`. Defaults to `-1`.\n The post-decimal precision to use for floating point numbers.\n Only used if precision > -1.\n scientific: An optional `bool`. Defaults to `False`.\n Use scientific notation for floating point numbers.\n shortest: An optional `bool`. Defaults to `False`.\n Use shortest representation (either scientific or standard) for\n floating point numbers.\n width: An optional `int`. Defaults to `-1`.\n Pad pre-decimal numbers to this width.\n Applies to both floating point and integer numbers.\n Only used if width > -1.\n fill: An optional `string`. Defaults to `\"\"`.\n The value to pad if width > -1. If empty, pads with spaces.\n Another typical value is '0'. String cannot be longer than 1 character.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `string`.\n "},{name:"asin",path:"./tf/math/asin.md",desc:"Computes the trignometric inverse sine of x element-wise.",type:"Functions",docs:"Computes the trignometric inverse sine of x element-wise.\n\n The `tf.math.asin` operation returns the inverse of `tf.math.sin`, such that\n if `y = tf.math.sin(x)` then, `x = tf.math.asin(y)`.\n\n **Note**: The output of `tf.math.asin` will lie within the invertible range\n of sine, i.e [-pi/2, pi/2].\n\n For example:\n\n ```python\n # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]\n x = tf.constant([1.047, 0.785])\n y = tf.math.sin(x) # [0.8659266, 0.7068252]\n\n tf.math.asin(y) # [1.047, 0.785] = x\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"asinh",path:"./tf/math/asinh.md",desc:"Computes inverse hyperbolic sine of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic sine of x element-wise.\n\n Given an input tensor, this function computes inverse hyperbolic sine\n for every element in the tensor. Both input and output has a range of\n `[-inf, inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"assert_equal",path:"./tf/debugging/assert_equal.md",desc:"Assert the condition `x == y` holds element-wise.",type:"Functions",docs:'Assert the condition `x == y` holds element-wise.\n\n This Op checks that `x[i] == y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` and `y` are not equal, `message`, as well as the first `summarize`\n entries of `x` and `y` are printed, and `InvalidArgumentError` is raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_equal".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x == y` is False. This can be\n used with `tf.control_dependencies` inside of `tf.function`s to block\n followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x == y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_greater",path:"./tf/debugging/assert_greater.md",desc:"Assert the condition `x > y` holds element-wise.",type:"Functions",docs:'Assert the condition `x > y` holds element-wise.\n\n This Op checks that `x[i] > y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` is not greater than `y` element-wise, `message`, as well as the first\n `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is\n raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_greater".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x > y` is False. This can be\n used with `tf.control_dependencies` inside of `tf.function`s to block\n followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x > y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_less",path:"./tf/debugging/assert_less.md",desc:"Assert the condition `x < y` holds element-wise.",type:"Functions",docs:'Assert the condition `x < y` holds element-wise.\n\n This Op checks that `x[i] < y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` is not less than `y` element-wise, `message`, as well as the first\n `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is\n raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_less".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x < y` is False.\n This can be used with `tf.control_dependencies` inside of `tf.function`s\n to block followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x < y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_rank",path:"./tf/debugging/assert_rank.md",desc:"Assert that `x` has rank equal to `rank`.",type:"Functions",docs:'Assert that `x` has rank equal to `rank`.\n\n This Op checks that the rank of `x` is equal to `rank`.\n\n If `x` has a different rank, `message`, as well as the shape of `x` are\n printed, and `InvalidArgumentError` is raised.\n\n Args:\n x: `Tensor`.\n rank: Scalar integer `Tensor`.\n message: A string to prefix to the default message.\n name: A name for this operation (optional). Defaults to\n "assert_rank".\n\n Returns:\n Op raising `InvalidArgumentError` unless `x` has specified rank.\n If static checks determine `x` has correct rank, a `no_op` is returned.\n This can be used with `tf.control_dependencies` inside of `tf.function`s\n to block followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x` does not have rank `rank`. The check can be performed immediately\n during eager execution or if the shape of `x` is statically known.\n '},{name:"atan",path:"./tf/math/atan.md",desc:"Computes the trignometric inverse tangent of x element-wise.",type:"Functions",docs:"Computes the trignometric inverse tangent of x element-wise.\n\n The `tf.math.atan` operation returns the inverse of `tf.math.tan`, such that\n if `y = tf.math.tan(x)` then, `x = tf.math.atan(y)`.\n\n **Note**: The output of `tf.math.atan` will lie within the invertible range\n of tan, i.e (-pi/2, pi/2).\n\n For example:\n\n ```python\n # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]\n x = tf.constant([1.047, 0.785])\n y = tf.math.tan(x) # [1.731261, 0.99920404]\n\n tf.math.atan(y) # [1.047, 0.785] = x\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"atan2",path:"./tf/math/atan2.md",desc:"Computes arctangent of `y/x` element-wise, respecting signs of the arguments.",type:"Functions",docs:`Computes arctangent of \`y/x\` element-wise, respecting signs of the arguments. - - This is the angle \\\\( \\theta \\in [-\\pi, \\pi] \\\\) such that - \\\\[ x = r \\cos(\\theta) \\\\] - and - \\\\[ y = r \\sin(\\theta) \\\\] - where \\\\(r = \\sqrt{x^2 + y^2} \\\\). - - For example: - - >>> x = [1., 1.] - >>> y = [1., -1.] - >>> print((tf.math.atan2(y,x) * (180 / np.pi)).numpy()) - [ 45. -45.] - - Args: - y: A \`Tensor\`. Must be one of the following types: \`bfloat16\`, \`half\`, \`float32\`, \`float64\`. - x: A \`Tensor\`. Must have the same type as \`y\`. - name: A name for the operation (optional). - - Returns: - A \`Tensor\`. Has the same type as \`y\`. - `},{name:"atanh",path:"./tf/math/atanh.md",desc:"Computes inverse hyperbolic tangent of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic tangent of x element-wise.\n\n Given an input tensor, this function computes inverse hyperbolic tangent\n for every element in the tensor. Input range is `[-1,1]` and output range is\n `[-inf, inf]`. If input is `-1`, output will be `-inf` and if the\n input is `1`, output will be `inf`. Values outside the range will have\n `nan` as output.\n\n ```python\n x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")])\n tf.math.atanh(x) ==> [nan -inf -0.54930615 inf 0. 0.54930615 nan nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"batch_to_space",path:"./tf/batch_to_space.md",desc:"BatchToSpace for N-D tensors of type T.",type:"Functions",docs:'BatchToSpace for N-D tensors of type T.\n\n This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of\n shape `block_shape + [batch]`, interleaves these blocks back into the grid\n defined by the spatial dimensions `[1, ..., M]`, to obtain a result with the\n same rank as the input. The spatial dimensions of this intermediate result\n are then optionally cropped according to `crops` to produce the output. This\n is the reverse of SpaceToBatch (see `tf.space_to_batch`).\n\n Args:\n input: A N-D `Tensor` with shape `input_shape = [batch] + spatial_shape +\n remaining_shape`, where `spatial_shape` has M dimensions.\n block_shape: A 1-D `Tensor` with shape [M]. Must be one of the following\n types: `int32`, `int64`. All values must be >= 1. For backwards\n compatibility with TF 1.0, this parameter may be an int, in which case it\n is converted to\n `numpy.array([block_shape, block_shape],\n dtype=numpy.int64)`.\n crops: A 2-D `Tensor` with shape `[M, 2]`. Must be one of the\n following types: `int32`, `int64`. All values must be >= 0.\n `crops[i] = [crop_start, crop_end]` specifies the amount to crop from\n input dimension `i + 1`, which corresponds to spatial dimension `i`.\n It is required that\n `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.\n This operation is equivalent to the following steps:\n 1. Reshape `input` to `reshaped` of shape: [block_shape[0], ...,\n block_shape[M-1], batch / prod(block_shape), input_shape[1], ...,\n input_shape[N-1]]\n 2. Permute dimensions of `reshaped` to produce `permuted` of shape\n [batch / prod(block_shape), input_shape[1], block_shape[0], ...,\n input_shape[M], block_shape[M-1], input_shape[M+1],\n ..., input_shape[N-1]]\n 3. Reshape `permuted` to produce `reshaped_permuted` of shape\n [batch / prod(block_shape), input_shape[1] * block_shape[0], ...,\n input_shape[M] * block_shape[M-1], input_shape[M+1], ...,\n input_shape[N-1]]\n 4. Crop the start and end of dimensions `[1, ..., M]` of\n `reshaped_permuted` according to `crops` to produce the output\n of shape:\n [batch / prod(block_shape), input_shape[1] *\n block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] *\n block_shape[M-1] - crops[M-1,0] - crops[M-1,1], input_shape[M+1],\n ..., input_shape[N-1]]\n name: A name for the operation (optional).\n\n Examples:\n\n 1. For the following input of shape `[4, 1, 1, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n [[[[1]]],\n [[[2]]],\n [[[3]]],\n [[[4]]]]\n ```\n\n The output tensor has shape `[1, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [2]],\n [[3], [4]]]]\n ```\n\n 2. For the following input of shape `[4, 1, 1, 3]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n [[[1, 2, 3]],\n [[4, 5, 6]],\n [[7, 8, 9]],\n [[10, 11, 12]]]\n ```\n\n The output tensor has shape `[1, 2, 2, 3]` and value:\n\n ```python\n x = [[[[1, 2, 3], [4, 5, 6 ]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n 3. For the following\n input of shape `[4, 2, 2, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n x = [[[[1], [3]], [[ 9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n The output tensor has shape `[1, 4, 4, 1]` and value:\n\n ```python\n x = [[[1], [2], [ 3], [ 4]],\n [[5], [6], [ 7], [ 8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]\n ```\n\n 4. For the following input of shape\n `[8, 1, 3, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`:\n\n ```python\n x = [[[[0], [ 1], [ 3]]],\n [[[0], [ 9], [11]]],\n [[[0], [ 2], [ 4]]],\n [[[0], [10], [12]]],\n [[[0], [ 5], [ 7]]],\n [[[0], [13], [15]]],\n [[[0], [ 6], [ 8]]],\n [[[0], [14], [16]]]]\n ```\n\n The output tensor has shape `[2, 2, 4, 1]` and value:\n\n ```python\n x = [[[[ 1], [ 2], [ 3], [ 4]],\n [[ 5], [ 6], [ 7], [ 8]]],\n [[[ 9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"bitcast",path:"./tf/bitcast.md",desc:"Bitcasts a tensor from one type to another without copying data.",type:"Functions",docs:"Bitcasts a tensor from one type to another without copying data.\n\n Given a tensor `input`, this operation returns a tensor that has the same buffer\n data as `input` with datatype `type`.\n\n If the input datatype `T` is larger than the output datatype `type` then the\n shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].\n\n If `T` is smaller than `type`, the operator requires that the rightmost\n dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from\n [..., sizeof(`type`)/sizeof(`T`)] to [...].\n\n tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype\n (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()\n gives module error.\n For example,\n\n Example 1:\n\n >>> a = [1., 2., 3.]\n >>> equality_bitcast = tf.bitcast(a, tf.complex128)\n Traceback (most recent call last):\n ...\n InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast]\n >>> equality_cast = tf.cast(a, tf.complex128)\n >>> print(equality_cast)\n tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)\n\n Example 2:\n\n >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)\n \n\n Example 3:\n\n >>> x = [1., 2., 3.]\n >>> y = [0., 2., 3.]\n >>> equality= tf.equal(x,y)\n >>> equality_cast = tf.cast(equality,tf.float32)\n >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8)\n >>> print(equality)\n tf.Tensor([False True True], shape=(3,), dtype=bool)\n >>> print(equality_cast)\n tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)\n >>> print(equality_bitcast)\n tf.Tensor(\n [[ 0 0 0 0]\n [ 0 0 128 63]\n [ 0 0 128 63]], shape=(3, 4), dtype=uint8)\n\n *NOTE*: Bitcast is implemented as a low-level cast, so machines with different\n endian orderings will give different results.\n\n Args:\n input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `complex64`, `complex128`, `qint8`, `quint8`, `qint16`, `quint16`, `qint32`.\n type: A `tf.DType` from: `tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `type`.\n "},{name:"boolean_mask",path:"./tf/boolean_mask.md",desc:"Apply boolean mask to tensor.",type:"Functions",docs:`Apply boolean mask to tensor. - - Numpy equivalent is \`tensor[mask]\`. - - In general, \`0 < dim(mask) = K <= dim(tensor)\`, and \`mask\`'s shape must match - the first K dimensions of \`tensor\`'s shape. We then have: - \`boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]\` - where \`(i1,...,iK)\` is the ith \`True\` entry of \`mask\` (row-major order). - The \`axis\` could be used with \`mask\` to indicate the axis to mask from. - In that case, \`axis + dim(mask) <= dim(tensor)\` and \`mask\`'s shape must match - the first \`axis + dim(mask)\` dimensions of \`tensor\`'s shape. - - See also: \`tf.ragged.boolean_mask\`, which can be applied to both dense and - ragged tensors, and can be used if you need to preserve the masked dimensions - of \`tensor\` (rather than flattening them, as \`tf.boolean_mask\` does). - - Examples: - - >>> tensor = [0, 1, 2, 3] # 1-D example - >>> mask = np.array([True, False, True, False]) - >>> tf.boolean_mask(tensor, mask) - - - >>> tensor = [[1, 2], [3, 4], [5, 6]] # 2-D example - >>> mask = np.array([True, False, True]) - >>> tf.boolean_mask(tensor, mask) - - - Args: - tensor: N-D Tensor. - mask: K-D boolean Tensor, K <= N and K must be known statically. - axis: A 0-D int Tensor representing the axis in \`tensor\` to mask from. By - default, axis is 0 which will mask from the first dimension. Otherwise K + - axis <= N. - name: A name for this operation (optional). - - Returns: - (N-K+1)-dimensional tensor populated by entries in \`tensor\` corresponding - to \`True\` values in \`mask\`. - - Raises: - ValueError: If shapes do not conform. - - Examples: - - \`\`\`python - # 2-D example - tensor = [[1, 2], [3, 4], [5, 6]] - mask = np.array([True, False, True]) - boolean_mask(tensor, mask) # [[1, 2], [5, 6]] - \`\`\` - `},{name:"broadcast_dynamic_shape",path:"./tf/broadcast_dynamic_shape.md",desc:"Computes the shape of a broadcast given symbolic shapes.",type:"Functions",docs:`Computes the shape of a broadcast given symbolic shapes. + `},{name:"as_dtype",path:"./tf/dtypes/as_dtype.md",desc:"Converts the given `type_value` to a `DType`.",type:"Functions",docs:"Converts the given `type_value` to a `DType`.\n\n Note: `DType` values are interned. When passed a new `DType` object,\n `as_dtype` always returns the interned value.\n\n Args:\n type_value: A value that can be converted to a `tf.DType` object. This may\n currently be a `tf.DType` object, a [`DataType`\n enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),\n a string type name, or a [`numpy.dtype`](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html).\n\n Returns:\n A `DType` corresponding to `type_value`.\n\n Raises:\n TypeError: If `type_value` cannot be converted to a `DType`.\n "},{name:"as_string",path:"./tf/strings/as_string.md",desc:"Converts each entry in the given tensor to strings.",type:"Functions",docs:"Converts each entry in the given tensor to strings.\n\n Supports many numeric types and boolean.\n\n For Unicode, see the\n [https://www.tensorflow.org/tutorials/representation/unicode](Working with Unicode text)\n tutorial.\n\n Examples:\n\n ```>>> tf.strings.as_string([3, 2])\n \n >>> tf.strings.as_string([3.1415926, 2.71828], precision=2).numpy()\n array([b'3.14', b'2.72'], dtype=object)\n\n```\n Args:\n input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`, `complex64`, `complex128`, `bool`, `variant`.\n precision: An optional `int`. Defaults to `-1`.\n The post-decimal precision to use for floating point numbers.\n Only used if precision > -1.\n scientific: An optional `bool`. Defaults to `False`.\n Use scientific notation for floating point numbers.\n shortest: An optional `bool`. Defaults to `False`.\n Use shortest representation (either scientific or standard) for\n floating point numbers.\n width: An optional `int`. Defaults to `-1`.\n Pad pre-decimal numbers to this width.\n Applies to both floating point and integer numbers.\n Only used if width > -1.\n fill: An optional `string`. Defaults to `\"\"`.\n The value to pad if width > -1. If empty, pads with spaces.\n Another typical value is '0'. String cannot be longer than 1 character.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `string`.\n "},{name:"asin",path:"./tf/math/asin.md",desc:"Computes the trignometric inverse sine of x element-wise.",type:"Functions",docs:"Computes the trignometric inverse sine of x element-wise.\n\n The `tf.math.asin` operation returns the inverse of `tf.math.sin`, such that\n if `y = tf.math.sin(x)` then, `x = tf.math.asin(y)`.\n\n **Note**: The output of `tf.math.asin` will lie within the invertible range\n of sine, i.e [-pi/2, pi/2].\n\n For example:\n\n ```python\n # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]\n x = tf.constant([1.047, 0.785])\n y = tf.math.sin(x) # [0.8659266, 0.7068252]\n\n tf.math.asin(y) # [1.047, 0.785] = x\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"asinh",path:"./tf/math/asinh.md",desc:"Computes inverse hyperbolic sine of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic sine of x element-wise.\n\n Given an input tensor, this function computes inverse hyperbolic sine\n for every element in the tensor. Both input and output has a range of\n `[-inf, inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"assert_equal",path:"./tf/debugging/assert_equal.md",desc:"Assert the condition `x == y` holds element-wise.",type:"Functions",docs:'Assert the condition `x == y` holds element-wise.\n\n This Op checks that `x[i] == y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` and `y` are not equal, `message`, as well as the first `summarize`\n entries of `x` and `y` are printed, and `InvalidArgumentError` is raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_equal".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x == y` is False. This can be\n used with `tf.control_dependencies` inside of `tf.function`s to block\n followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x == y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_greater",path:"./tf/debugging/assert_greater.md",desc:"Assert the condition `x > y` holds element-wise.",type:"Functions",docs:'Assert the condition `x > y` holds element-wise.\n\n This Op checks that `x[i] > y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` is not greater than `y` element-wise, `message`, as well as the first\n `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is\n raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_greater".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x > y` is False. This can be\n used with `tf.control_dependencies` inside of `tf.function`s to block\n followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x > y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_less",path:"./tf/debugging/assert_less.md",desc:"Assert the condition `x < y` holds element-wise.",type:"Functions",docs:'Assert the condition `x < y` holds element-wise.\n\n This Op checks that `x[i] < y[i]` holds for every pair of (possibly\n broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is\n trivially satisfied.\n\n If `x` is not less than `y` element-wise, `message`, as well as the first\n `summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is\n raised.\n\n Args:\n x: Numeric `Tensor`.\n y: Numeric `Tensor`, same dtype as and broadcastable to `x`.\n message: A string to prefix to the default message.\n summarize: Print this many entries of each tensor.\n name: A name for this operation (optional). Defaults to "assert_less".\n\n Returns:\n Op that raises `InvalidArgumentError` if `x < y` is False.\n This can be used with `tf.control_dependencies` inside of `tf.function`s\n to block followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x < y` is False. The check can be performed immediately during eager\n execution or if `x` and `y` are statically known.\n '},{name:"assert_rank",path:"./tf/debugging/assert_rank.md",desc:"Assert that `x` has rank equal to `rank`.",type:"Functions",docs:'Assert that `x` has rank equal to `rank`.\n\n This Op checks that the rank of `x` is equal to `rank`.\n\n If `x` has a different rank, `message`, as well as the shape of `x` are\n printed, and `InvalidArgumentError` is raised.\n\n Args:\n x: `Tensor`.\n rank: Scalar integer `Tensor`.\n message: A string to prefix to the default message.\n name: A name for this operation (optional). Defaults to\n "assert_rank".\n\n Returns:\n Op raising `InvalidArgumentError` unless `x` has specified rank.\n If static checks determine `x` has correct rank, a `no_op` is returned.\n This can be used with `tf.control_dependencies` inside of `tf.function`s\n to block followup computation until the check has executed.\n @compatibility(eager)\n returns None\n @end_compatibility\n\n Raises:\n InvalidArgumentError: if the check can be performed immediately and\n `x` does not have rank `rank`. The check can be performed immediately\n during eager execution or if the shape of `x` is statically known.\n '},{name:"atan",path:"./tf/math/atan.md",desc:"Computes the trignometric inverse tangent of x element-wise.",type:"Functions",docs:"Computes the trignometric inverse tangent of x element-wise.\n\n The `tf.math.atan` operation returns the inverse of `tf.math.tan`, such that\n if `y = tf.math.tan(x)` then, `x = tf.math.atan(y)`.\n\n **Note**: The output of `tf.math.atan` will lie within the invertible range\n of tan, i.e (-pi/2, pi/2).\n\n For example:\n\n ```python\n # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]\n x = tf.constant([1.047, 0.785])\n y = tf.math.tan(x) # [1.731261, 0.99920404]\n\n tf.math.atan(y) # [1.047, 0.785] = x\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"atan2",path:"./tf/math/atan2.md",desc:"Computes arctangent of `y/x` element-wise, respecting signs of the arguments.",type:"Functions",docs:"Computes arctangent of `y/x` element-wise, respecting signs of the arguments.\n\n This is the angle \\\\( \\theta \\in [-\\pi, \\pi] \\\\) such that\n \\\\[ x = r \\cos(\\theta) \\\\]\n and\n \\\\[ y = r \\sin(\\theta) \\\\]\n where \\\\(r = \\sqrt{x^2 + y^2} \\\\).\n\n For example:\n\n ```>>> x = [1., 1.]\n >>> y = [1., -1.]\n >>> print((tf.math.atan2(y,x) * (180 / np.pi)).numpy())\n [ 45. -45.]\n\n```\n Args:\n y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.\n x: A `Tensor`. Must have the same type as `y`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `y`.\n "},{name:"atanh",path:"./tf/math/atanh.md",desc:"Computes inverse hyperbolic tangent of x element-wise.",type:"Functions",docs:'Computes inverse hyperbolic tangent of x element-wise.\n\n Given an input tensor, this function computes inverse hyperbolic tangent\n for every element in the tensor. Input range is `[-1,1]` and output range is\n `[-inf, inf]`. If input is `-1`, output will be `-inf` and if the\n input is `1`, output will be `inf`. Values outside the range will have\n `nan` as output.\n\n ```python\n x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")])\n tf.math.atanh(x) ==> [nan -inf -0.54930615 inf 0. 0.54930615 nan nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"batch_to_space",path:"./tf/batch_to_space.md",desc:"BatchToSpace for N-D tensors of type T.",type:"Functions",docs:'BatchToSpace for N-D tensors of type T.\n\n This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of\n shape `block_shape + [batch]`, interleaves these blocks back into the grid\n defined by the spatial dimensions `[1, ..., M]`, to obtain a result with the\n same rank as the input. The spatial dimensions of this intermediate result\n are then optionally cropped according to `crops` to produce the output. This\n is the reverse of SpaceToBatch (see `tf.space_to_batch`).\n\n Args:\n input: A N-D `Tensor` with shape `input_shape = [batch] + spatial_shape +\n remaining_shape`, where `spatial_shape` has M dimensions.\n block_shape: A 1-D `Tensor` with shape [M]. Must be one of the following\n types: `int32`, `int64`. All values must be >= 1. For backwards\n compatibility with TF 1.0, this parameter may be an int, in which case it\n is converted to\n `numpy.array([block_shape, block_shape],\n dtype=numpy.int64)`.\n crops: A 2-D `Tensor` with shape `[M, 2]`. Must be one of the\n following types: `int32`, `int64`. All values must be >= 0.\n `crops[i] = [crop_start, crop_end]` specifies the amount to crop from\n input dimension `i + 1`, which corresponds to spatial dimension `i`.\n It is required that\n `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.\n This operation is equivalent to the following steps:\n 1. Reshape `input` to `reshaped` of shape: [block_shape[0], ...,\n block_shape[M-1], batch / prod(block_shape), input_shape[1], ...,\n input_shape[N-1]]\n 2. Permute dimensions of `reshaped` to produce `permuted` of shape\n [batch / prod(block_shape), input_shape[1], block_shape[0], ...,\n input_shape[M], block_shape[M-1], input_shape[M+1],\n ..., input_shape[N-1]]\n 3. Reshape `permuted` to produce `reshaped_permuted` of shape\n [batch / prod(block_shape), input_shape[1] * block_shape[0], ...,\n input_shape[M] * block_shape[M-1], input_shape[M+1], ...,\n input_shape[N-1]]\n 4. Crop the start and end of dimensions `[1, ..., M]` of\n `reshaped_permuted` according to `crops` to produce the output\n of shape:\n [batch / prod(block_shape), input_shape[1] *\n block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] *\n block_shape[M-1] - crops[M-1,0] - crops[M-1,1], input_shape[M+1],\n ..., input_shape[N-1]]\n name: A name for the operation (optional).\n\n Examples:\n\n 1. For the following input of shape `[4, 1, 1, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n [[[[1]]],\n [[[2]]],\n [[[3]]],\n [[[4]]]]\n ```\n\n The output tensor has shape `[1, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [2]],\n [[3], [4]]]]\n ```\n\n 2. For the following input of shape `[4, 1, 1, 3]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n [[[1, 2, 3]],\n [[4, 5, 6]],\n [[7, 8, 9]],\n [[10, 11, 12]]]\n ```\n\n The output tensor has shape `[1, 2, 2, 3]` and value:\n\n ```python\n x = [[[[1, 2, 3], [4, 5, 6 ]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n 3. For the following\n input of shape `[4, 2, 2, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n ```python\n x = [[[[1], [3]], [[ 9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n The output tensor has shape `[1, 4, 4, 1]` and value:\n\n ```python\n x = [[[1], [2], [ 3], [ 4]],\n [[5], [6], [ 7], [ 8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]\n ```\n\n 4. For the following input of shape\n `[8, 1, 3, 1]`,\n `block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`:\n\n ```python\n x = [[[[0], [ 1], [ 3]]],\n [[[0], [ 9], [11]]],\n [[[0], [ 2], [ 4]]],\n [[[0], [10], [12]]],\n [[[0], [ 5], [ 7]]],\n [[[0], [13], [15]]],\n [[[0], [ 6], [ 8]]],\n [[[0], [14], [16]]]]\n ```\n\n The output tensor has shape `[2, 2, 4, 1]` and value:\n\n ```python\n x = [[[[ 1], [ 2], [ 3], [ 4]],\n [[ 5], [ 6], [ 7], [ 8]]],\n [[[ 9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"bitcast",path:"./tf/bitcast.md",desc:"Bitcasts a tensor from one type to another without copying data.",type:"Functions",docs:"Bitcasts a tensor from one type to another without copying data.\n\n Given a tensor `input`, this operation returns a tensor that has the same buffer\n data as `input` with datatype `type`.\n\n If the input datatype `T` is larger than the output datatype `type` then the\n shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].\n\n If `T` is smaller than `type`, the operator requires that the rightmost\n dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from\n [..., sizeof(`type`)/sizeof(`T`)] to [...].\n\n tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype\n (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()\n gives module error.\n For example,\n\n Example 1:\n\n ```>>> a = [1., 2., 3.]\n >>> equality_bitcast = tf.bitcast(a, tf.complex128)\n Traceback (most recent call last):\n ...\n InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast]\n >>> equality_cast = tf.cast(a, tf.complex128)\n >>> print(equality_cast)\n tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)\n\n```\n Example 2:\n\n ```>>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)\n \n\n```\n Example 3:\n\n ```>>> x = [1., 2., 3.]\n >>> y = [0., 2., 3.]\n >>> equality= tf.equal(x,y)\n >>> equality_cast = tf.cast(equality,tf.float32)\n >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8)\n >>> print(equality)\n tf.Tensor([False True True], shape=(3,), dtype=bool)\n >>> print(equality_cast)\n tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)\n >>> print(equality_bitcast)\n tf.Tensor(\n [[ 0 0 0 0]\n [ 0 0 128 63]\n [ 0 0 128 63]], shape=(3, 4), dtype=uint8)\n\n```\n *NOTE*: Bitcast is implemented as a low-level cast, so machines with different\n endian orderings will give different results.\n\n Args:\n input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `complex64`, `complex128`, `qint8`, `quint8`, `qint16`, `quint16`, `qint32`.\n type: A `tf.DType` from: `tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `type`.\n "},{name:"boolean_mask",path:"./tf/boolean_mask.md",desc:"Apply boolean mask to tensor.",type:"Functions",docs:"Apply boolean mask to tensor.\n\n Numpy equivalent is `tensor[mask]`.\n\n In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match\n the first K dimensions of `tensor`'s shape. We then have:\n `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`\n where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).\n The `axis` could be used with `mask` to indicate the axis to mask from.\n In that case, `axis + dim(mask) <= dim(tensor)` and `mask`'s shape must match\n the first `axis + dim(mask)` dimensions of `tensor`'s shape.\n\n See also: `tf.ragged.boolean_mask`, which can be applied to both dense and\n ragged tensors, and can be used if you need to preserve the masked dimensions\n of `tensor` (rather than flattening them, as `tf.boolean_mask` does).\n\n Examples:\n\n ```>>> tensor = [0, 1, 2, 3] # 1-D example\n >>> mask = np.array([True, False, True, False])\n >>> tf.boolean_mask(tensor, mask)\n \n\n```\n ```>>> tensor = [[1, 2], [3, 4], [5, 6]] # 2-D example\n >>> mask = np.array([True, False, True])\n >>> tf.boolean_mask(tensor, mask)\n \n\n```\n Args:\n tensor: N-D Tensor.\n mask: K-D boolean Tensor, K <= N and K must be known statically.\n axis: A 0-D int Tensor representing the axis in `tensor` to mask from. By\n default, axis is 0 which will mask from the first dimension. Otherwise K +\n axis <= N.\n name: A name for this operation (optional).\n\n Returns:\n (N-K+1)-dimensional tensor populated by entries in `tensor` corresponding\n to `True` values in `mask`.\n\n Raises:\n ValueError: If shapes do not conform.\n\n Examples:\n\n ```python\n # 2-D example\n tensor = [[1, 2], [3, 4], [5, 6]]\n mask = np.array([True, False, True])\n boolean_mask(tensor, mask) # [[1, 2], [5, 6]]\n ```\n "},{name:"broadcast_dynamic_shape",path:"./tf/broadcast_dynamic_shape.md",desc:"Computes the shape of a broadcast given symbolic shapes.",type:"Functions",docs:`Computes the shape of a broadcast given symbolic shapes. When \`shape_x\` and \`shape_y\` are Tensors representing shapes (i.e. the result of calling tf.shape on another Tensor) this computes a Tensor which is the @@ -1896,11 +1661,12 @@ Note: The output of this function should be used. If it is not, a warning will b Example: - >>> shape_x = (1, 2, 3) + \`\`\`>>> shape_x = (1, 2, 3) >>> shape_y = (5, 1, 3) >>> tf.broadcast_dynamic_shape(shape_x, shape_y) +\`\`\` Args: shape_x: A rank 1 integer \`Tensor\`, representing the shape of x. shape_y: A rank 1 integer \`Tensor\`, representing the shape of y. @@ -1911,36 +1677,7 @@ Note: The output of this function should be used. If it is not, a warning will b Raises: InvalidArgumentError: If the two shapes are incompatible for broadcasting. - `},{name:"broadcast_static_shape",path:"./tf/broadcast_static_shape.md",desc:"Computes the shape of a broadcast given known shapes.",type:"Functions",docs:`Computes the shape of a broadcast given known shapes. - - When \`shape_x\` and \`shape_y\` are fully known \`TensorShape\`s this computes a - \`TensorShape\` which is the shape of the result of a broadcasting op applied in - tensors of shapes \`shape_x\` and \`shape_y\`. - - For example, if shape_x is \`TensorShape([1, 2, 3])\` and shape_y is - \`TensorShape([5, 1, 3])\`, the result is a TensorShape whose value is - \`TensorShape([5, 2, 3])\`. - - This is useful when validating the result of a broadcasting operation when the - tensors have statically known shapes. - - Example: - - >>> shape_x = tf.TensorShape([1, 2, 3]) - >>> shape_y = tf.TensorShape([5, 1 ,3]) - >>> tf.broadcast_static_shape(shape_x, shape_y) - TensorShape([5, 2, 3]) - - Args: - shape_x: A \`TensorShape\` - shape_y: A \`TensorShape\` - - Returns: - A \`TensorShape\` representing the broadcasted shape. - - Raises: - ValueError: If the two shapes can not be broadcasted. - `},{name:"broadcast_to",path:"./tf/broadcast_to.md",desc:"Broadcast an array for a compatible shape.",type:"Functions",docs:`Broadcast an array for a compatible shape. + `},{name:"broadcast_static_shape",path:"./tf/broadcast_static_shape.md",desc:"Computes the shape of a broadcast given known shapes.",type:"Functions",docs:"Computes the shape of a broadcast given known shapes.\n\n When `shape_x` and `shape_y` are fully known `TensorShape`s this computes a\n `TensorShape` which is the shape of the result of a broadcasting op applied in\n tensors of shapes `shape_x` and `shape_y`.\n\n For example, if shape_x is `TensorShape([1, 2, 3])` and shape_y is\n `TensorShape([5, 1, 3])`, the result is a TensorShape whose value is\n `TensorShape([5, 2, 3])`.\n\n This is useful when validating the result of a broadcasting operation when the\n tensors have statically known shapes.\n\n Example:\n\n ```>>> shape_x = tf.TensorShape([1, 2, 3])\n >>> shape_y = tf.TensorShape([5, 1 ,3])\n >>> tf.broadcast_static_shape(shape_x, shape_y)\n TensorShape([5, 2, 3])\n\n```\n Args:\n shape_x: A `TensorShape`\n shape_y: A `TensorShape`\n\n Returns:\n A `TensorShape` representing the broadcasted shape.\n\n Raises:\n ValueError: If the two shapes can not be broadcasted.\n "},{name:"broadcast_to",path:"./tf/broadcast_to.md",desc:"Broadcast an array for a compatible shape.",type:"Functions",docs:`Broadcast an array for a compatible shape. Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each @@ -1950,7 +1687,7 @@ Note: The output of this function should be used. If it is not, a warning will b For example, - >>> x = tf.constant([1, 2, 3]) + \`\`\`>>> x = tf.constant([1, 2, 3]) >>> y = tf.broadcast_to(x, [3, 3]) >>> print(y) tf.Tensor( @@ -1958,6 +1695,7 @@ Note: The output of this function should be used. If it is not, a warning will b [1 2 3] [1 2 3]], shape=(3, 3), dtype=int32) +\`\`\` In the above example, the input Tensor with the shape of \`[1, 3]\` is broadcasted to output Tensor with shape of \`[3, 3]\`. @@ -2065,128 +1803,7 @@ Note: The output of this function should be used. If it is not, a warning will b TypeError: If \`pred_fn_pairs\` is a list but does not contain 2-tuples. TypeError: If \`fns[i]\` is not callable for any i, or \`default\` is not callable. - `},{name:"cast",path:"./tf/cast.md",desc:"Casts a tensor to a new type.",type:"Functions",docs:"Casts a tensor to a new type.\n\n The operation casts `x` (in case of `Tensor`) or `x.values`\n (in case of `SparseTensor` or `IndexedSlices`) to `dtype`.\n\n For example:\n\n >>> x = tf.constant([1.8, 2.2], dtype=tf.float32)\n >>> tf.cast(x, tf.int32)\n \n\n Notice `tf.cast` has an alias `tf.dtypes.cast`:\n\n >>> x = tf.constant([1.8, 2.2], dtype=tf.float32)\n >>> tf.dtypes.cast(x, tf.int32)\n \n\n The operation supports data types (for `x` and `dtype`) of\n `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,\n `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.\n In case of casting from complex types (`complex64`, `complex128`) to real\n types, only the real part of `x` is returned. In case of casting from real\n types to complex types (`complex64`, `complex128`), the imaginary part of the\n returned value is set to `0`. The handling of complex types here matches the\n behavior of numpy.\n\n Note casting nan and inf values to integral types has undefined behavior.\n\n Args:\n x: A `Tensor` or `SparseTensor` or `IndexedSlices` of numeric type. It could\n be `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`,\n `int64`, `float16`, `float32`, `float64`, `complex64`, `complex128`,\n `bfloat16`.\n dtype: The destination type. The list of supported dtypes is the same as\n `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` and\n same type as `dtype`.\n\n Raises:\n TypeError: If `x` cannot be cast to the `dtype`.\n "},{name:"clip_by_global_norm",path:"./tf/clip_by_global_norm.md",desc:"Clips values of multiple tensors by the ratio of the sum of their norms.",type:"Functions",docs:"Clips values of multiple tensors by the ratio of the sum of their norms.\n\n Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,\n this operation returns a list of clipped tensors `list_clipped`\n and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,\n if you've already computed the global norm for `t_list`, you can specify\n the global norm with `use_norm`.\n\n To perform the clipping, the values `t_list[i]` are set to:\n\n t_list[i] * clip_norm / max(global_norm, clip_norm)\n\n where:\n\n global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))\n\n If `clip_norm > global_norm` then the entries in `t_list` remain as they are,\n otherwise they're all shrunk by the global ratio.\n\n If `global_norm == infinity` then the entries in `t_list` are all set to `NaN`\n to signal that an error occurred.\n\n Any of the entries of `t_list` that are of type `None` are ignored.\n\n This is the correct way to perform gradient clipping (Pascanu et al., 2012).\n\n However, it is slower than `clip_by_norm()` because all the parameters must be\n ready before the clipping operation can be performed.\n\n Args:\n t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.\n clip_norm: A 0-D (scalar) `Tensor` > 0. The clipping ratio.\n use_norm: A 0-D (scalar) `Tensor` of type `float` (optional). The global\n norm to use. If not provided, `global_norm()` is used to compute the norm.\n name: A name for the operation (optional).\n\n Returns:\n list_clipped: A list of `Tensors` of the same type as `list_t`.\n global_norm: A 0-D (scalar) `Tensor` representing the global norm.\n\n Raises:\n TypeError: If `t_list` is not a sequence.\n\n References:\n On the difficulty of training Recurrent Neural Networks:\n [Pascanu et al., 2012](http://proceedings.mlr.press/v28/pascanu13.html)\n ([pdf](http://proceedings.mlr.press/v28/pascanu13.pdf))\n "},{name:"clip_by_norm",path:"./tf/clip_by_norm.md",desc:"Clips tensor values to a maximum L2-norm.",type:"Functions",docs:`Clips tensor values to a maximum L2-norm. - - Given a tensor \`t\`, and a maximum clip value \`clip_norm\`, this operation - normalizes \`t\` so that its L2-norm is less than or equal to \`clip_norm\`, - along the dimensions given in \`axes\`. Specifically, in the default case - where all dimensions are used for calculation, if the L2-norm of \`t\` is - already less than or equal to \`clip_norm\`, then \`t\` is not modified. If - the L2-norm is greater than \`clip_norm\`, then this operation returns a - tensor of the same type and shape as \`t\` with its values set to: - - \`t * clip_norm / l2norm(t)\` - - In this case, the L2-norm of the output tensor is \`clip_norm\`. - - As another example, if \`t\` is a matrix and \`axes == [1]\`, then each row - of the output will have L2-norm less than or equal to \`clip_norm\`. If - \`axes == [0]\` instead, each column of the output will be clipped. - - Code example: - - >>> some_nums = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.float32) - >>> tf.clip_by_norm(some_nums, 2.0).numpy() - array([[0.26967996, 0.5393599 , 0.80903983, 1.0787199 , 1.3483998 ]], - dtype=float32) - - This operation is typically used to clip gradients before applying them with - an optimizer. Most gradient data is a collection of different shaped tensors - for different parts of the model. Thus, this is a common usage: - - \`\`\` - # Get your gradients after training - loss_value, grads = grad(model, features, labels) - - # Apply some clipping - grads = [tf.clip_by_norm(g, norm) - for g in grads] - - # Continue on with training - optimizer.apply_gradients(grads) - \`\`\` - - Args: - t: A \`Tensor\` or \`IndexedSlices\`. This must be a floating point type. - clip_norm: A 0-D (scalar) \`Tensor\` > 0. A maximum clipping value, also - floating point - axes: A 1-D (vector) \`Tensor\` of type int32 containing the dimensions - to use for computing the L2-norm. If \`None\` (the default), uses all - dimensions. - name: A name for the operation (optional). - - Returns: - A clipped \`Tensor\` or \`IndexedSlices\`. - - Raises: - ValueError: If the clip_norm tensor is not a 0-D scalar tensor. - TypeError: If dtype of the input is not a floating point or - complex type. - `},{name:"clip_by_value",path:"./tf/clip_by_value.md",desc:"Clips tensor values to a specified min and max.",type:"Functions",docs:`Clips tensor values to a specified min and max. - - Given a tensor \`t\`, this operation returns a tensor of the same type and - shape as \`t\` with its values clipped to \`clip_value_min\` and \`clip_value_max\`. - Any values less than \`clip_value_min\` are set to \`clip_value_min\`. Any values - greater than \`clip_value_max\` are set to \`clip_value_max\`. - - Note: \`clip_value_min\` needs to be smaller or equal to \`clip_value_max\` for - correct results. - - For example: - - Basic usage passes a scalar as the min and max value. - - >>> t = tf.constant([[-10., -1., 0.], [0., 2., 10.]]) - >>> t2 = tf.clip_by_value(t, clip_value_min=-1, clip_value_max=1) - >>> t2.numpy() - array([[-1., -1., 0.], - [ 0., 1., 1.]], dtype=float32) - - The min and max can be the same size as \`t\`, or broadcastable to that size. - - >>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]]) - >>> clip_min = [[2],[1]] - >>> t3 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100) - >>> t3.numpy() - array([[ 2., 2., 10.], - [ 1., 1., 10.]], dtype=float32) - - Broadcasting fails, intentionally, if you would expand the dimensions of \`t\` - - >>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]]) - >>> clip_min = [[[2, 1]]] # Has a third axis - >>> t4 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100) - Traceback (most recent call last): - ... - InvalidArgumentError: Incompatible shapes: [2,3] vs. [1,1,2] - - It throws a \`TypeError\` if you try to clip an \`int\` to a \`float\` value - (\`tf.cast\` the input to \`float\` first). - - >>> t = tf.constant([[1, 2], [3, 4]], dtype=tf.int32) - >>> t5 = tf.clip_by_value(t, clip_value_min=-3.1, clip_value_max=3.1) - Traceback (most recent call last): - ... - TypeError: Cannot convert ... - - - Args: - t: A \`Tensor\` or \`IndexedSlices\`. - clip_value_min: The minimum value to clip to. A scalar \`Tensor\` or one that - is broadcastable to the shape of \`t\`. - clip_value_max: The maximum value to clip to. A scalar \`Tensor\` or one that - is broadcastable to the shape of \`t\`. - name: A name for the operation (optional). - - Returns: - A clipped \`Tensor\` or \`IndexedSlices\`. - - Raises: - \`tf.errors.InvalidArgumentError\`: If the clip tensors would trigger array - broadcasting that would make the returned tensor larger than the input. - TypeError: If dtype of the input is \`int32\` and dtype of - the \`clip_value_min\` or \`clip_value_max\` is \`float32\` - `},{name:"complex",path:"./tf/dtypes/complex.md",desc:"Converts two real numbers to a complex number.",type:"Functions",docs:"Converts two real numbers to a complex number.\n\n Given a tensor `real` representing the real part of a complex number, and a\n tensor `imag` representing the imaginary part of a complex number, this\n operation returns complex numbers elementwise of the form \\\\(a + bj\\\\), where\n *a* represents the `real` part and *b* represents the `imag` part.\n\n The input tensors `real` and `imag` must have the same shape.\n\n For example:\n\n ```python\n real = tf.constant([2.25, 3.25])\n imag = tf.constant([4.75, 5.75])\n tf.complex(real, imag) # [[2.25 + 4.75j], [3.25 + 5.75j]]\n ```\n\n Args:\n real: A `Tensor`. Must be one of the following types: `float32`, `float64`.\n imag: A `Tensor`. Must have the same type as `real`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `complex64` or `complex128`.\n\n Raises:\n TypeError: Real and imag must be correct types\n "},{name:"concat",path:"./tf/concat.md",desc:"Concatenates tensors along one dimension.",type:"Functions",docs:`Concatenates tensors along one dimension. + `},{name:"cast",path:"./tf/cast.md",desc:"Casts a tensor to a new type.",type:"Functions",docs:"Casts a tensor to a new type.\n\n The operation casts `x` (in case of `Tensor`) or `x.values`\n (in case of `SparseTensor` or `IndexedSlices`) to `dtype`.\n\n For example:\n\n ```>>> x = tf.constant([1.8, 2.2], dtype=tf.float32)\n >>> tf.cast(x, tf.int32)\n \n\n```\n Notice `tf.cast` has an alias `tf.dtypes.cast`:\n\n ```>>> x = tf.constant([1.8, 2.2], dtype=tf.float32)\n >>> tf.dtypes.cast(x, tf.int32)\n \n\n```\n The operation supports data types (for `x` and `dtype`) of\n `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`, `int64`,\n `float16`, `float32`, `float64`, `complex64`, `complex128`, `bfloat16`.\n In case of casting from complex types (`complex64`, `complex128`) to real\n types, only the real part of `x` is returned. In case of casting from real\n types to complex types (`complex64`, `complex128`), the imaginary part of the\n returned value is set to `0`. The handling of complex types here matches the\n behavior of numpy.\n\n Note casting nan and inf values to integral types has undefined behavior.\n\n Args:\n x: A `Tensor` or `SparseTensor` or `IndexedSlices` of numeric type. It could\n be `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `int32`,\n `int64`, `float16`, `float32`, `float64`, `complex64`, `complex128`,\n `bfloat16`.\n dtype: The destination type. The list of supported dtypes is the same as\n `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` and\n same type as `dtype`.\n\n Raises:\n TypeError: If `x` cannot be cast to the `dtype`.\n "},{name:"clip_by_global_norm",path:"./tf/clip_by_global_norm.md",desc:"Clips values of multiple tensors by the ratio of the sum of their norms.",type:"Functions",docs:"Clips values of multiple tensors by the ratio of the sum of their norms.\n\n Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,\n this operation returns a list of clipped tensors `list_clipped`\n and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,\n if you've already computed the global norm for `t_list`, you can specify\n the global norm with `use_norm`.\n\n To perform the clipping, the values `t_list[i]` are set to:\n\n t_list[i] * clip_norm / max(global_norm, clip_norm)\n\n where:\n\n global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))\n\n If `clip_norm > global_norm` then the entries in `t_list` remain as they are,\n otherwise they're all shrunk by the global ratio.\n\n If `global_norm == infinity` then the entries in `t_list` are all set to `NaN`\n to signal that an error occurred.\n\n Any of the entries of `t_list` that are of type `None` are ignored.\n\n This is the correct way to perform gradient clipping (Pascanu et al., 2012).\n\n However, it is slower than `clip_by_norm()` because all the parameters must be\n ready before the clipping operation can be performed.\n\n Args:\n t_list: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.\n clip_norm: A 0-D (scalar) `Tensor` > 0. The clipping ratio.\n use_norm: A 0-D (scalar) `Tensor` of type `float` (optional). The global\n norm to use. If not provided, `global_norm()` is used to compute the norm.\n name: A name for the operation (optional).\n\n Returns:\n list_clipped: A list of `Tensors` of the same type as `list_t`.\n global_norm: A 0-D (scalar) `Tensor` representing the global norm.\n\n Raises:\n TypeError: If `t_list` is not a sequence.\n\n References:\n On the difficulty of training Recurrent Neural Networks:\n [Pascanu et al., 2012](http://proceedings.mlr.press/v28/pascanu13.html)\n ([pdf](http://proceedings.mlr.press/v28/pascanu13.pdf))\n "},{name:"clip_by_norm",path:"./tf/clip_by_norm.md",desc:"Clips tensor values to a maximum L2-norm.",type:"Functions",docs:"Clips tensor values to a maximum L2-norm.\n\n Given a tensor `t`, and a maximum clip value `clip_norm`, this operation\n normalizes `t` so that its L2-norm is less than or equal to `clip_norm`,\n along the dimensions given in `axes`. Specifically, in the default case\n where all dimensions are used for calculation, if the L2-norm of `t` is\n already less than or equal to `clip_norm`, then `t` is not modified. If\n the L2-norm is greater than `clip_norm`, then this operation returns a\n tensor of the same type and shape as `t` with its values set to:\n\n `t * clip_norm / l2norm(t)`\n\n In this case, the L2-norm of the output tensor is `clip_norm`.\n\n As another example, if `t` is a matrix and `axes == [1]`, then each row\n of the output will have L2-norm less than or equal to `clip_norm`. If\n `axes == [0]` instead, each column of the output will be clipped.\n\n Code example:\n\n ```>>> some_nums = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.float32)\n >>> tf.clip_by_norm(some_nums, 2.0).numpy()\n array([[0.26967996, 0.5393599 , 0.80903983, 1.0787199 , 1.3483998 ]],\n dtype=float32)\n\n```\n This operation is typically used to clip gradients before applying them with\n an optimizer. Most gradient data is a collection of different shaped tensors\n for different parts of the model. Thus, this is a common usage:\n\n ```\n # Get your gradients after training\n loss_value, grads = grad(model, features, labels)\n\n # Apply some clipping\n grads = [tf.clip_by_norm(g, norm)\n for g in grads]\n\n # Continue on with training\n optimizer.apply_gradients(grads)\n ```\n\n Args:\n t: A `Tensor` or `IndexedSlices`. This must be a floating point type.\n clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value, also\n floating point\n axes: A 1-D (vector) `Tensor` of type int32 containing the dimensions\n to use for computing the L2-norm. If `None` (the default), uses all\n dimensions.\n name: A name for the operation (optional).\n\n Returns:\n A clipped `Tensor` or `IndexedSlices`.\n\n Raises:\n ValueError: If the clip_norm tensor is not a 0-D scalar tensor.\n TypeError: If dtype of the input is not a floating point or\n complex type.\n "},{name:"clip_by_value",path:"./tf/clip_by_value.md",desc:"Clips tensor values to a specified min and max.",type:"Functions",docs:"Clips tensor values to a specified min and max.\n\n Given a tensor `t`, this operation returns a tensor of the same type and\n shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.\n Any values less than `clip_value_min` are set to `clip_value_min`. Any values\n greater than `clip_value_max` are set to `clip_value_max`.\n\n Note: `clip_value_min` needs to be smaller or equal to `clip_value_max` for\n correct results.\n\n For example:\n\n Basic usage passes a scalar as the min and max value.\n\n ```>>> t = tf.constant([[-10., -1., 0.], [0., 2., 10.]])\n >>> t2 = tf.clip_by_value(t, clip_value_min=-1, clip_value_max=1)\n >>> t2.numpy()\n array([[-1., -1., 0.],\n [ 0., 1., 1.]], dtype=float32)\n\n```\n The min and max can be the same size as `t`, or broadcastable to that size.\n\n ```>>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])\n >>> clip_min = [[2],[1]]\n >>> t3 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)\n >>> t3.numpy()\n array([[ 2., 2., 10.],\n [ 1., 1., 10.]], dtype=float32)\n\n```\n Broadcasting fails, intentionally, if you would expand the dimensions of `t`\n\n ```>>> t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])\n >>> clip_min = [[[2, 1]]] # Has a third axis\n >>> t4 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)\n Traceback (most recent call last):\n ...\n InvalidArgumentError: Incompatible shapes: [2,3] vs. [1,1,2]\n\n```\n It throws a `TypeError` if you try to clip an `int` to a `float` value\n (`tf.cast` the input to `float` first).\n\n ```>>> t = tf.constant([[1, 2], [3, 4]], dtype=tf.int32)\n >>> t5 = tf.clip_by_value(t, clip_value_min=-3.1, clip_value_max=3.1)\n Traceback (most recent call last):\n ...\n TypeError: Cannot convert ...\n\n```\n\n Args:\n t: A `Tensor` or `IndexedSlices`.\n clip_value_min: The minimum value to clip to. A scalar `Tensor` or one that\n is broadcastable to the shape of `t`.\n clip_value_max: The maximum value to clip to. A scalar `Tensor` or one that\n is broadcastable to the shape of `t`.\n name: A name for the operation (optional).\n\n Returns:\n A clipped `Tensor` or `IndexedSlices`.\n\n Raises:\n `tf.errors.InvalidArgumentError`: If the clip tensors would trigger array\n broadcasting that would make the returned tensor larger than the input.\n TypeError: If dtype of the input is `int32` and dtype of\n the `clip_value_min` or `clip_value_max` is `float32`\n "},{name:"complex",path:"./tf/dtypes/complex.md",desc:"Converts two real numbers to a complex number.",type:"Functions",docs:"Converts two real numbers to a complex number.\n\n Given a tensor `real` representing the real part of a complex number, and a\n tensor `imag` representing the imaginary part of a complex number, this\n operation returns complex numbers elementwise of the form \\\\(a + bj\\\\), where\n *a* represents the `real` part and *b* represents the `imag` part.\n\n The input tensors `real` and `imag` must have the same shape.\n\n For example:\n\n ```python\n real = tf.constant([2.25, 3.25])\n imag = tf.constant([4.75, 5.75])\n tf.complex(real, imag) # [[2.25 + 4.75j], [3.25 + 5.75j]]\n ```\n\n Args:\n real: A `Tensor`. Must be one of the following types: `float32`, `float64`.\n imag: A `Tensor`. Must have the same type as `real`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `complex64` or `complex128`.\n\n Raises:\n TypeError: Real and imag must be correct types\n "},{name:"concat",path:"./tf/concat.md",desc:"Concatenates tensors along one dimension.",type:"Functions",docs:`Concatenates tensors along one dimension. See also \`tf.tile\`, \`tf.stack\`, \`tf.repeat\`. @@ -2208,7 +1825,7 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> t1 = [[1, 2, 3], [4, 5, 6]] + \`\`\`>>> t1 = [[1, 2, 3], [4, 5, 6]] >>> t2 = [[7, 8, 9], [10, 11, 12]] >>> tf.concat([t1, t2], 0) - >>> tf.concat([t1, t2], 1) +\`\`\` + \`\`\`>>> tf.concat([t1, t2], 1) +\`\`\` As in Python, the \`axis\` could also be negative numbers. Negative \`axis\` are interpreted as counting from the end of the rank, i.e., \`axis + rank(values)\`-th dimension. For example: - >>> t1 = [[[1, 2], [2, 3]], [[4, 4], [5, 3]]] + \`\`\`>>> t1 = [[[1, 2], [2, 3]], [[4, 4], [5, 3]]] >>> t2 = [[[7, 4], [8, 4]], [[2, 10], [15, 11]]] >>> tf.concat([t1, t2], -1) +\`\`\` Note: If you are concatenating along a new axis consider using stack. E.g. @@ -2273,7 +1893,7 @@ Note: The output of this function should be used. If it is not, a warning will b If the argument \`dtype\` is not specified, then the type is inferred from the type of \`value\`. - >>> # Constant 1-D Tensor from a python list. + \`\`\`>>> # Constant 1-D Tensor from a python list. >>> tf.constant([1, 2, 3, 4, 5, 6]) @@ -2284,17 +1904,19 @@ Note: The output of this function should be used. If it is not, a warning will b array([[1, 2, 3], [4, 5, 6]])> +\`\`\` If \`dtype\` is specified, the resulting tensor values are cast to the requested \`dtype\`. - >>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64) + \`\`\`>>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64) +\`\`\` If \`shape\` is set, the \`value\` is reshaped to match. Scalars are expanded to fill the \`shape\`: - >>> tf.constant(0, shape=(2, 3)) + \`\`\`>>> tf.constant(0, shape=(2, 3)) @@ -2303,25 +1925,28 @@ Note: The output of this function should be used. If it is not, a warning will b array([[1, 2, 3], [4, 5, 6]], dtype=int32)> +\`\`\` \`tf.constant\` has no effect if an eager Tensor is passed as the \`value\`, it even transmits gradients: - >>> v = tf.Variable([0.0]) + \`\`\`>>> v = tf.Variable([0.0]) >>> with tf.GradientTape() as g: ... loss = tf.constant(v + v) >>> g.gradient(loss, v).numpy() array([2.], dtype=float32) +\`\`\` But, since \`tf.constant\` embeds the value in the \`tf.Graph\` this fails for symbolic tensors: - >>> with tf.compat.v1.Graph().as_default(): + \`\`\`>>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.constant(i) Traceback (most recent call last): ... TypeError: ... +\`\`\` \`tf.constant\` will create tensors on the current device. Inputs which are already tensors maintain their placements unchanged. @@ -2331,10 +1956,11 @@ Note: The output of this function should be used. If it is not, a warning will b * It has no \`shape\` argument. * Symbolic tensors are allowed to pass through. - >>> with tf.compat.v1.Graph().as_default(): + \`\`\`>>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.convert_to_tensor(i) +\`\`\` * \`tf.fill\`: differs in a few ways: * \`tf.constant\` supports arbitrary constants, not just uniform scalar Tensors like \`tf.fill\`. @@ -2385,12 +2011,13 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> import numpy as np + \`\`\`>>> import numpy as np >>> def my_func(arg): ... arg = tf.convert_to_tensor(arg, dtype=tf.float32) ... return arg - >>> # The following calls are equivalent. +\`\`\` + \`\`\`>>> # The following calls are equivalent. ... >>> value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]])) >>> print(value_1) @@ -2408,6 +2035,7 @@ Note: The output of this function should be used. If it is not, a warning will b [[1. 2.] [3. 4.]], shape=(2, 2), dtype=float32) +\`\`\` This function can be useful when composing a new operation in Python (such as \`my_func\` in the example above). All standard Python op constructors apply this function to each of their Tensor-valued @@ -2436,7 +2064,7 @@ Note: The output of this function should be used. If it is not, a warning will b TypeError: If no conversion function is registered for \`value\` to \`dtype\`. RuntimeError: If a registered conversion function returns an invalid value. ValueError: If the \`value\` is a tensor not of given \`dtype\` in graph mode. - `},{name:"cos",path:"./tf/math/cos.md",desc:"Computes cos of x element-wise.",type:"Functions",docs:'Computes cos of x element-wise.\n\n Given an input tensor, this function computes cosine of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `[-1,1]`. If input lies outside the boundary, `nan`\n is returned.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"cosh",path:"./tf/math/cosh.md",desc:"Computes hyperbolic cosine of x element-wise.",type:"Functions",docs:'Computes hyperbolic cosine of x element-wise.\n\n Given an input tensor, this function computes hyperbolic cosine of every\n element in the tensor. Input range is `[-inf, inf]` and output range\n is `[1, inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])\n tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"cumsum",path:"./tf/math/cumsum.md",desc:"Compute the cumulative sum of the tensor `x` along `axis`.",type:"Functions",docs:"Compute the cumulative sum of the tensor `x` along `axis`.\n\n By default, this op performs an inclusive cumsum, which means that the first\n element of the input is identical to the first element of the output:\n For example:\n\n >>> # tf.cumsum([a, b, c]) # [a, a + b, a + b + c]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x)\n \n\n >>> # using varying `axis` values\n >>> y = tf.constant([[2, 4, 6, 8], [1,3,5,7]])\n >>> tf.cumsum(y, axis=0)\n \n >>> tf.cumsum(y, axis=1)\n \n\n By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed\n instead:\n\n >>> # tf.cumsum([a, b, c], exclusive=True) => [0, a, a + b]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, exclusive=True)\n \n\n By setting the `reverse` kwarg to `True`, the cumsum is performed in the\n opposite direction:\n\n >>> # tf.cumsum([a, b, c], reverse=True) # [a + b + c, b + c, c]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, reverse=True)\n \n\n This is more efficient than using separate `tf.reverse` ops.\n The `reverse` and `exclusive` kwargs can also be combined:\n\n >>> # tf.cumsum([a, b, c], exclusive=True, reverse=True) # [b + c, c, 0]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, exclusive=True, reverse=True)\n \n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`,\n `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n `complex128`, `qint8`, `quint8`, `qint32`, `half`.\n axis: A `Tensor` of type `int32` (default: 0). Must be in the range\n `[-rank(x), rank(x))`.\n exclusive: If `True`, perform exclusive cumsum.\n reverse: A `bool` (default: False).\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"custom_gradient",path:"./tf/custom_gradient.md",desc:"Decorator to define a function with a custom gradient.",type:"Functions",docs:`Decorator to define a function with a custom gradient. + `},{name:"cos",path:"./tf/math/cos.md",desc:"Computes cos of x element-wise.",type:"Functions",docs:'Computes cos of x element-wise.\n\n Given an input tensor, this function computes cosine of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `[-1,1]`. If input lies outside the boundary, `nan`\n is returned.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"cosh",path:"./tf/math/cosh.md",desc:"Computes hyperbolic cosine of x element-wise.",type:"Functions",docs:'Computes hyperbolic cosine of x element-wise.\n\n Given an input tensor, this function computes hyperbolic cosine of every\n element in the tensor. Input range is `[-inf, inf]` and output range\n is `[1, inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])\n tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"cumsum",path:"./tf/math/cumsum.md",desc:"Compute the cumulative sum of the tensor `x` along `axis`.",type:"Functions",docs:"Compute the cumulative sum of the tensor `x` along `axis`.\n\n By default, this op performs an inclusive cumsum, which means that the first\n element of the input is identical to the first element of the output:\n For example:\n\n ```>>> # tf.cumsum([a, b, c]) # [a, a + b, a + b + c]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x)\n \n\n```\n ```>>> # using varying `axis` values\n >>> y = tf.constant([[2, 4, 6, 8], [1,3,5,7]])\n >>> tf.cumsum(y, axis=0)\n \n >>> tf.cumsum(y, axis=1)\n \n\n```\n By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed\n instead:\n\n ```>>> # tf.cumsum([a, b, c], exclusive=True) => [0, a, a + b]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, exclusive=True)\n \n\n```\n By setting the `reverse` kwarg to `True`, the cumsum is performed in the\n opposite direction:\n\n ```>>> # tf.cumsum([a, b, c], reverse=True) # [a + b + c, b + c, c]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, reverse=True)\n \n\n```\n This is more efficient than using separate `tf.reverse` ops.\n The `reverse` and `exclusive` kwargs can also be combined:\n\n ```>>> # tf.cumsum([a, b, c], exclusive=True, reverse=True) # [b + c, c, 0]\n >>> x = tf.constant([2, 4, 6, 8])\n >>> tf.cumsum(x, exclusive=True, reverse=True)\n \n\n```\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`,\n `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,\n `complex128`, `qint8`, `quint8`, `qint32`, `half`.\n axis: A `Tensor` of type `int32` (default: 0). Must be in the range\n `[-rank(x), rank(x))`.\n exclusive: If `True`, perform exclusive cumsum.\n reverse: A `bool` (default: False).\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"custom_gradient",path:"./tf/custom_gradient.md",desc:"Decorator to define a function with a custom gradient.",type:"Functions",docs:`Decorator to define a function with a custom gradient. This decorator allows fine grained control over the gradients of a sequence for operations. This may be useful for multiple reasons, including providing @@ -2492,7 +2120,7 @@ Note: The output of this function should be used. If it is not, a warning will b function must also return the same number of variables. We take the function \`z = x * y\` as an example. - >>> @tf.custom_gradient + \`\`\`>>> @tf.custom_gradient ... def bar(x, y): ... def grad(upstream): ... dz_dx = y @@ -2513,6 +2141,7 @@ Note: The output of this function should be used. If it is not, a warning will b >>> tape.gradient(z, y) +\`\`\` Nesting custom gradients can lead to unintuitive results. The default behavior does not correspond to n-th order derivatives. For example @@ -2558,7 +2187,7 @@ Note: The output of this function should be used. If it is not, a warning will b require special handling because they are effectively inputs of the forward function. - >>> weights = tf.Variable(tf.ones([2])) # Trainable variable weights + \`\`\`>>> weights = tf.Variable(tf.ones([2])) # Trainable variable weights >>> @tf.custom_gradient ... def linear_poly(x): ... # Creating polynomial @@ -2594,6 +2223,7 @@ Note: The output of this function should be used. If it is not, a warning will b >>> tape.gradient(poly, weights) +\`\`\` Above example illustrates usage of trainable variable \`weights\`. In the example, the inner \`grad_fn\` accepts an extra \`variables\` input parameter and also returns an extra \`grad_vars\` output. That extra argument @@ -2710,12 +2340,13 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> x = tf.constant([16, 12, 11]) + \`\`\`>>> x = tf.constant([16, 12, 11]) >>> y = tf.constant([4, 6, 2]) >>> tf.divide(x,y) +\`\`\` Args: x: A \`Tensor\` y: A \`Tensor\` @@ -2808,7 +2439,7 @@ Note: The output of this function should be used. If it is not, a warning will b * \`hypothesis\` is a \`tf.SparseTensor\` of shape \`[2, 1, 1]\` * \`truth\` is a \`tf.SparseTensor\` of shape \`[2, 2, 2]\` - >>> hypothesis = tf.SparseTensor( + \`\`\`>>> hypothesis = tf.SparseTensor( ... [[0, 0, 0], ... [1, 0, 0]], ... ["a", "b"], @@ -2825,6 +2456,7 @@ Note: The output of this function should be used. If it is not, a warning will b array([[inf, 1. ], [0.5, 1. ]], dtype=float32)> +\`\`\` The operation returns a dense Tensor of shape \`[2, 2]\` with edit distances normalized by \`truth\` lengths. @@ -2948,67 +2580,75 @@ Note: The output of this function should be used. If it is not, a warning will b **Matrix multiplication** - >>> m0 = tf.random.normal(shape=[2, 3]) + \`\`\`>>> m0 = tf.random.normal(shape=[2, 3]) >>> m1 = tf.random.normal(shape=[3, 5]) >>> e = tf.einsum('ij,jk->ik', m0, m1) >>> # output[i,k] = sum_j m0[i,j] * m1[j, k] >>> print(e.shape) (2, 5) +\`\`\` Repeated indices are summed if the output indices are not specified. - >>> e = tf.einsum('ij,jk', m0, m1) # output[i,k] = sum_j m0[i,j] * m1[j, k] + \`\`\`>>> e = tf.einsum('ij,jk', m0, m1) # output[i,k] = sum_j m0[i,j] * m1[j, k] >>> print(e.shape) (2, 5) +\`\`\` **Dot product** - >>> u = tf.random.normal(shape=[5]) + \`\`\`>>> u = tf.random.normal(shape=[5]) >>> v = tf.random.normal(shape=[5]) >>> e = tf.einsum('i,i->', u, v) # output = sum_i u[i]*v[i] >>> print(e.shape) () +\`\`\` **Outer product** - >>> u = tf.random.normal(shape=[3]) + \`\`\`>>> u = tf.random.normal(shape=[3]) >>> v = tf.random.normal(shape=[5]) >>> e = tf.einsum('i,j->ij', u, v) # output[i,j] = u[i]*v[j] >>> print(e.shape) (3, 5) +\`\`\` **Transpose** - >>> m = tf.ones(2,3) + \`\`\`>>> m = tf.ones(2,3) >>> e = tf.einsum('ij->ji', m0) # output[j,i] = m0[i,j] >>> print(e.shape) (3, 2) +\`\`\` **Diag** - >>> m = tf.reshape(tf.range(9), [3,3]) + \`\`\`>>> m = tf.reshape(tf.range(9), [3,3]) >>> diag = tf.einsum('ii->i', m) >>> print(diag.shape) (3,) +\`\`\` **Trace** - >>> # Repeated indices are summed. + \`\`\`>>> # Repeated indices are summed. >>> trace = tf.einsum('ii', m) # output[j,i] = trace(m) = sum_i m[i, i] >>> assert trace == sum(diag) >>> print(trace.shape) () +\`\`\` **Batch matrix multiplication** - >>> s = tf.random.normal(shape=[7,5,3]) + \`\`\`>>> s = tf.random.normal(shape=[7,5,3]) >>> t = tf.random.normal(shape=[7,3,2]) >>> e = tf.einsum('bij,bjk->bik', s, t) >>> # output[a,i,k] = sum_j s[a,i,j] * t[a, j, k] >>> print(e.shape) (7, 5, 2) +\`\`\` This method does not support broadcasting on named-axes. All axes with matching labels should have the same length. If you have length-1 axes, use \`tf.squeeze\` or \`tf.reshape\` to eliminate them. @@ -3020,20 +2660,22 @@ Note: The output of this function should be used. If it is not, a warning will b For example, to perform a NumPy-style broadcasting-batch-matrix multiplication where the matrix multiply acts on the last two axes of the input, use: - >>> s = tf.random.normal(shape=[11, 7, 5, 3]) + \`\`\`>>> s = tf.random.normal(shape=[11, 7, 5, 3]) >>> t = tf.random.normal(shape=[11, 7, 3, 2]) >>> e = tf.einsum('...ij,...jk->...ik', s, t) >>> print(e.shape) (11, 7, 5, 2) +\`\`\` Einsum **will** broadcast over axes covered by the ellipsis. - >>> s = tf.random.normal(shape=[11, 1, 5, 3]) + \`\`\`>>> s = tf.random.normal(shape=[11, 1, 5, 3]) >>> t = tf.random.normal(shape=[1, 7, 3, 2]) >>> e = tf.einsum('...ij,...jk->...ik', s, t) >>> print(e.shape) (11, 7, 5, 2) +\`\`\` Args: equation: a \`str\` describing the contraction, in the same format as \`numpy.einsum\`. @@ -3058,24 +2700,27 @@ Note: The output of this function should be used. If it is not, a warning will b is compatible with the \`shape\` argument. See \`tf.TensorShape.is_compatible_with\` for details. - >>> x = tf.constant([[1, 2, 3], + \`\`\`>>> x = tf.constant([[1, 2, 3], ... [4, 5, 6]]) >>> x = tf.ensure_shape(x, [2, 3]) +\`\`\` Use \`None\` for unknown dimensions: - >>> x = tf.ensure_shape(x, [None, 3]) + \`\`\`>>> x = tf.ensure_shape(x, [None, 3]) >>> x = tf.ensure_shape(x, [2, None]) +\`\`\` If the tensor's shape is not compatible with the \`shape\` argument, an error is raised: - >>> x = tf.ensure_shape(x, [5]) + \`\`\`>>> x = tf.ensure_shape(x, [5]) Traceback (most recent call last): ... tf.errors.InvalidArgumentError: Shape of tensor dummy_input [3] is not compatible with expected shape [5]. [Op:EnsureShape] +\`\`\` During graph construction (typically tracing a \`tf.function\`), \`tf.ensure_shape\` updates the static-shape of the **result** tensor by merging the two shapes. See \`tf.TensorShape.merge_with\` for details. @@ -3086,24 +2731,27 @@ Note: The output of this function should be used. If it is not, a warning will b The following trivial \`tf.function\` prints the input tensor's static-shape before and after \`ensure_shape\` is applied. - >>> @tf.function + \`\`\`>>> @tf.function ... def f(tensor): ... print("Static-shape before:", tensor.shape) ... tensor = tf.ensure_shape(tensor, [None, 3]) ... print("Static-shape after:", tensor.shape) ... return tensor +\`\`\` This lets you see the effect of \`tf.ensure_shape\` when the function is traced: - >>> cf = f.get_concrete_function(tf.TensorSpec([None, None])) + \`\`\`>>> cf = f.get_concrete_function(tf.TensorSpec([None, None])) Static-shape before: (None, None) Static-shape after: (None, 3) - >>> cf(tf.zeros([3, 3])) # Passes +\`\`\` + \`\`\`>>> cf(tf.zeros([3, 3])) # Passes >>> cf(tf.constant([1, 2, 3])) # fails Traceback (most recent call last): ... InvalidArgumentError: Shape of tensor x [3] is not compatible with expected shape [3,3]. +\`\`\` The above example raises \`tf.errors.InvalidArgumentError\`, because \`x\`'s shape, \`(3,)\`, is not compatible with the \`shape\` argument, \`(None, 3)\` @@ -3120,7 +2768,7 @@ Note: The output of this function should be used. If it is not, a warning will b For example, of loading images of a known size: - >>> @tf.function + \`\`\`>>> @tf.function ... def decode_image(png): ... image = tf.image.decode_png(png, channels=3) ... # the \`print\` executes during tracing. @@ -3129,23 +2777,26 @@ Note: The output of this function should be used. If it is not, a warning will b ... print("Final shape: ", image.shape) ... return image +\`\`\` When tracing a function, no ops are being executed, shapes may be unknown. See the [Concrete Functions Guide](https://www.tensorflow.org/guide/concrete_function) for details. - >>> concrete_decode = decode_image.get_concrete_function( + \`\`\`>>> concrete_decode = decode_image.get_concrete_function( ... tf.TensorSpec([], dtype=tf.string)) Initial shape: (None, None, 3) Final shape: (28, 28, 3) - >>> image = tf.random.uniform(maxval=255, shape=[28, 28, 3], dtype=tf.int32) +\`\`\` + \`\`\`>>> image = tf.random.uniform(maxval=255, shape=[28, 28, 3], dtype=tf.int32) >>> image = tf.cast(image,tf.uint8) >>> png = tf.image.encode_png(image) >>> image2 = concrete_decode(png) >>> print(image2.shape) (28, 28, 3) - >>> image = tf.concat([image,image], axis=0) +\`\`\` + \`\`\`>>> image = tf.concat([image,image], axis=0) >>> print(image.shape) (56, 28, 3) >>> png = tf.image.encode_png(image) @@ -3155,10 +2806,11 @@ Note: The output of this function should be used. If it is not, a warning will b tf.errors.InvalidArgumentError: Shape of tensor DecodePng [56,28,3] is not compatible with expected shape [28,28,3]. +\`\`\` Caution: if you don't use the result of \`tf.ensure_shape\` the check may not run. - >>> @tf.function + \`\`\`>>> @tf.function ... def bad_decode_image(png): ... image = tf.image.decode_png(png, channels=3) ... # the \`print\` executes during tracing. @@ -3168,12 +2820,14 @@ Note: The output of this function should be used. If it is not, a warning will b ... print("Final shape: ", image.shape) ... return image - >>> image = bad_decode_image(png) +\`\`\` + \`\`\`>>> image = bad_decode_image(png) Initial shape: (None, None, 3) Final shape: (None, None, 3) >>> print(image.shape) (56, 28, 3) +\`\`\` Args: x: A \`Tensor\`. shape: A \`TensorShape\` representing the shape of this tensor, a @@ -3195,16 +2849,18 @@ Note: The output of this function should be used. If it is not, a warning will b For example: - >>> x = tf.constant([2, 4]) + \`\`\`>>> x = tf.constant([2, 4]) >>> y = tf.constant(2) >>> tf.math.equal(x, y) - >>> x = tf.constant([2, 4]) +\`\`\` + \`\`\`>>> x = tf.constant([2, 4]) >>> y = tf.constant([2, 4]) >>> tf.math.equal(x, y) +\`\`\` Args: x: A \`tf.Tensor\` or \`tf.sparse.SparseTensor\` or \`tf.IndexedSlices\`. y: A \`tf.Tensor\` or \`tf.sparse.SparseTensor\` or \`tf.IndexedSlices\`. @@ -3228,12 +2884,13 @@ Note: The output of this function should be used. If it is not, a warning will b General case: - >>> print(tf.executing_eagerly()) + \`\`\`>>> print(tf.executing_eagerly()) True +\`\`\` Inside \`tf.function\`: - >>> @tf.function + \`\`\`>>> @tf.function ... def fn(): ... with tf.init_scope(): ... print(tf.executing_eagerly()) @@ -3242,9 +2899,10 @@ Note: The output of this function should be used. If it is not, a warning will b True False +\`\`\` Inside \`tf.function\` after \`tf.config.run_functions_eagerly(True)\` is called: - >>> tf.config.run_functions_eagerly(True) + \`\`\`>>> tf.config.run_functions_eagerly(True) >>> @tf.function ... def fn(): ... with tf.init_scope(): @@ -3255,15 +2913,17 @@ Note: The output of this function should be used. If it is not, a warning will b True >>> tf.config.run_functions_eagerly(False) +\`\`\` Inside a transformation function for \`tf.dataset\`: - >>> def data_fn(x): + \`\`\`>>> def data_fn(x): ... print(tf.executing_eagerly()) ... return x >>> dataset = tf.data.Dataset.range(100) >>> dataset = dataset.map(data_fn) False +\`\`\` Returns: \`True\` if the current thread has eager execution enabled. `},{name:"exp",path:"./tf/math/exp.md",desc:"Computes exponential of x element-wise. \\\\(y = e^x\\\\",type:"Functions",docs:`Computes exponential of x element-wise. \\\\(y = e^x\\\\). @@ -3273,15 +2933,17 @@ Note: The output of this function should be used. If it is not, a warning will b \\\\(e\\\\) denotes Euler's number and is approximately equal to 2.718281. Output is positive for any real input. - >>> x = tf.constant(2.0) + \`\`\`>>> x = tf.constant(2.0) >>> tf.math.exp(x) - >>> x = tf.constant([2.0, 8.0]) +\`\`\` + \`\`\`>>> x = tf.constant([2.0, 8.0]) >>> tf.math.exp(x) +\`\`\` For complex numbers, the exponential value is calculated as $$ e^{x+iy} = {e^x} {e^{iy}} = {e^x} ({\\cos (y) + i \\sin (y)}) @@ -3292,11 +2954,12 @@ Note: The output of this function should be used. If it is not, a warning will b e^1 (\\cos (1) + i \\sin (1)) = 2.7182817 \\times (0.5403023+0.84147096j) $$ - >>> x = tf.constant(1 + 1j) + \`\`\`>>> x = tf.constant(1 + 1j) >>> tf.math.exp(x) +\`\`\` Args: x: A \`tf.Tensor\`. Must be one of the following types: \`bfloat16\`, \`half\`, \`float32\`, \`float64\`, \`complex64\`, \`complex128\`. @@ -3308,88 +2971,7 @@ Note: The output of this function should be used. If it is not, a warning will b @compatibility(numpy) Equivalent to np.exp @end_compatibility - `},{name:"expand_dims",path:"./tf/expand_dims.md",desc:"Returns a tensor with a length 1 axis inserted at index `axis`.",type:"Functions",docs:`Returns a tensor with a length 1 axis inserted at index \`axis\`. - - Given a tensor \`input\`, this operation inserts a dimension of length 1 at the - dimension index \`axis\` of \`input\`'s shape. The dimension index follows Python - indexing rules: It's zero-based, a negative index it is counted backward - from the end. - - This operation is useful to: - - * Add an outer "batch" dimension to a single element. - * Align axes for broadcasting. - * To add an inner vector length axis to a tensor of scalars. - - For example: - - If you have a single image of shape \`[height, width, channels]\`: - - >>> image = tf.zeros([10,10,3]) - - You can add an outer \`batch\` axis by passing \`axis=0\`: - - >>> tf.expand_dims(image, axis=0).shape.as_list() - [1, 10, 10, 3] - - The new axis location matches Python \`list.insert(axis, 1)\`: - - >>> tf.expand_dims(image, axis=1).shape.as_list() - [10, 1, 10, 3] - - Following standard Python indexing rules, a negative \`axis\` counts from the - end so \`axis=-1\` adds an inner most dimension: - - >>> tf.expand_dims(image, -1).shape.as_list() - [10, 10, 3, 1] - - This operation requires that \`axis\` is a valid index for \`input.shape\`, - following Python indexing rules: - - \`\`\` - -1-tf.rank(input) <= axis <= tf.rank(input) - \`\`\` - - This operation is related to: - - * \`tf.squeeze\`, which removes dimensions of size 1. - * \`tf.reshape\`, which provides more flexible reshaping capability. - * \`tf.sparse.expand_dims\`, which provides this functionality for - \`tf.SparseTensor\` - - Args: - input: A \`Tensor\`. - axis: Integer specifying the dimension index at which to expand the - shape of \`input\`. Given an input of D dimensions, \`axis\` must be in range - \`[-(D+1), D]\` (inclusive). - name: Optional string. The name of the output \`Tensor\`. - - Returns: - A tensor with the same data as \`input\`, with an additional dimension - inserted at the index specified by \`axis\`. - - Raises: - TypeError: If \`axis\` is not specified. - InvalidArgumentError: If \`axis\` is out of range \`[-(D+1), D]\`. - `},{name:"extract_volume_patches",path:"./tf/extract_volume_patches.md",desc:'Extract `patches` from `input` and put them in the `"depth"` output dimension. 3D extension of `extract_image_patches`.',type:"Functions",docs:'Extract `patches` from `input` and put them in the `"depth"` output dimension. 3D extension of `extract_image_patches`.\n\n Args:\n input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n 5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`.\n ksizes: A list of `ints` that has length `>= 5`.\n The size of the sliding window for each dimension of `input`.\n strides: A list of `ints` that has length `>= 5`.\n 1-D of length 5. How far the centers of two consecutive patches are in\n `input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`.\n padding: A `string` from: `"SAME", "VALID"`.\n The type of padding algorithm to use.\n\n The size-related attributes are specified as follows:\n\n ```python\n ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]\n strides = [1, stride_planes, strides_rows, strides_cols, 1]\n ```\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"eye",path:"./tf/eye.md",desc:"Construct an identity matrix, or a batch of matrices.",type:"Functions",docs:'Construct an identity matrix, or a batch of matrices.\n\n See also `tf.ones`, `tf.zeros`, `tf.fill`, `tf.one_hot`.\n\n ```python\n # Construct one identity matrix.\n tf.eye(2)\n ==> [[1., 0.],\n [0., 1.]]\n\n # Construct a batch of 3 identity matrices, each 2 x 2.\n # batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.\n batch_identity = tf.eye(2, batch_shape=[3])\n\n # Construct one 2 x 3 "identity" matrix\n tf.eye(2, num_columns=3)\n ==> [[ 1., 0., 0.],\n [ 0., 1., 0.]]\n ```\n\n Args:\n num_rows: Non-negative `int32` scalar `Tensor` giving the number of rows\n in each batch matrix.\n num_columns: Optional non-negative `int32` scalar `Tensor` giving the number\n of columns in each batch matrix. Defaults to `num_rows`.\n batch_shape: A list or tuple of Python integers or a 1-D `int32` `Tensor`.\n If provided, the returned `Tensor` will have leading batch dimensions of\n this shape.\n dtype: The type of an element in the resulting `Tensor`\n name: A name for this `Op`. Defaults to "eye".\n\n Returns:\n A `Tensor` of shape `batch_shape + [num_rows, num_columns]`\n '},{name:"fill",path:"./tf/fill.md",desc:"Creates a tensor filled with a scalar value.",type:"Functions",docs:"Creates a tensor filled with a scalar value.\n\n See also `tf.ones`, `tf.zeros`, `tf.one_hot`, `tf.eye`.\n\n This operation creates a tensor of shape `dims` and fills it with `value`.\n\n For example:\n\n >>> tf.fill([2, 3], 9)\n \n\n `tf.fill` evaluates at graph runtime and supports dynamic shapes based on\n other runtime `tf.Tensors`, unlike `tf.constant(value, shape=dims)`, which\n embeds the value as a `Const` node.\n\n Args:\n dims: A 1-D sequence of non-negative numbers. Represents the shape of the\n output `tf.Tensor`. Entries should be of type: `int32`, `int64`.\n value: A value to fill the returned `tf.Tensor`.\n name: Optional string. The name of the output `tf.Tensor`.\n\n Returns:\n A `tf.Tensor` with shape `dims` and the same dtype as `value`.\n\n Raises:\n InvalidArgumentError: `dims` contains negative entries.\n NotFoundError: `dims` contains non-integer entries.\n\n @compatibility(numpy)\n Similar to `np.full`. In `numpy`, more parameters are supported. Passing a\n number argument as the shape (`np.full(5, value)`) is valid in `numpy` for\n specifying a 1-D shaped result, while TensorFlow does not support this syntax.\n @end_compatibility\n "},{name:"fingerprint",path:"./tf/fingerprint.md",desc:"Generates fingerprint values.",type:"Functions",docs:"Generates fingerprint values.\n\n Generates fingerprint values of `data`.\n\n Fingerprint op considers the first dimension of `data` as the batch dimension,\n and `output[i]` contains the fingerprint value generated from contents in\n `data[i, ...]` for all `i`.\n\n Fingerprint op writes fingerprint values as byte arrays. For example, the\n default method `farmhash64` generates a 64-bit fingerprint value at a time.\n This 8-byte value is written out as an `tf.uint8` array of size 8, in\n little-endian order.\n\n For example, suppose that `data` has data type `tf.int32` and shape (2, 3, 4),\n and that the fingerprint method is `farmhash64`. In this case, the output\n shape is (2, 8), where 2 is the batch dimension size of `data`, and 8 is the\n size of each fingerprint value in bytes. `output[0, :]` is generated from\n 12 integers in `data[0, :, :]` and similarly `output[1, :]` is generated from\n other 12 integers in `data[1, :, :]`.\n\n Note that this op fingerprints the raw underlying buffer, and it does not\n fingerprint Tensor's metadata such as data type and/or shape. For example, the\n fingerprint values are invariant under reshapes and bitcasts as long as the\n batch dimension remain the same:\n\n ```python\n tf.fingerprint(data) == tf.fingerprint(tf.reshape(data, ...))\n tf.fingerprint(data) == tf.fingerprint(tf.bitcast(data, ...))\n ```\n\n For string data, one should expect `tf.fingerprint(data) !=\n tf.fingerprint(tf.string.reduce_join(data))` in general.\n\n Args:\n data: A `Tensor`. Must have rank 1 or higher.\n method: A `Tensor` of type `tf.string`. Fingerprint method used by this op.\n Currently available method is `farmhash64`.\n name: A name for the operation (optional).\n\n Returns:\n A two-dimensional `Tensor` of type `tf.uint8`. The first dimension equals to\n `data`'s first dimension, and the second dimension size depends on the\n fingerprint algorithm.\n "},{name:"floor",path:"./tf/math/floor.md",desc:"Returns element-wise largest integer not greater than x.",type:"Functions",docs:`Returns element-wise largest integer not greater than x. - - Both input range is \`(-inf, inf)\` and the - output range consists of all integer values. - - For example: - - >>> x = tf.constant([1.3324, -1.5, 5.555, -2.532, 0.99, float("inf")]) - >>> tf.floor(x).numpy() - array([ 1., -2., 5., -3., 0., inf], dtype=float32) - - Args: - x: A \`Tensor\`. Must be one of the following types: \`bfloat16\`, \`half\`, - \`float32\`, \`float64\`. - name: A name for the operation (optional). - - Returns: - A \`Tensor\`. Has the same type as x. - `},{name:"foldl",path:"./tf/foldl.md",desc:"foldl on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values",type:"Functions",docs:`foldl on the list of tensors unpacked from \`elems\` on dimension 0. (deprecated argument values) + `},{name:"expand_dims",path:"./tf/expand_dims.md",desc:"Returns a tensor with a length 1 axis inserted at index `axis`.",type:"Functions",docs:"Returns a tensor with a length 1 axis inserted at index `axis`.\n\n Given a tensor `input`, this operation inserts a dimension of length 1 at the\n dimension index `axis` of `input`'s shape. The dimension index follows Python\n indexing rules: It's zero-based, a negative index it is counted backward\n from the end.\n\n This operation is useful to:\n\n * Add an outer \"batch\" dimension to a single element.\n * Align axes for broadcasting.\n * To add an inner vector length axis to a tensor of scalars.\n\n For example:\n\n If you have a single image of shape `[height, width, channels]`:\n\n ```>>> image = tf.zeros([10,10,3])\n\n```\n You can add an outer `batch` axis by passing `axis=0`:\n\n ```>>> tf.expand_dims(image, axis=0).shape.as_list()\n [1, 10, 10, 3]\n\n```\n The new axis location matches Python `list.insert(axis, 1)`:\n\n ```>>> tf.expand_dims(image, axis=1).shape.as_list()\n [10, 1, 10, 3]\n\n```\n Following standard Python indexing rules, a negative `axis` counts from the\n end so `axis=-1` adds an inner most dimension:\n\n ```>>> tf.expand_dims(image, -1).shape.as_list()\n [10, 10, 3, 1]\n\n```\n This operation requires that `axis` is a valid index for `input.shape`,\n following Python indexing rules:\n\n ```\n -1-tf.rank(input) <= axis <= tf.rank(input)\n ```\n\n This operation is related to:\n\n * `tf.squeeze`, which removes dimensions of size 1.\n * `tf.reshape`, which provides more flexible reshaping capability.\n * `tf.sparse.expand_dims`, which provides this functionality for\n `tf.SparseTensor`\n\n Args:\n input: A `Tensor`.\n axis: Integer specifying the dimension index at which to expand the\n shape of `input`. Given an input of D dimensions, `axis` must be in range\n `[-(D+1), D]` (inclusive).\n name: Optional string. The name of the output `Tensor`.\n\n Returns:\n A tensor with the same data as `input`, with an additional dimension\n inserted at the index specified by `axis`.\n\n Raises:\n TypeError: If `axis` is not specified.\n InvalidArgumentError: If `axis` is out of range `[-(D+1), D]`.\n "},{name:"extract_volume_patches",path:"./tf/extract_volume_patches.md",desc:'Extract `patches` from `input` and put them in the `"depth"` output dimension. 3D extension of `extract_image_patches`.',type:"Functions",docs:'Extract `patches` from `input` and put them in the `"depth"` output dimension. 3D extension of `extract_image_patches`.\n\n Args:\n input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n 5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`.\n ksizes: A list of `ints` that has length `>= 5`.\n The size of the sliding window for each dimension of `input`.\n strides: A list of `ints` that has length `>= 5`.\n 1-D of length 5. How far the centers of two consecutive patches are in\n `input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`.\n padding: A `string` from: `"SAME", "VALID"`.\n The type of padding algorithm to use.\n\n The size-related attributes are specified as follows:\n\n ```python\n ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]\n strides = [1, stride_planes, strides_rows, strides_cols, 1]\n ```\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"eye",path:"./tf/eye.md",desc:"Construct an identity matrix, or a batch of matrices.",type:"Functions",docs:'Construct an identity matrix, or a batch of matrices.\n\n See also `tf.ones`, `tf.zeros`, `tf.fill`, `tf.one_hot`.\n\n ```python\n # Construct one identity matrix.\n tf.eye(2)\n ==> [[1., 0.],\n [0., 1.]]\n\n # Construct a batch of 3 identity matrices, each 2 x 2.\n # batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.\n batch_identity = tf.eye(2, batch_shape=[3])\n\n # Construct one 2 x 3 "identity" matrix\n tf.eye(2, num_columns=3)\n ==> [[ 1., 0., 0.],\n [ 0., 1., 0.]]\n ```\n\n Args:\n num_rows: Non-negative `int32` scalar `Tensor` giving the number of rows\n in each batch matrix.\n num_columns: Optional non-negative `int32` scalar `Tensor` giving the number\n of columns in each batch matrix. Defaults to `num_rows`.\n batch_shape: A list or tuple of Python integers or a 1-D `int32` `Tensor`.\n If provided, the returned `Tensor` will have leading batch dimensions of\n this shape.\n dtype: The type of an element in the resulting `Tensor`\n name: A name for this `Op`. Defaults to "eye".\n\n Returns:\n A `Tensor` of shape `batch_shape + [num_rows, num_columns]`\n '},{name:"fill",path:"./tf/fill.md",desc:"Creates a tensor filled with a scalar value.",type:"Functions",docs:"Creates a tensor filled with a scalar value.\n\n See also `tf.ones`, `tf.zeros`, `tf.one_hot`, `tf.eye`.\n\n This operation creates a tensor of shape `dims` and fills it with `value`.\n\n For example:\n\n ```>>> tf.fill([2, 3], 9)\n \n\n```\n `tf.fill` evaluates at graph runtime and supports dynamic shapes based on\n other runtime `tf.Tensors`, unlike `tf.constant(value, shape=dims)`, which\n embeds the value as a `Const` node.\n\n Args:\n dims: A 1-D sequence of non-negative numbers. Represents the shape of the\n output `tf.Tensor`. Entries should be of type: `int32`, `int64`.\n value: A value to fill the returned `tf.Tensor`.\n name: Optional string. The name of the output `tf.Tensor`.\n\n Returns:\n A `tf.Tensor` with shape `dims` and the same dtype as `value`.\n\n Raises:\n InvalidArgumentError: `dims` contains negative entries.\n NotFoundError: `dims` contains non-integer entries.\n\n @compatibility(numpy)\n Similar to `np.full`. In `numpy`, more parameters are supported. Passing a\n number argument as the shape (`np.full(5, value)`) is valid in `numpy` for\n specifying a 1-D shaped result, while TensorFlow does not support this syntax.\n @end_compatibility\n "},{name:"fingerprint",path:"./tf/fingerprint.md",desc:"Generates fingerprint values.",type:"Functions",docs:"Generates fingerprint values.\n\n Generates fingerprint values of `data`.\n\n Fingerprint op considers the first dimension of `data` as the batch dimension,\n and `output[i]` contains the fingerprint value generated from contents in\n `data[i, ...]` for all `i`.\n\n Fingerprint op writes fingerprint values as byte arrays. For example, the\n default method `farmhash64` generates a 64-bit fingerprint value at a time.\n This 8-byte value is written out as an `tf.uint8` array of size 8, in\n little-endian order.\n\n For example, suppose that `data` has data type `tf.int32` and shape (2, 3, 4),\n and that the fingerprint method is `farmhash64`. In this case, the output\n shape is (2, 8), where 2 is the batch dimension size of `data`, and 8 is the\n size of each fingerprint value in bytes. `output[0, :]` is generated from\n 12 integers in `data[0, :, :]` and similarly `output[1, :]` is generated from\n other 12 integers in `data[1, :, :]`.\n\n Note that this op fingerprints the raw underlying buffer, and it does not\n fingerprint Tensor's metadata such as data type and/or shape. For example, the\n fingerprint values are invariant under reshapes and bitcasts as long as the\n batch dimension remain the same:\n\n ```python\n tf.fingerprint(data) == tf.fingerprint(tf.reshape(data, ...))\n tf.fingerprint(data) == tf.fingerprint(tf.bitcast(data, ...))\n ```\n\n For string data, one should expect `tf.fingerprint(data) !=\n tf.fingerprint(tf.string.reduce_join(data))` in general.\n\n Args:\n data: A `Tensor`. Must have rank 1 or higher.\n method: A `Tensor` of type `tf.string`. Fingerprint method used by this op.\n Currently available method is `farmhash64`.\n name: A name for the operation (optional).\n\n Returns:\n A two-dimensional `Tensor` of type `tf.uint8`. The first dimension equals to\n `data`'s first dimension, and the second dimension size depends on the\n fingerprint algorithm.\n "},{name:"floor",path:"./tf/math/floor.md",desc:"Returns element-wise largest integer not greater than x.",type:"Functions",docs:'Returns element-wise largest integer not greater than x.\n\n Both input range is `(-inf, inf)` and the\n output range consists of all integer values.\n\n For example:\n\n ```>>> x = tf.constant([1.3324, -1.5, 5.555, -2.532, 0.99, float("inf")])\n >>> tf.floor(x).numpy()\n array([ 1., -2., 5., -3., 0., inf], dtype=float32)\n\n```\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`,\n `float32`, `float64`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as x.\n '},{name:"foldl",path:"./tf/foldl.md",desc:"foldl on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values",type:"Functions",docs:`foldl on the list of tensors unpacked from \`elems\` on dimension 0. (deprecated argument values) Deprecated: SOME ARGUMENT VALUES ARE DEPRECATED: \`(back_prop=False)\`. They will be removed in a future version. Instructions for updating: @@ -3521,7 +3103,7 @@ known limitations. Example usage: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x, y): ... return x ** 2 + y >>> x = tf.constant([2, 3]) @@ -3529,6 +3111,7 @@ Example usage: >>> f(x, y) +\`\`\` The trace-compilation allows non-TensorFlow operations to execute, but under special conditions. In general, only TensorFlow operations are guaranteed to run and create fresh results whenever the \`GenericFunction\` is called. @@ -3538,7 +3121,7 @@ run and create fresh results whenever the \`GenericFunction\` is called. \`func\` may use data-dependent Python control flow statements, including \`if\`, \`for\`, \`while\` \`break\`, \`continue\` and \`return\`: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x): ... if tf.reduce_sum(x) > 0: ... return x * x @@ -3547,9 +3130,10 @@ run and create fresh results whenever the \`GenericFunction\` is called. >>> f(tf.constant(-2)) +\`\`\` \`func\`'s closure may include \`tf.Tensor\` and \`tf.Variable\` objects: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(): ... return x ** 2 + y >>> x = tf.constant([-2, -3]) @@ -3557,10 +3141,11 @@ run and create fresh results whenever the \`GenericFunction\` is called. >>> f() +\`\`\` \`func\` may also use ops with side effects, such as \`tf.print\`, \`tf.Variable\` and others: ->>> v = tf.Variable(1) +\`\`\`>>> v = tf.Variable(1) >>> @tf.function ... def f(x): ... for i in tf.range(x): @@ -3569,12 +3154,13 @@ and others: >>> v +\`\`\` Important: Any Python side-effects (appending to a list, printing with \`print\`, etc) will only happen once, when \`func\` is traced. To have side-effects executed into your \`tf.function\` they need to be written as TF ops: ->>> l = [] +\`\`\`>>> l = [] >>> @tf.function ... def f(x): ... for i in x: @@ -3583,9 +3169,10 @@ as TF ops: >>> l [] +\`\`\` Instead, use TensorFlow collections like \`tf.TensorArray\`: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x): ... ta = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True) ... for i in range(len(x)): @@ -3594,6 +3181,7 @@ Instead, use TensorFlow collections like \`tf.TensorArray\`: >>> f(tf.constant([1, 2, 3])) +\`\`\` ## \`tf.function\` creates polymorphic callables Internally, \`tf.types.experimental.GenericFunction\` may contain multiple @@ -3615,12 +3203,13 @@ same arguments as \`func\` and returns a \`tf.types.experimental.ConcreteFunction\`. \`ConcreteFunction\`s are backed by a single \`tf.Graph\`: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x): ... return x + 1 >>> isinstance(f.get_concrete_function(1).graph, tf.Graph) True +\`\`\` \`ConcreteFunction\`s can be executed just like \`GenericFunction\`s, but their input is resticted to the types to which they're specialized. @@ -3637,7 +3226,7 @@ Caution: Passing python scalars or lists as arguments to \`tf.function\` will usually retrace. To avoid this, pass numeric arguments as Tensors whenever possible: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x): ... return tf.abs(x) >>> f1 = f.get_concrete_function(1) @@ -3649,6 +3238,7 @@ False >>> f1 is f2 True +\`\`\` Python numerical arguments should only be used when they take few distinct values, such as hyperparameters like the number of layers in a neural network. @@ -3658,7 +3248,7 @@ For Tensor arguments, \`GenericFunction\`creates a new \`ConcreteFunction\` for every unique set of input shapes and datatypes. The example below creates two separate \`ConcreteFunction\`s, each specialized to a different shape: ->>> @tf.function +\`\`\`>>> @tf.function ... def f(x): ... return x + 1 >>> vector = tf.constant([1.0, 1.0]) @@ -3666,6 +3256,7 @@ separate \`ConcreteFunction\`s, each specialized to a different shape: >>> f.get_concrete_function(vector) is f.get_concrete_function(matrix) False +\`\`\` An "input signature" can be optionally provided to \`tf.function\` to control this process. The input signature specifies the shape and type of each Tensor argument to the function using a \`tf.TensorSpec\` object. More general @@ -3673,7 +3264,7 @@ shapes can be used. This ensures only one \`ConcreteFunction\` is created, and restricts the \`GenericFunction\` to the specified shapes and types. It is an effective way to limit retracing when Tensors have dynamic shapes. ->>> @tf.function( +\`\`\`>>> @tf.function( ... input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)]) ... def f(x): ... return x + 1 @@ -3682,12 +3273,13 @@ an effective way to limit retracing when Tensors have dynamic shapes. >>> f.get_concrete_function(vector) is f.get_concrete_function(matrix) True +\`\`\` ## Variables may only be created once \`tf.function\` only allows creating new \`tf.Variable\` objects when it is called for the first time: ->>> class MyModule(tf.Module): +\`\`\`>>> class MyModule(tf.Module): ... def __init__(self): ... self.v = None ... @@ -3697,6 +3289,7 @@ for the first time: ... self.v = tf.Variable(tf.ones_like(x)) ... return self.v * x +\`\`\` In general, it is recommended to create \`tf.Variable\`s outside of \`tf.function\`. In simple cases, persisting state across \`tf.function\` boundaries may be @@ -3705,7 +3298,7 @@ implemented using a pure functional style in which state is represented by Contrast the two styles below: ->>> state = tf.Variable(1) +\`\`\`>>> state = tf.Variable(1) >>> @tf.function ... def f(x): ... state.assign_add(x) @@ -3713,7 +3306,8 @@ Contrast the two styles below: >>> state ->>> state = tf.constant(1) +\`\`\` +\`\`\`>>> state = tf.constant(1) >>> @tf.function ... def f(state, x): ... state += x @@ -3722,6 +3316,7 @@ Contrast the two styles below: >>> state +\`\`\` ## Python operations execute only once per trace \`func\` may contain TensorFlow operations mixed with pure Python operations. @@ -3730,7 +3325,7 @@ run. The Python operations run only once, at trace time. If TensorFlow operations depend on results from Pyhton operations, those results will be frozen into the graph. ->>> @tf.function +\`\`\`>>> @tf.function ... def f(a, b): ... print('this runs at trace time; a is', a, 'and b is', b) ... return b @@ -3738,23 +3333,27 @@ frozen into the graph. this runs at trace time; a is 1 and b is Tensor("...", shape=(), dtype=int32) ->>> f(1, tf.constant(2)) +\`\`\` +\`\`\`>>> f(1, tf.constant(2)) ->>> f(2, tf.constant(1)) +\`\`\` +\`\`\`>>> f(2, tf.constant(1)) this runs at trace time; a is 2 and b is Tensor("...", shape=(), dtype=int32) ->>> f(2, tf.constant(2)) +\`\`\` +\`\`\`>>> f(2, tf.constant(2)) +\`\`\` ## Using type annotations to improve performance 'experimental_follow_type_hints\` can be used along with type annotations to reduce retracing by automatically casting any Python values to \`tf.Tensor\` (something that is not done by default, unless you use input signatures). ->>> @tf.function(experimental_follow_type_hints=True) +\`\`\`>>> @tf.function(experimental_follow_type_hints=True) ... def f_with_hints(x: tf.Tensor): ... print('Tracing') ... return x @@ -3774,6 +3373,7 @@ Tracing >>> f_with_hints(2) +\`\`\` Args: func: the function to be compiled. If \`func\` is None, \`tf.function\` returns a decorator that can be invoked with a single argument - \`func\`. In other @@ -3861,19 +3461,21 @@ must be an integer tensor of any dimension (often 1-D). In the simplest case it's identical to scalar indexing: ->>> params = tf.constant(['p0', 'p1', 'p2', 'p3', 'p4', 'p5']) +\`\`\`>>> params = tf.constant(['p0', 'p1', 'p2', 'p3', 'p4', 'p5']) >>> params[3].numpy() b'p3' >>> tf.gather(params, 3).numpy() b'p3' +\`\`\` The most common case is to pass a single axis tensor of indices (this can't be expressed as a python slice because the indices are not sequential): ->>> indices = [2, 0, 2, 5] +\`\`\`>>> indices = [2, 0, 2, 5] >>> tf.gather(params, indices).numpy() array([b'p2', b'p0', b'p2', b'p5'], dtype=object) +\`\`\`
@@ -3882,15 +3484,16 @@ alt> The indices can have any shape. When the \`params\` has 1 axis, the output shape is equal to the input shape: ->>> tf.gather(params, [[2, 0], [2, 5]]).numpy() +\`\`\`>>> tf.gather(params, [[2, 0], [2, 5]]).numpy() array([[b'p2', b'p0'], [b'p2', b'p5']], dtype=object) +\`\`\` The \`params\` may also have any shape. \`gather\` can select slices across any axis depending on the \`axis\` argument (which defaults to 0). Below it is used to gather first rows, then columns from a matrix: ->>> params = tf.constant([[0, 1.0, 2.0], +\`\`\`>>> params = tf.constant([[0, 1.0, 2.0], ... [10.0, 11.0, 12.0], ... [20.0, 21.0, 22.0], ... [30.0, 31.0, 32.0]]) @@ -3903,10 +3506,11 @@ array([[ 2., 1.], [22., 21.], [32., 31.]], dtype=float32) +\`\`\` More generally: The output shape has the same shape as the input, with the indexed-axis replaced by the shape of the indices. ->>> def result_shape(p_shape, i_shape, axis=0): +\`\`\`>>> def result_shape(p_shape, i_shape, axis=0): ... return p_shape[:axis] + i_shape + p_shape[axis+1:] >>> >>> result_shape([1, 2, 3], [], axis=1) @@ -3916,9 +3520,10 @@ indexed-axis replaced by the shape of the indices. >>> result_shape([1, 2, 3], [7, 5], axis=1) [1, 7, 5, 3] +\`\`\` Here are some examples: ->>> params.shape.as_list() +\`\`\`>>> params.shape.as_list() [4, 3] >>> indices = tf.constant([[0, 2]]) >>> tf.gather(params, indices=indices, axis=0).shape.as_list() @@ -3926,16 +3531,18 @@ Here are some examples: >>> tf.gather(params, indices=indices, axis=1).shape.as_list() [4, 1, 2] ->>> params = tf.random.normal(shape=(5, 6, 7, 8)) +\`\`\` +\`\`\`>>> params = tf.random.normal(shape=(5, 6, 7, 8)) >>> indices = tf.random.uniform(shape=(10, 11), maxval=7, dtype=tf.int32) >>> result = tf.gather(params, indices, axis=2) >>> result.shape.as_list() [5, 6, 10, 11, 8] +\`\`\` This is because each index takes a slice from \`params\`, and places it at the corresponding location in the output. For the above example ->>> # For any location in indices +\`\`\`>>> # For any location in indices >>> a, b = 0, 1 >>> tf.reduce_all( ... # the corresponding slice of the result @@ -3945,6 +3552,7 @@ places it at the corresponding location in the output. For the above example ... ).numpy() True +\`\`\` ### Batching: The \`batch_dims\` argument lets you gather different items from each element @@ -3953,7 +3561,7 @@ of a batch. Using \`batch_dims=1\` is equivalent to having an outer loop over the first axis of \`params\` and \`indices\`: ->>> params = tf.constant([ +\`\`\`>>> params = tf.constant([ ... [0, 0, 1, 0, 2], ... [3, 0, 0, 0, 4], ... [0, 5, 0, 6, 0]]) @@ -3962,14 +3570,16 @@ axis of \`params\` and \`indices\`: ... [0, 4], ... [1, 3]]) ->>> tf.gather(params, indices, axis=1, batch_dims=1).numpy() +\`\`\` +\`\`\`>>> tf.gather(params, indices, axis=1, batch_dims=1).numpy() array([[1, 2], [3, 4], [5, 6]], dtype=int32) +\`\`\` This is equivalent to: ->>> def manually_batched_gather(params, indices, axis): +\`\`\`>>> def manually_batched_gather(params, indices, axis): ... batch_dims=1 ... result = [] ... for p,i in zip(params, indices): @@ -3981,10 +3591,11 @@ array([[1, 2], [3, 4], [5, 6]], dtype=int32) +\`\`\` Higher values of \`batch_dims\` are equivalent to multiple nested loops over the outer axes of \`params\` and \`indices\`. So the overall shape function is ->>> def batched_result_shape(p_shape, i_shape, axis=0, batch_dims=0): +\`\`\`>>> def batched_result_shape(p_shape, i_shape, axis=0, batch_dims=0): ... return p_shape[:axis] + i_shape[batch_dims:] + p_shape[axis+1:] >>> >>> batched_result_shape( @@ -3994,9 +3605,11 @@ the outer axes of \`params\` and \`indices\`. So the overall shape function is ... batch_dims=1) [3, 2] ->>> tf.gather(params, indices, axis=1, batch_dims=1).shape.as_list() +\`\`\` +\`\`\`>>> tf.gather(params, indices, axis=1, batch_dims=1).shape.as_list() [3, 2] +\`\`\` This comes up naturally if you need to use the indices of an operation like \`tf.argsort\`, or \`tf.math.top_k\` where the last dimension of the indices indexes into the last dimension of input, at the corresponding location. @@ -4052,13 +3665,14 @@ Returns: In the simplest case the vectors in \`indices\` index the full rank of \`params\`: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices=[[0, 0], ... [1, 1]], ... params = [['a', 'b'], ... ['c', 'd']]).numpy() array([b'a', b'd'], dtype=object) +\`\`\` In this case the result has 1-axis fewer than \`indices\`, and each index vector is replaced by the scalar indexed from \`params\`. @@ -4079,7 +3693,7 @@ Returns: in the result contains a slice of params. This example collects rows from a matrix: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[1], ... [0]], ... params = [['a', 'b', 'c'], @@ -4087,6 +3701,7 @@ Returns: array([[b'd', b'e', b'f'], [b'a', b'b', b'c']], dtype=object) +\`\`\` Here \`indices\` contains \`[2]\` index vectors, each with a length of \`1\`. The index vectors each refer to rows of the \`params\` matrix. Each row has a shape of \`[3]\` so the output shape is \`[2, 3]\`. @@ -4114,7 +3729,7 @@ Returns: set the leading elements of the index vectors to be their location in the batch: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[0, 1], ... [1, 0], ... [2, 4], @@ -4123,10 +3738,11 @@ Returns: ... params=tf.zeros([5, 7, 3])).shape.as_list() [5, 3] +\`\`\` The \`batch_dims\` argument lets you omit those leading location dimensions from the index: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... batch_dims=1, ... indices = [[1], ... [0], @@ -4136,11 +3752,12 @@ Returns: ... params=tf.zeros([5, 7, 3])).shape.as_list() [5, 3] +\`\`\` This is equivalent to caling a separate \`gather_nd\` for each location in the batch dimensions. - >>> params=tf.zeros([5, 7, 3]) + \`\`\`>>> params=tf.zeros([5, 7, 3]) >>> indices=tf.zeros([5, 1]) >>> batch_dims = 1 >>> @@ -4154,59 +3771,65 @@ Returns: >>> output_shape.as_list() [5, 3] +\`\`\` ### More examples Indexing into a 3-tensor: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[1]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() array([[[b'a1', b'b1'], [b'c1', b'd1']]], dtype=object) +\`\`\` - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[0, 1], [1, 0]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() array([[b'c0', b'd0'], [b'a1', b'b1']], dtype=object) +\`\`\` - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[0, 0, 1], [1, 0, 1]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() array([b'b0', b'b1'], dtype=object) +\`\`\` The examples below are for the case when only indices have leading extra dimensions. If both 'params' and 'indices' have leading batch dimensions, use the 'batch_dims' parameter to run gather_nd in batch mode. Batched indexing into a matrix: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[[0, 0]], [[0, 1]]], ... params = [['a', 'b'], ['c', 'd']]).numpy() array([[b'a'], [b'b']], dtype=object) +\`\`\` Batched slice indexing into a matrix: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[[1]], [[0]]], ... params = [['a', 'b'], ['c', 'd']]).numpy() array([[[b'c', b'd']], [[b'a', b'b']]], dtype=object) +\`\`\` Batched indexing into a 3-tensor: - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[[1]], [[0]]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() @@ -4215,8 +3838,9 @@ Returns: [[[b'a0', b'b0'], [b'c0', b'd0']]]], dtype=object) +\`\`\` - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() @@ -4225,17 +3849,19 @@ Returns: [[b'a0', b'b0'], [b'c1', b'd1']]], dtype=object) - >>> tf.gather_nd( +\`\`\` + \`\`\`>>> tf.gather_nd( ... indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]], ... params = [[['a0', 'b0'], ['c0', 'd0']], ... [['a1', 'b1'], ['c1', 'd1']]]).numpy() array([[b'b0', b'b1'], [b'd0', b'c1']], dtype=object) +\`\`\` Examples with batched 'params' and 'indices': - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... batch_dims = 1, ... indices = [[1], ... [0]], @@ -4246,8 +3872,9 @@ Returns: array([[b'c0', b'd0'], [b'a1', b'b1']], dtype=object) +\`\`\` - >>> tf.gather_nd( + \`\`\`>>> tf.gather_nd( ... batch_dims = 1, ... indices = [[[1]], [[0]]], ... params = [[['a0', 'b0'], ['c0', 'd0']], @@ -4255,7 +3882,8 @@ Returns: array([[[b'c0', b'd0']], [[b'a1', b'b1']]], dtype=object) - >>> tf.gather_nd( +\`\`\` + \`\`\`>>> tf.gather_nd( ... batch_dims = 1, ... indices = [[[1, 0]], [[0, 1]]], ... params = [[['a0', 'b0'], ['c0', 'd0']], @@ -4263,6 +3891,7 @@ Returns: array([[b'c0'], [b'b1']], dtype=object) +\`\`\` See also \`tf.gather\`. @@ -4306,18 +3935,20 @@ Returns: Example usage: - >>> a = tf.constant(10) + \`\`\`>>> a = tf.constant(10) >>> tf.get_static_value(a) 10 >>> b = tf.constant(20) >>> tf.get_static_value(tf.add(a, b)) 30 - >>> # \`tf.Variable\` is not supported. +\`\`\` + \`\`\`>>> # \`tf.Variable\` is not supported. >>> c = tf.Variable(30) >>> print(tf.get_static_value(c)) None +\`\`\` Using \`partial\` option is most relevant when calling \`get_static_value\` inside a \`tf.function\`. Setting it to \`True\` will return the results but for the values that cannot be evaluated will be \`None\`. For example: @@ -4393,7 +4024,7 @@ Returns: Returns: A function \`h(x)\` which returns the same values as \`f(x)\` and whose gradients are the same as those of an identity function. - `},{name:"gradients",path:"./tf/gradients.md",desc:"Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.",type:"Functions",docs:"Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.\n\n `tf.gradients` is only valid in a graph context. In particular,\n it is valid in the context of a `tf.function` wrapper, where code\n is executing as a graph.\n\n `ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys`\n is a list of `Tensor`, holding the gradients received by the\n `ys`. The list must be the same length as `ys`.\n\n `gradients()` adds ops to the graph to output the derivatives of `ys` with\n respect to `xs`. It returns a list of `Tensor` of length `len(xs)` where\n each tensor is the `sum(dy/dx)` for y in `ys` and for x in `xs`.\n\n `grad_ys` is a list of tensors of the same length as `ys` that holds\n the initial gradients for each y in `ys`. When `grad_ys` is None,\n we fill in a tensor of '1's of the shape of y for each y in `ys`. A\n user can provide their own initial `grad_ys` to compute the\n derivatives using a different initial gradient for each y (e.g., if\n one wanted to weight the gradient differently for each value in\n each y).\n\n `stop_gradients` is a `Tensor` or a list of tensors to be considered constant\n with respect to all `xs`. These tensors will not be backpropagated through,\n as though they had been explicitly disconnected using `stop_gradient`. Among\n other things, this allows computation of partial derivatives as opposed to\n total derivatives. For example:\n\n >>> @tf.function\n ... def example():\n ... a = tf.constant(0.)\n ... b = 2 * a\n ... return tf.gradients(a + b, [a, b], stop_gradients=[a, b])\n >>> example()\n [,\n ]\n\n Here the partial derivatives `g` evaluate to `[1.0, 1.0]`, compared to the\n total derivatives `tf.gradients(a + b, [a, b])`, which take into account the\n influence of `a` on `b` and evaluate to `[3.0, 1.0]`. Note that the above is\n equivalent to:\n\n >>> @tf.function\n ... def example():\n ... a = tf.stop_gradient(tf.constant(0.))\n ... b = tf.stop_gradient(2 * a)\n ... return tf.gradients(a + b, [a, b])\n >>> example()\n [,\n ]\n\n `stop_gradients` provides a way of stopping gradient after the graph has\n already been constructed, as compared to `tf.stop_gradient` which is used\n during graph construction. When the two approaches are combined,\n backpropagation stops at both `tf.stop_gradient` nodes and nodes in\n `stop_gradients`, whichever is encountered first.\n\n All integer tensors are considered constant with respect to all `xs`, as if\n they were included in `stop_gradients`.\n\n `unconnected_gradients` determines the value returned for each x in xs if it\n is unconnected in the graph to ys. By default this is None to safeguard\n against errors. Mathematically these gradients are zero which can be requested\n using the `'zero'` option. `tf.UnconnectedGradients` provides the\n following options and behaviors:\n\n >>> @tf.function\n ... def example(use_zero):\n ... a = tf.ones([1, 2])\n ... b = tf.ones([3, 1])\n ... if use_zero:\n ... return tf.gradients([b], [a], unconnected_gradients='zero')\n ... else:\n ... return tf.gradients([b], [a], unconnected_gradients='none')\n >>> example(False)\n [None]\n >>> example(True)\n []\n\n Let us take one practical example which comes during the back propogation\n phase. This function is used to evaluate the derivatives of the cost function\n with respect to Weights `Ws` and Biases `bs`. Below sample implementation\n provides the exaplantion of what it is actually used for :\n\n >>> @tf.function\n ... def example():\n ... Ws = tf.constant(0.)\n ... bs = 2 * Ws\n ... cost = Ws + bs # This is just an example. Please ignore the formulas.\n ... g = tf.gradients(cost, [Ws, bs])\n ... dCost_dW, dCost_db = g\n ... return dCost_dW, dCost_db\n >>> example()\n (,\n )\n\n Args:\n ys: A `Tensor` or list of tensors to be differentiated.\n xs: A `Tensor` or list of tensors to be used for differentiation.\n grad_ys: Optional. A `Tensor` or list of tensors the same size as\n `ys` and holding the gradients computed for each y in `ys`.\n name: Optional name to use for grouping all the gradient ops together.\n defaults to 'gradients'.\n gate_gradients: If True, add a tuple around the gradients returned\n for an operations. This avoids some race conditions.\n aggregation_method: Specifies the method used to combine gradient terms.\n Accepted values are constants defined in the class `AggregationMethod`.\n stop_gradients: Optional. A `Tensor` or list of tensors not to differentiate\n through.\n unconnected_gradients: Optional. Specifies the gradient value returned when\n the given input tensors are unconnected. Accepted values are constants\n defined in the class `tf.UnconnectedGradients` and the default value is\n `none`.\n\n Returns:\n A list of `Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`\n for y in `ys` and for x in `xs`.\n\n Raises:\n LookupError: if one of the operations between `x` and `y` does not\n have a registered gradient function.\n ValueError: if the arguments are invalid.\n RuntimeError: if called in Eager mode.\n\n "},{name:"greater",path:"./tf/math/greater.md",desc:"Returns the truth value of (x > y",type:"Functions",docs:"Returns the truth value of (x > y) element-wise.\n\n *NOTE*: `math.greater` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 2, 5])\n tf.math.greater(x, y) ==> [False, True, True]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.greater(x, y) ==> [False, False, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"greater_equal",path:"./tf/math/greater_equal.md",desc:"Returns the truth value of (x >= y",type:"Functions",docs:"Returns the truth value of (x >= y) element-wise.\n\n *NOTE*: `math.greater_equal` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6, 7])\n y = tf.constant([5, 2, 5, 10])\n tf.math.greater_equal(x, y) ==> [True, True, True, False]\n\n x = tf.constant([5, 4, 6, 7])\n y = tf.constant([5])\n tf.math.greater_equal(x, y) ==> [True, False, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"group",path:"./tf/group.md",desc:"Create an op that groups multiple operations.",type:"Functions",docs:`Create an op that groups multiple operations. + `},{name:"gradients",path:"./tf/gradients.md",desc:"Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.",type:"Functions",docs:"Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.\n\n `tf.gradients` is only valid in a graph context. In particular,\n it is valid in the context of a `tf.function` wrapper, where code\n is executing as a graph.\n\n `ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys`\n is a list of `Tensor`, holding the gradients received by the\n `ys`. The list must be the same length as `ys`.\n\n `gradients()` adds ops to the graph to output the derivatives of `ys` with\n respect to `xs`. It returns a list of `Tensor` of length `len(xs)` where\n each tensor is the `sum(dy/dx)` for y in `ys` and for x in `xs`.\n\n `grad_ys` is a list of tensors of the same length as `ys` that holds\n the initial gradients for each y in `ys`. When `grad_ys` is None,\n we fill in a tensor of '1's of the shape of y for each y in `ys`. A\n user can provide their own initial `grad_ys` to compute the\n derivatives using a different initial gradient for each y (e.g., if\n one wanted to weight the gradient differently for each value in\n each y).\n\n `stop_gradients` is a `Tensor` or a list of tensors to be considered constant\n with respect to all `xs`. These tensors will not be backpropagated through,\n as though they had been explicitly disconnected using `stop_gradient`. Among\n other things, this allows computation of partial derivatives as opposed to\n total derivatives. For example:\n\n ```>>> @tf.function\n ... def example():\n ... a = tf.constant(0.)\n ... b = 2 * a\n ... return tf.gradients(a + b, [a, b], stop_gradients=[a, b])\n >>> example()\n [,\n ]\n\n```\n Here the partial derivatives `g` evaluate to `[1.0, 1.0]`, compared to the\n total derivatives `tf.gradients(a + b, [a, b])`, which take into account the\n influence of `a` on `b` and evaluate to `[3.0, 1.0]`. Note that the above is\n equivalent to:\n\n ```>>> @tf.function\n ... def example():\n ... a = tf.stop_gradient(tf.constant(0.))\n ... b = tf.stop_gradient(2 * a)\n ... return tf.gradients(a + b, [a, b])\n >>> example()\n [,\n ]\n\n```\n `stop_gradients` provides a way of stopping gradient after the graph has\n already been constructed, as compared to `tf.stop_gradient` which is used\n during graph construction. When the two approaches are combined,\n backpropagation stops at both `tf.stop_gradient` nodes and nodes in\n `stop_gradients`, whichever is encountered first.\n\n All integer tensors are considered constant with respect to all `xs`, as if\n they were included in `stop_gradients`.\n\n `unconnected_gradients` determines the value returned for each x in xs if it\n is unconnected in the graph to ys. By default this is None to safeguard\n against errors. Mathematically these gradients are zero which can be requested\n using the `'zero'` option. `tf.UnconnectedGradients` provides the\n following options and behaviors:\n\n ```>>> @tf.function\n ... def example(use_zero):\n ... a = tf.ones([1, 2])\n ... b = tf.ones([3, 1])\n ... if use_zero:\n ... return tf.gradients([b], [a], unconnected_gradients='zero')\n ... else:\n ... return tf.gradients([b], [a], unconnected_gradients='none')\n >>> example(False)\n [None]\n >>> example(True)\n []\n\n```\n Let us take one practical example which comes during the back propogation\n phase. This function is used to evaluate the derivatives of the cost function\n with respect to Weights `Ws` and Biases `bs`. Below sample implementation\n provides the exaplantion of what it is actually used for :\n\n ```>>> @tf.function\n ... def example():\n ... Ws = tf.constant(0.)\n ... bs = 2 * Ws\n ... cost = Ws + bs # This is just an example. Please ignore the formulas.\n ... g = tf.gradients(cost, [Ws, bs])\n ... dCost_dW, dCost_db = g\n ... return dCost_dW, dCost_db\n >>> example()\n (,\n )\n\n```\n Args:\n ys: A `Tensor` or list of tensors to be differentiated.\n xs: A `Tensor` or list of tensors to be used for differentiation.\n grad_ys: Optional. A `Tensor` or list of tensors the same size as\n `ys` and holding the gradients computed for each y in `ys`.\n name: Optional name to use for grouping all the gradient ops together.\n defaults to 'gradients'.\n gate_gradients: If True, add a tuple around the gradients returned\n for an operations. This avoids some race conditions.\n aggregation_method: Specifies the method used to combine gradient terms.\n Accepted values are constants defined in the class `AggregationMethod`.\n stop_gradients: Optional. A `Tensor` or list of tensors not to differentiate\n through.\n unconnected_gradients: Optional. Specifies the gradient value returned when\n the given input tensors are unconnected. Accepted values are constants\n defined in the class `tf.UnconnectedGradients` and the default value is\n `none`.\n\n Returns:\n A list of `Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`\n for y in `ys` and for x in `xs`.\n\n Raises:\n LookupError: if one of the operations between `x` and `y` does not\n have a registered gradient function.\n ValueError: if the arguments are invalid.\n RuntimeError: if called in Eager mode.\n\n "},{name:"greater",path:"./tf/math/greater.md",desc:"Returns the truth value of (x > y",type:"Functions",docs:"Returns the truth value of (x > y) element-wise.\n\n *NOTE*: `math.greater` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 2, 5])\n tf.math.greater(x, y) ==> [False, True, True]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.greater(x, y) ==> [False, False, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"greater_equal",path:"./tf/math/greater_equal.md",desc:"Returns the truth value of (x >= y",type:"Functions",docs:"Returns the truth value of (x >= y) element-wise.\n\n *NOTE*: `math.greater_equal` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6, 7])\n y = tf.constant([5, 2, 5, 10])\n tf.math.greater_equal(x, y) ==> [True, True, True, False]\n\n x = tf.constant([5, 4, 6, 7])\n y = tf.constant([5])\n tf.math.greater_equal(x, y) ==> [True, False, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"group",path:"./tf/group.md",desc:"Create an op that groups multiple operations.",type:"Functions",docs:`Create an op that groups multiple operations. When this op finishes, all ops in \`inputs\` have finished. This op has no output. @@ -4518,18 +4149,19 @@ Returns: For example: - >>> a = tf.constant([0.78]) + \`\`\`>>> a = tf.constant([0.78]) >>> a_identity = tf.identity(a) >>> a.numpy() array([0.78], dtype=float32) >>> a_identity.numpy() array([0.78], dtype=float32) +\`\`\` Calling \`tf.identity\` on a variable will make a Tensor that represents the value of that variable at the time it is called. This is equivalent to calling \`.read_value()\`. - >>> a = tf.Variable(5) + \`\`\`>>> a = tf.Variable(5) >>> a_identity = tf.identity(a) >>> a.assign_add(1) @@ -4538,6 +4170,7 @@ Returns: >>> a_identity.numpy() 5 +\`\`\` Args: input: A \`Tensor\`, a \`Variable\`, a \`CompositeTensor\` or anything that can be converted to a tensor using \`tf.convert_to_tensor\`. @@ -4629,7 +4262,7 @@ Returns: ... print(tf.inside_function()) >>> f() True - `},{name:"is_tensor",path:"./tf/is_tensor.md",desc:"Checks whether `x` is a TF-native type that can be passed to many TF ops.",type:"Functions",docs:"Checks whether `x` is a TF-native type that can be passed to many TF ops.\n\n Use `is_tensor` to differentiate types that can ingested by TensorFlow ops\n without any conversion (e.g., `tf.Tensor`, `tf.SparseTensor`, and\n `tf.RaggedTensor`) from types that need to be converted into tensors before\n they are ingested (e.g., numpy `ndarray` and Python scalars).\n\n For example, in the following code block:\n\n ```python\n if not tf.is_tensor(t):\n t = tf.convert_to_tensor(t)\n return t.shape, t.dtype\n ```\n\n we check to make sure that `t` is a tensor (and convert it if not) before\n accessing its `shape` and `dtype`. (But note that not all TensorFlow native\n types have shapes or dtypes; `tf.data.Dataset` is an example of a TensorFlow\n native type that has neither shape nor dtype.)\n\n Args:\n x: A python object to check.\n\n Returns:\n `True` if `x` is a TensorFlow-native type.\n "},{name:"less",path:"./tf/math/less.md",desc:"Returns the truth value of (x < y",type:"Functions",docs:"Returns the truth value of (x < y) element-wise.\n\n *NOTE*: `math.less` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.less(x, y) ==> [False, True, False]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 6, 7])\n tf.math.less(x, y) ==> [False, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"less_equal",path:"./tf/math/less_equal.md",desc:"Returns the truth value of (x <= y",type:"Functions",docs:"Returns the truth value of (x <= y) element-wise.\n\n *NOTE*: `math.less_equal` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.less_equal(x, y) ==> [True, True, False]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 6, 6])\n tf.math.less_equal(x, y) ==> [True, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"linspace",path:"./tf/linspace.md",desc:"Generates evenly-spaced values in an interval along a given axis.",type:"Functions",docs:'Generates evenly-spaced values in an interval along a given axis.\n\n A sequence of `num` evenly-spaced values are generated beginning at `start`\n along a given `axis`.\n If `num > 1`, the values in the sequence increase by\n `(stop - start) / (num - 1)`, so that the last one is exactly `stop`.\n If `num <= 0`, `ValueError` is raised.\n\n Matches\n [np.linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)\'s\n behaviour\n except when `num == 0`.\n\n For example:\n\n ```\n tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]\n ```\n\n `Start` and `stop` can be tensors of arbitrary size:\n\n >>> tf.linspace([0., 5.], [10., 40.], 5, axis=0)\n \n\n `Axis` is where the values will be generated (the dimension in the\n returned tensor which corresponds to the axis will be equal to `num`)\n\n >>> tf.linspace([0., 5.], [10., 40.], 5, axis=-1)\n \n\n\n\n Args:\n start: A `Tensor`. Must be one of the following types: `bfloat16`,\n `float32`, `float64`. N-D tensor. First entry in the range.\n stop: A `Tensor`. Must have the same type and shape as `start`. N-D tensor.\n Last entry in the range.\n num: A `Tensor`. Must be one of the following types: `int32`, `int64`. 0-D\n tensor. Number of values to generate.\n name: A name for the operation (optional).\n axis: Axis along which the operation is performed (used only when N-D\n tensors are provided).\n\n Returns:\n A `Tensor`. Has the same type as `start`.\n '},{name:"load_library",path:"./tf/load_library.md",desc:"Loads a TensorFlow plugin.",type:"Functions",docs:`Loads a TensorFlow plugin. + `},{name:"is_tensor",path:"./tf/is_tensor.md",desc:"Checks whether `x` is a TF-native type that can be passed to many TF ops.",type:"Functions",docs:"Checks whether `x` is a TF-native type that can be passed to many TF ops.\n\n Use `is_tensor` to differentiate types that can ingested by TensorFlow ops\n without any conversion (e.g., `tf.Tensor`, `tf.SparseTensor`, and\n `tf.RaggedTensor`) from types that need to be converted into tensors before\n they are ingested (e.g., numpy `ndarray` and Python scalars).\n\n For example, in the following code block:\n\n ```python\n if not tf.is_tensor(t):\n t = tf.convert_to_tensor(t)\n return t.shape, t.dtype\n ```\n\n we check to make sure that `t` is a tensor (and convert it if not) before\n accessing its `shape` and `dtype`. (But note that not all TensorFlow native\n types have shapes or dtypes; `tf.data.Dataset` is an example of a TensorFlow\n native type that has neither shape nor dtype.)\n\n Args:\n x: A python object to check.\n\n Returns:\n `True` if `x` is a TensorFlow-native type.\n "},{name:"less",path:"./tf/math/less.md",desc:"Returns the truth value of (x < y",type:"Functions",docs:"Returns the truth value of (x < y) element-wise.\n\n *NOTE*: `math.less` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.less(x, y) ==> [False, True, False]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 6, 7])\n tf.math.less(x, y) ==> [False, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"less_equal",path:"./tf/math/less_equal.md",desc:"Returns the truth value of (x <= y",type:"Functions",docs:"Returns the truth value of (x <= y) element-wise.\n\n *NOTE*: `math.less_equal` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Example:\n\n ```python\n x = tf.constant([5, 4, 6])\n y = tf.constant([5])\n tf.math.less_equal(x, y) ==> [True, True, False]\n\n x = tf.constant([5, 4, 6])\n y = tf.constant([5, 6, 6])\n tf.math.less_equal(x, y) ==> [True, True, True]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"linspace",path:"./tf/linspace.md",desc:"Generates evenly-spaced values in an interval along a given axis.",type:"Functions",docs:'Generates evenly-spaced values in an interval along a given axis.\n\n A sequence of `num` evenly-spaced values are generated beginning at `start`\n along a given `axis`.\n If `num > 1`, the values in the sequence increase by\n `(stop - start) / (num - 1)`, so that the last one is exactly `stop`.\n If `num <= 0`, `ValueError` is raised.\n\n Matches\n [np.linspace](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)\'s\n behaviour\n except when `num == 0`.\n\n For example:\n\n ```\n tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]\n ```\n\n `Start` and `stop` can be tensors of arbitrary size:\n\n ```>>> tf.linspace([0., 5.], [10., 40.], 5, axis=0)\n \n\n```\n `Axis` is where the values will be generated (the dimension in the\n returned tensor which corresponds to the axis will be equal to `num`)\n\n ```>>> tf.linspace([0., 5.], [10., 40.], 5, axis=-1)\n \n\n```\n\n\n Args:\n start: A `Tensor`. Must be one of the following types: `bfloat16`,\n `float32`, `float64`. N-D tensor. First entry in the range.\n stop: A `Tensor`. Must have the same type and shape as `start`. N-D tensor.\n Last entry in the range.\n num: A `Tensor`. Must be one of the following types: `int32`, `int64`. 0-D\n tensor. Number of values to generate.\n name: A name for the operation (optional).\n axis: Axis along which the operation is performed (used only when N-D\n tensors are provided).\n\n Returns:\n A `Tensor`. Has the same type as `start`.\n '},{name:"load_library",path:"./tf/load_library.md",desc:"Loads a TensorFlow plugin.",type:"Functions",docs:`Loads a TensorFlow plugin. "library_location" can be a path to a specific shared object, or a folder. If it is a folder, all shared objects that are named "libtfkernel*" will be @@ -4666,135 +4299,7 @@ Returns: Raises: RuntimeError: when unable to load the library or get the python wrappers. - `},{name:"logical_and",path:"./tf/math/logical_and.md",desc:"Returns the truth value of x AND y element-wise.",type:"Functions",docs:`Returns the truth value of x AND y element-wise. - - Logical AND function. - - Requires that \`x\` and \`y\` have the same shape or have - [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) - shapes. For example, \`x\` and \`y\` can be: - - - Two single elements of type \`bool\`. - - One \`tf.Tensor\` of type \`bool\` and one single \`bool\`, where the result will - be calculated by applying logical AND with the single element to each - element in the larger Tensor. - - Two \`tf.Tensor\` objects of type \`bool\` of the same shape. In this case, - the result will be the element-wise logical AND of the two input tensors. - - You can also use the \`&\` operator instead. - - Usage: - - >>> a = tf.constant([True]) - >>> b = tf.constant([False]) - >>> tf.math.logical_and(a, b) - - >>> a & b - - - >>> c = tf.constant([True]) - >>> x = tf.constant([False, True, True, False]) - >>> tf.math.logical_and(c, x) - - >>> c & x - - - >>> y = tf.constant([False, False, True, True]) - >>> z = tf.constant([False, True, False, True]) - >>> tf.math.logical_and(y, z) - - >>> y & z - - - This op also supports broadcasting - - >>> tf.logical_and([[True, False]], [[True], [False]]) - - - The reduction version of this elementwise operation is \`tf.math.reduce_all\`. - - Args: - x: A \`tf.Tensor\` of type bool. - y: A \`tf.Tensor\` of type bool. - name: A name for the operation (optional). - - Returns: - A \`tf.Tensor\` of type bool with the shape that \`x\` and \`y\` broadcast to. - - Args: - x: A \`Tensor\` of type \`bool\`. - y: A \`Tensor\` of type \`bool\`. - name: A name for the operation (optional). - - Returns: - A \`Tensor\` of type \`bool\`. - `},{name:"logical_not",path:"./tf/math/logical_not.md",desc:"Returns the truth value of `NOT x` element-wise.",type:"Functions",docs:"Returns the truth value of `NOT x` element-wise.\n\n Example:\n\n >>> tf.math.logical_not(tf.constant([True, False]))\n \n\n Args:\n x: A `Tensor` of type `bool`. A `Tensor` of type `bool`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"logical_or",path:"./tf/math/logical_or.md",desc:"Returns the truth value of x OR y element-wise.",type:"Functions",docs:`Returns the truth value of x OR y element-wise. - - Logical OR function. - - Requires that \`x\` and \`y\` have the same shape or have - [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) - shapes. For example, \`x\` and \`y\` can be: - - - Two single elements of type \`bool\`. - - One \`tf.Tensor\` of type \`bool\` and one single \`bool\`, where the result will - be calculated by applying logical OR with the single element to each - element in the larger Tensor. - - Two \`tf.Tensor\` objects of type \`bool\` of the same shape. In this case, - the result will be the element-wise logical OR of the two input tensors. - - You can also use the \`|\` operator instead. - - Usage: - - >>> a = tf.constant([True]) - >>> b = tf.constant([False]) - >>> tf.math.logical_or(a, b) - - >>> a | b - - - >>> c = tf.constant([False]) - >>> x = tf.constant([False, True, True, False]) - >>> tf.math.logical_or(c, x) - - >>> c | x - - - >>> y = tf.constant([False, False, True, True]) - >>> z = tf.constant([False, True, False, True]) - >>> tf.math.logical_or(y, z) - - >>> y | z - - - This op also supports broadcasting - - >>> tf.logical_or([[True, False]], [[True], [False]]) - - - The reduction version of this elementwise operation is \`tf.math.reduce_any\`. - - Args: - x: A \`tf.Tensor\` of type bool. - y: A \`tf.Tensor\` of type bool. - name: A name for the operation (optional). - - Returns: - A \`tf.Tensor\` of type bool with the shape that \`x\` and \`y\` broadcast to. - - Args: - x: A \`Tensor\` of type \`bool\`. - y: A \`Tensor\` of type \`bool\`. - name: A name for the operation (optional). - - Returns: - A \`Tensor\` of type \`bool\`. - `},{name:"make_ndarray",path:"./tf/make_ndarray.md",desc:"Create a numpy ndarray from a tensor.",type:"Functions",docs:`Create a numpy ndarray from a tensor. + `},{name:"logical_and",path:"./tf/math/logical_and.md",desc:"Returns the truth value of x AND y element-wise.",type:"Functions",docs:"Returns the truth value of x AND y element-wise.\n\n Logical AND function.\n\n Requires that `x` and `y` have the same shape or have\n [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n shapes. For example, `x` and `y` can be:\n\n - Two single elements of type `bool`.\n - One `tf.Tensor` of type `bool` and one single `bool`, where the result will\n be calculated by applying logical AND with the single element to each\n element in the larger Tensor.\n - Two `tf.Tensor` objects of type `bool` of the same shape. In this case,\n the result will be the element-wise logical AND of the two input tensors.\n\n You can also use the `&` operator instead.\n\n Usage:\n\n ```>>> a = tf.constant([True])\n >>> b = tf.constant([False])\n >>> tf.math.logical_and(a, b)\n \n >>> a & b\n \n\n```\n ```>>> c = tf.constant([True])\n >>> x = tf.constant([False, True, True, False])\n >>> tf.math.logical_and(c, x)\n \n >>> c & x\n \n\n```\n ```>>> y = tf.constant([False, False, True, True])\n >>> z = tf.constant([False, True, False, True])\n >>> tf.math.logical_and(y, z)\n \n >>> y & z\n \n\n```\n This op also supports broadcasting\n\n ```>>> tf.logical_and([[True, False]], [[True], [False]])\n \n\n```\n The reduction version of this elementwise operation is `tf.math.reduce_all`.\n\n Args:\n x: A `tf.Tensor` of type bool.\n y: A `tf.Tensor` of type bool.\n name: A name for the operation (optional).\n\n Returns:\n A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.\n\n Args:\n x: A `Tensor` of type `bool`.\n y: A `Tensor` of type `bool`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"logical_not",path:"./tf/math/logical_not.md",desc:"Returns the truth value of `NOT x` element-wise.",type:"Functions",docs:"Returns the truth value of `NOT x` element-wise.\n\n Example:\n\n ```>>> tf.math.logical_not(tf.constant([True, False]))\n \n\n```\n Args:\n x: A `Tensor` of type `bool`. A `Tensor` of type `bool`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"logical_or",path:"./tf/math/logical_or.md",desc:"Returns the truth value of x OR y element-wise.",type:"Functions",docs:"Returns the truth value of x OR y element-wise.\n\n Logical OR function.\n\n Requires that `x` and `y` have the same shape or have\n [broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n shapes. For example, `x` and `y` can be:\n\n - Two single elements of type `bool`.\n - One `tf.Tensor` of type `bool` and one single `bool`, where the result will\n be calculated by applying logical OR with the single element to each\n element in the larger Tensor.\n - Two `tf.Tensor` objects of type `bool` of the same shape. In this case,\n the result will be the element-wise logical OR of the two input tensors.\n\n You can also use the `|` operator instead.\n\n Usage:\n\n ```>>> a = tf.constant([True])\n >>> b = tf.constant([False])\n >>> tf.math.logical_or(a, b)\n \n >>> a | b\n \n\n```\n ```>>> c = tf.constant([False])\n >>> x = tf.constant([False, True, True, False])\n >>> tf.math.logical_or(c, x)\n \n >>> c | x\n \n\n```\n ```>>> y = tf.constant([False, False, True, True])\n >>> z = tf.constant([False, True, False, True])\n >>> tf.math.logical_or(y, z)\n \n >>> y | z\n \n\n```\n This op also supports broadcasting\n\n ```>>> tf.logical_or([[True, False]], [[True], [False]])\n \n\n```\n The reduction version of this elementwise operation is `tf.math.reduce_any`.\n\n Args:\n x: A `tf.Tensor` of type bool.\n y: A `tf.Tensor` of type bool.\n name: A name for the operation (optional).\n\n Returns:\n A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.\n\n Args:\n x: A `Tensor` of type `bool`.\n y: A `Tensor` of type `bool`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `bool`.\n "},{name:"make_ndarray",path:"./tf/make_ndarray.md",desc:"Create a numpy ndarray from a tensor.",type:"Functions",docs:`Create a numpy ndarray from a tensor. Create a numpy ndarray with the same shape and data as the tensor. @@ -4873,270 +4378,7 @@ Returns: ValueError: if arguments have inappropriate values or if verify_shape is True and shape of values is not equals to a shape from the argument. - `},{name:"map_fn",path:"./tf/map_fn.md",desc:"Transforms `elems` by applying `fn` to each element unstacked on axis 0. (deprecated arguments",type:"Functions",docs:`Transforms \`elems\` by applying \`fn\` to each element unstacked on axis 0. (deprecated arguments) - -Deprecated: SOME ARGUMENTS ARE DEPRECATED: \`(dtype)\`. They will be removed in a future version. -Instructions for updating: -Use fn_output_signature instead - -See also \`tf.scan\`. - -\`map_fn\` unstacks \`elems\` on axis 0 to obtain a sequence of elements; -calls \`fn\` to transform each element; and then stacks the transformed -values back together. - -#### Mapping functions with single-Tensor inputs and outputs - -If \`elems\` is a single tensor and \`fn\`'s signature is \`tf.Tensor->tf.Tensor\`, -then \`map_fn(fn, elems)\` is equivalent to -\`tf.stack([fn(elem) for elem in tf.unstack(elems)])\`. E.g.: - ->>> tf.map_fn(fn=lambda t: tf.range(t, t + 3), elems=tf.constant([3, 5, 2])) - - -\`map_fn(fn, elems).shape = [elems.shape[0]] + fn(elems[0]).shape\`. - -#### Mapping functions with multi-arity inputs and outputs - -\`map_fn\` also supports functions with multi-arity inputs and outputs: - -* If \`elems\` is a tuple (or nested structure) of tensors, then those tensors - must all have the same outer-dimension size (\`num_elems\`); and \`fn\` is - used to transform each tuple (or structure) of corresponding slices from - \`elems\`. E.g., if \`elems\` is a tuple \`(t1, t2, t3)\`, then \`fn\` is used to - transform each tuple of slices \`(t1[i], t2[i], t3[i])\` - (where \`0 <= i < num_elems\`). - -* If \`fn\` returns a tuple (or nested structure) of tensors, then the - result is formed by stacking corresponding elements from those structures. - -#### Specifying \`fn\`'s output signature - -If \`fn\`'s input and output signatures are different, then the output -signature must be specified using \`fn_output_signature\`. (The input and -output signatures are differ if their structures, dtypes, or tensor types do -not match). E.g.: - ->>> tf.map_fn(fn=tf.strings.length, # input & output have different dtypes -... elems=tf.constant(["hello", "moon"]), -... fn_output_signature=tf.int32) - ->>> tf.map_fn(fn=tf.strings.join, # input & output have different structures -... elems=[tf.constant(['The', 'A']), tf.constant(['Dog', 'Cat'])], -... fn_output_signature=tf.string) - - -\`fn_output_signature\` can be specified using any of the following: - -* A \`tf.DType\` or \`tf.TensorSpec\` (to describe a \`tf.Tensor\`) -* A \`tf.RaggedTensorSpec\` (to describe a \`tf.RaggedTensor\`) -* A \`tf.SparseTensorSpec\` (to describe a \`tf.sparse.SparseTensor\`) -* A (possibly nested) tuple, list, or dict containing the above types. - -#### RaggedTensors - -\`map_fn\` supports \`tf.RaggedTensor\` inputs and outputs. In particular: - -* If \`elems\` is a \`RaggedTensor\`, then \`fn\` will be called with each - row of that ragged tensor. - * If \`elems\` has only one ragged dimension, then the values passed to - \`fn\` will be \`tf.Tensor\`s. - * If \`elems\` has multiple ragged dimensions, then the values passed to - \`fn\` will be \`tf.RaggedTensor\`s with one fewer ragged dimension. - -* If the result of \`map_fn\` should be a \`RaggedTensor\`, then use a - \`tf.RaggedTensorSpec\` to specify \`fn_output_signature\`. - * If \`fn\` returns \`tf.Tensor\`s with varying sizes, then use a - \`tf.RaggedTensorSpec\` with \`ragged_rank=0\` to combine them into a - single ragged tensor (which will have ragged_rank=1). - * If \`fn\` returns \`tf.RaggedTensor\`s, then use a \`tf.RaggedTensorSpec\` - with the same \`ragged_rank\`. - ->>> # Example: RaggedTensor input ->>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]]) ->>> tf.map_fn(tf.reduce_sum, rt, fn_output_signature=tf.int32) - - ->>> # Example: RaggedTensor output ->>> elems = tf.constant([3, 5, 0, 2]) ->>> tf.map_fn(tf.range, elems, -... fn_output_signature=tf.RaggedTensorSpec(shape=[None], -... dtype=tf.int32)) - - -Note: \`map_fn\` should only be used if you need to map a function over the -*rows* of a \`RaggedTensor\`. If you wish to map a function over the -individual values, then you should use: - -* \`tf.ragged.map_flat_values(fn, rt)\` - (if fn is expressible as TensorFlow ops) -* \`rt.with_flat_values(map_fn(fn, rt.flat_values))\` - (otherwise) - -E.g.: - ->>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]]) ->>> tf.ragged.map_flat_values(lambda x: x + 2, rt) - - -#### SparseTensors - -\`map_fn\` supports \`tf.sparse.SparseTensor\` inputs and outputs. In particular: - -* If \`elems\` is a \`SparseTensor\`, then \`fn\` will be called with each row - of that sparse tensor. In particular, the value passed to \`fn\` will be a - \`tf.sparse.SparseTensor\` with one fewer dimension than \`elems\`. - -* If the result of \`map_fn\` should be a \`SparseTensor\`, then use a - \`tf.SparseTensorSpec\` to specify \`fn_output_signature\`. The individual - \`SparseTensor\`s returned by \`fn\` will be stacked into a single - \`SparseTensor\` with one more dimension. - ->>> # Example: SparseTensor input ->>> st = tf.sparse.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4]) ->>> tf.map_fn(tf.sparse.reduce_sum, st, fn_output_signature=tf.int32) - - ->>> # Example: SparseTensor output ->>> tf.sparse.to_dense( -... tf.map_fn(tf.sparse.eye, tf.constant([2, 3]), -... fn_output_signature=tf.SparseTensorSpec(None, tf.float32))) - - -Note: \`map_fn\` should only be used if you need to map a function over the -*rows* of a \`SparseTensor\`. If you wish to map a function over the nonzero -values, then you should use: - -* If the function is expressible as TensorFlow ops, use: - \`\`\`python - tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape) - \`\`\` -* Otherwise, use: - \`\`\`python - tf.sparse.SparseTensor(st.indices, tf.map_fn(fn, st.values), - st.dense_shape) - \`\`\` - -#### \`map_fn\` vs. vectorized operations - -\`map_fn\` will apply the operations used by \`fn\` to each element of \`elems\`, -resulting in \`O(elems.shape[0])\` total operations. This is somewhat -mitigated by the fact that \`map_fn\` can process elements in parallel. -However, a transform expressed using \`map_fn\` is still typically less -efficient than an equivalent transform expressed using vectorized operations. - -\`map_fn\` should typically only be used if one of the following is true: - -* It is difficult or expensive to express the desired transform with - vectorized operations. -* \`fn\` creates large intermediate values, so an equivalent vectorized - transform would take too much memory. -* Processing elements in parallel is more efficient than an equivalent - vectorized transform. -* Efficiency of the transform is not critical, and using \`map_fn\` is - more readable. - -E.g., the example given above that maps \`fn=lambda t: tf.range(t, t + 3)\` -across \`elems\` could be rewritten more efficiently using vectorized ops: - ->>> elems = tf.constant([3, 5, 2]) ->>> tf.range(3) + tf.expand_dims(elems, 1) - - -In some cases, \`tf.vectorized_map\` can be used to automatically convert a -function to a vectorized equivalent. - -#### Eager execution - -When executing eagerly, \`map_fn\` does not execute in parallel even if -\`parallel_iterations\` is set to a value > 1. You can still get the -performance benefits of running a function in parallel by using the -\`tf.function\` decorator: - ->>> fn=lambda t: tf.range(t, t + 3) ->>> @tf.function -... def func(elems): -... return tf.map_fn(fn, elems, parallel_iterations=3) ->>> func(tf.constant([3, 5, 2])) - - - -Note: if you use the \`tf.function\` decorator, any non-TensorFlow Python -code that you may have written in your function won't get executed. See -\`tf.function\` for more details. The recommendation would be to debug without -\`tf.function\` but switch to it to get performance benefits of running \`map_fn\` -in parallel. - -Args: - fn: The callable to be performed. It accepts one argument, which will have - the same (possibly nested) structure as \`elems\`. Its output must have the - same structure as \`fn_output_signature\` if one is provided; otherwise it - must have the same structure as \`elems\`. - elems: A tensor or (possibly nested) sequence of tensors, each of which will - be unstacked along their first dimension. \`fn\` will be applied to the - nested sequence of the resulting slices. \`elems\` may include ragged and - sparse tensors. \`elems\` must consist of at least one tensor. - dtype: Deprecated: Equivalent to \`fn_output_signature\`. - parallel_iterations: (optional) The number of iterations allowed to run in - parallel. When graph building, the default value is 10. While executing - eagerly, the default value is set to 1. - back_prop: (optional) Deprecated: prefer using \`tf.stop_gradient\` instead. False disables support for back propagation. - swap_memory: (optional) True enables GPU-CPU memory swapping. - infer_shape: (optional) False disables tests for consistent output shapes. - name: (optional) Name prefix for the returned tensors. - fn_output_signature: The output signature of \`fn\`. Must be specified if - \`fn\`'s input and output signatures are different (i.e., if their - structures, dtypes, or tensor types do not match). - \`fn_output_signature\` can be specified using any of the following: - - * A \`tf.DType\` or \`tf.TensorSpec\` (to describe a \`tf.Tensor\`) - * A \`tf.RaggedTensorSpec\` (to describe a \`tf.RaggedTensor\`) - * A \`tf.SparseTensorSpec\` (to describe a \`tf.sparse.SparseTensor\`) - * A (possibly nested) tuple, list, or dict containing the above types. - -Returns: - A tensor or (possibly nested) sequence of tensors. Each tensor stacks the - results of applying \`fn\` to tensors unstacked from \`elems\` along the first - dimension, from first to last. The result may include ragged and sparse - tensors. - -Raises: - TypeError: if \`fn\` is not callable or the structure of the output of - \`fn\` and \`fn_output_signature\` do not match. - ValueError: if the lengths of the output of \`fn\` and \`fn_output_signature\` - do not match, or if the \`elems\` does not contain any tensor. - -Examples: - - >>> elems = np.array([1, 2, 3, 4, 5, 6]) - >>> tf.map_fn(lambda x: x * x, elems) - - - >>> elems = (np.array([1, 2, 3]), np.array([-1, 1, -1])) - >>> tf.map_fn(lambda x: x[0] * x[1], elems, fn_output_signature=tf.int64) - - - >>> elems = np.array([1, 2, 3]) - >>> tf.map_fn(lambda x: (x, -x), elems, - ... fn_output_signature=(tf.int64, tf.int64)) - (, - )`},{name:"matmul",path:"./tf/linalg/matmul.md",desc:"Multiplies matrix `a` by matrix `b`, producing `a` * `b`.",type:"Functions",docs:"Multiplies matrix `a` by matrix `b`, producing `a` * `b`.\n\n The inputs must, following any transpositions, be tensors of rank >= 2\n where the inner 2 dimensions specify valid matrix multiplication dimensions,\n and any further outer dimensions specify matching batch size.\n\n Both matrices must be of the same type. The supported types are:\n `bfloat16`, `float16`, `float32`, `float64`, `int32`, `int64`,\n `complex64`, `complex128`.\n\n Either matrix can be transposed or adjointed (conjugated and transposed) on\n the fly by setting one of the corresponding flag to `True`. These are `False`\n by default.\n\n If one or both of the matrices contain a lot of zeros, a more efficient\n multiplication algorithm can be used by setting the corresponding\n `a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.\n This optimization is only available for plain matrices (rank-2 tensors) with\n datatypes `bfloat16` or `float32`.\n\n A simple 2-D tensor matrix multiplication:\n\n >>> a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])\n >>> a # 2-D tensor\n \n >>> b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])\n >>> b # 2-D tensor\n \n >>> c = tf.matmul(a, b)\n >>> c # `a` * `b`\n \n\n A batch matrix multiplication with batch shape [2]:\n\n >>> a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])\n >>> a # 3-D tensor\n \n >>> b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])\n >>> b # 3-D tensor\n \n >>> c = tf.matmul(a, b)\n >>> c # `a` * `b`\n \n\n Since python >= 3.5 the @ operator is supported\n (see [PEP 465](https://www.python.org/dev/peps/pep-0465/)). In TensorFlow,\n it simply calls the `tf.matmul()` function, so the following lines are\n equivalent:\n\n >>> d = a @ b @ [[10], [11]]\n >>> d = tf.matmul(tf.matmul(a, b), [[10], [11]])\n\n Args:\n a: `tf.Tensor` of type `float16`, `float32`, `float64`, `int32`,\n `complex64`, `complex128` and rank > 1.\n b: `tf.Tensor` with same type and rank as `a`.\n transpose_a: If `True`, `a` is transposed before multiplication.\n transpose_b: If `True`, `b` is transposed before multiplication.\n adjoint_a: If `True`, `a` is conjugated and transposed before\n multiplication.\n adjoint_b: If `True`, `b` is conjugated and transposed before\n multiplication.\n a_is_sparse: If `True`, `a` is treated as a sparse matrix. Notice, this\n **does not support `tf.sparse.SparseTensor`**, it just makes optimizations\n that assume most values in `a` are zero.\n See `tf.sparse.sparse_dense_matmul`\n for some support for `tf.sparse.SparseTensor` multiplication.\n b_is_sparse: If `True`, `b` is treated as a sparse matrix. Notice, this\n **does not support `tf.sparse.SparseTensor`**, it just makes optimizations\n that assume most values in `a` are zero.\n See `tf.sparse.sparse_dense_matmul`\n for some support for `tf.sparse.SparseTensor` multiplication.\n output_type: The output datatype if needed. Defaults to None in which case\n the output_type is the same as input type. Currently only works when input\n tensors are type (u)int8 and output_type can be int32.\n name: Name for the operation (optional).\n\n Returns:\n A `tf.Tensor` of the same type as `a` and `b` where each inner-most matrix\n is the product of the corresponding matrices in `a` and `b`, e.g. if all\n transpose or adjoint attributes are `False`:\n\n `output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j])`,\n for all indices `i`, `j`.\n\n Note: This is matrix product, not element-wise product.\n\n\n Raises:\n ValueError: If `transpose_a` and `adjoint_a`, or `transpose_b` and\n `adjoint_b` are both set to `True`.\n TypeError: If output_type is specified but the types of `a`, `b` and\n `output_type` is not (u)int8, (u)int8 and int32.\n "},{name:"matrix_square_root",path:"./tf/linalg/sqrtm.md",desc:"Computes the matrix square root of one or more square matrices:",type:"Functions",docs:`Computes the matrix square root of one or more square matrices: + `},{name:"map_fn",path:"./tf/map_fn.md",desc:"Transforms `elems` by applying `fn` to each element unstacked on axis 0. (deprecated arguments",type:"Functions",docs:"Transforms `elems` by applying `fn` to each element unstacked on axis 0. (deprecated arguments)\n\nDeprecated: SOME ARGUMENTS ARE DEPRECATED: `(dtype)`. They will be removed in a future version.\nInstructions for updating:\nUse fn_output_signature instead\n\nSee also `tf.scan`.\n\n`map_fn` unstacks `elems` on axis 0 to obtain a sequence of elements;\ncalls `fn` to transform each element; and then stacks the transformed\nvalues back together.\n\n#### Mapping functions with single-Tensor inputs and outputs\n\nIf `elems` is a single tensor and `fn`'s signature is `tf.Tensor->tf.Tensor`,\nthen `map_fn(fn, elems)` is equivalent to\n`tf.stack([fn(elem) for elem in tf.unstack(elems)])`. E.g.:\n\n```>>> tf.map_fn(fn=lambda t: tf.range(t, t + 3), elems=tf.constant([3, 5, 2]))\n\n\n```\n`map_fn(fn, elems).shape = [elems.shape[0]] + fn(elems[0]).shape`.\n\n#### Mapping functions with multi-arity inputs and outputs\n\n`map_fn` also supports functions with multi-arity inputs and outputs:\n\n* If `elems` is a tuple (or nested structure) of tensors, then those tensors\n must all have the same outer-dimension size (`num_elems`); and `fn` is\n used to transform each tuple (or structure) of corresponding slices from\n `elems`. E.g., if `elems` is a tuple `(t1, t2, t3)`, then `fn` is used to\n transform each tuple of slices `(t1[i], t2[i], t3[i])`\n (where `0 <= i < num_elems`).\n\n* If `fn` returns a tuple (or nested structure) of tensors, then the\n result is formed by stacking corresponding elements from those structures.\n\n#### Specifying `fn`'s output signature\n\nIf `fn`'s input and output signatures are different, then the output\nsignature must be specified using `fn_output_signature`. (The input and\noutput signatures are differ if their structures, dtypes, or tensor types do\nnot match). E.g.:\n\n```>>> tf.map_fn(fn=tf.strings.length, # input & output have different dtypes\n... elems=tf.constant([\"hello\", \"moon\"]),\n... fn_output_signature=tf.int32)\n\n>>> tf.map_fn(fn=tf.strings.join, # input & output have different structures\n... elems=[tf.constant(['The', 'A']), tf.constant(['Dog', 'Cat'])],\n... fn_output_signature=tf.string)\n\n\n```\n`fn_output_signature` can be specified using any of the following:\n\n* A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)\n* A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)\n* A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)\n* A (possibly nested) tuple, list, or dict containing the above types.\n\n#### RaggedTensors\n\n`map_fn` supports `tf.RaggedTensor` inputs and outputs. In particular:\n\n* If `elems` is a `RaggedTensor`, then `fn` will be called with each\n row of that ragged tensor.\n * If `elems` has only one ragged dimension, then the values passed to\n `fn` will be `tf.Tensor`s.\n * If `elems` has multiple ragged dimensions, then the values passed to\n `fn` will be `tf.RaggedTensor`s with one fewer ragged dimension.\n\n* If the result of `map_fn` should be a `RaggedTensor`, then use a\n `tf.RaggedTensorSpec` to specify `fn_output_signature`.\n * If `fn` returns `tf.Tensor`s with varying sizes, then use a\n `tf.RaggedTensorSpec` with `ragged_rank=0` to combine them into a\n single ragged tensor (which will have ragged_rank=1).\n * If `fn` returns `tf.RaggedTensor`s, then use a `tf.RaggedTensorSpec`\n with the same `ragged_rank`.\n\n```>>> # Example: RaggedTensor input\n>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])\n>>> tf.map_fn(tf.reduce_sum, rt, fn_output_signature=tf.int32)\n\n\n```\n```>>> # Example: RaggedTensor output\n>>> elems = tf.constant([3, 5, 0, 2])\n>>> tf.map_fn(tf.range, elems,\n... fn_output_signature=tf.RaggedTensorSpec(shape=[None],\n... dtype=tf.int32))\n\n\n```\nNote: `map_fn` should only be used if you need to map a function over the\n*rows* of a `RaggedTensor`. If you wish to map a function over the\nindividual values, then you should use:\n\n* `tf.ragged.map_flat_values(fn, rt)`\n (if fn is expressible as TensorFlow ops)\n* `rt.with_flat_values(map_fn(fn, rt.flat_values))`\n (otherwise)\n\nE.g.:\n\n```>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])\n>>> tf.ragged.map_flat_values(lambda x: x + 2, rt)\n\n\n```\n#### SparseTensors\n\n`map_fn` supports `tf.sparse.SparseTensor` inputs and outputs. In particular:\n\n* If `elems` is a `SparseTensor`, then `fn` will be called with each row\n of that sparse tensor. In particular, the value passed to `fn` will be a\n `tf.sparse.SparseTensor` with one fewer dimension than `elems`.\n\n* If the result of `map_fn` should be a `SparseTensor`, then use a\n `tf.SparseTensorSpec` to specify `fn_output_signature`. The individual\n `SparseTensor`s returned by `fn` will be stacked into a single\n `SparseTensor` with one more dimension.\n\n```>>> # Example: SparseTensor input\n>>> st = tf.sparse.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4])\n>>> tf.map_fn(tf.sparse.reduce_sum, st, fn_output_signature=tf.int32)\n\n\n```\n```>>> # Example: SparseTensor output\n>>> tf.sparse.to_dense(\n... tf.map_fn(tf.sparse.eye, tf.constant([2, 3]),\n... fn_output_signature=tf.SparseTensorSpec(None, tf.float32)))\n\n\n```\nNote: `map_fn` should only be used if you need to map a function over the\n*rows* of a `SparseTensor`. If you wish to map a function over the nonzero\nvalues, then you should use:\n\n* If the function is expressible as TensorFlow ops, use:\n ```python\n tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape)\n ```\n* Otherwise, use:\n ```python\n tf.sparse.SparseTensor(st.indices, tf.map_fn(fn, st.values),\n st.dense_shape)\n ```\n\n#### `map_fn` vs. vectorized operations\n\n`map_fn` will apply the operations used by `fn` to each element of `elems`,\nresulting in `O(elems.shape[0])` total operations. This is somewhat\nmitigated by the fact that `map_fn` can process elements in parallel.\nHowever, a transform expressed using `map_fn` is still typically less\nefficient than an equivalent transform expressed using vectorized operations.\n\n`map_fn` should typically only be used if one of the following is true:\n\n* It is difficult or expensive to express the desired transform with\n vectorized operations.\n* `fn` creates large intermediate values, so an equivalent vectorized\n transform would take too much memory.\n* Processing elements in parallel is more efficient than an equivalent\n vectorized transform.\n* Efficiency of the transform is not critical, and using `map_fn` is\n more readable.\n\nE.g., the example given above that maps `fn=lambda t: tf.range(t, t + 3)`\nacross `elems` could be rewritten more efficiently using vectorized ops:\n\n```>>> elems = tf.constant([3, 5, 2])\n>>> tf.range(3) + tf.expand_dims(elems, 1)\n\n\n```\nIn some cases, `tf.vectorized_map` can be used to automatically convert a\nfunction to a vectorized equivalent.\n\n#### Eager execution\n\nWhen executing eagerly, `map_fn` does not execute in parallel even if\n`parallel_iterations` is set to a value > 1. You can still get the\nperformance benefits of running a function in parallel by using the\n`tf.function` decorator:\n\n```>>> fn=lambda t: tf.range(t, t + 3)\n>>> @tf.function\n... def func(elems):\n... return tf.map_fn(fn, elems, parallel_iterations=3)\n>>> func(tf.constant([3, 5, 2]))\n\n\n```\n\nNote: if you use the `tf.function` decorator, any non-TensorFlow Python\ncode that you may have written in your function won't get executed. See\n`tf.function` for more details. The recommendation would be to debug without\n`tf.function` but switch to it to get performance benefits of running `map_fn`\nin parallel.\n\nArgs:\n fn: The callable to be performed. It accepts one argument, which will have\n the same (possibly nested) structure as `elems`. Its output must have the\n same structure as `fn_output_signature` if one is provided; otherwise it\n must have the same structure as `elems`.\n elems: A tensor or (possibly nested) sequence of tensors, each of which will\n be unstacked along their first dimension. `fn` will be applied to the\n nested sequence of the resulting slices. `elems` may include ragged and\n sparse tensors. `elems` must consist of at least one tensor.\n dtype: Deprecated: Equivalent to `fn_output_signature`.\n parallel_iterations: (optional) The number of iterations allowed to run in\n parallel. When graph building, the default value is 10. While executing\n eagerly, the default value is set to 1.\n back_prop: (optional) Deprecated: prefer using `tf.stop_gradient` instead. False disables support for back propagation.\n swap_memory: (optional) True enables GPU-CPU memory swapping.\n infer_shape: (optional) False disables tests for consistent output shapes.\n name: (optional) Name prefix for the returned tensors.\n fn_output_signature: The output signature of `fn`. Must be specified if\n `fn`'s input and output signatures are different (i.e., if their\n structures, dtypes, or tensor types do not match).\n `fn_output_signature` can be specified using any of the following:\n\n * A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)\n * A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)\n * A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)\n * A (possibly nested) tuple, list, or dict containing the above types.\n\nReturns:\n A tensor or (possibly nested) sequence of tensors. Each tensor stacks the\n results of applying `fn` to tensors unstacked from `elems` along the first\n dimension, from first to last. The result may include ragged and sparse\n tensors.\n\nRaises:\n TypeError: if `fn` is not callable or the structure of the output of\n `fn` and `fn_output_signature` do not match.\n ValueError: if the lengths of the output of `fn` and `fn_output_signature`\n do not match, or if the `elems` does not contain any tensor.\n\nExamples:\n\n ```>>> elems = np.array([1, 2, 3, 4, 5, 6])\n >>> tf.map_fn(lambda x: x * x, elems)\n \n\n```\n ```>>> elems = (np.array([1, 2, 3]), np.array([-1, 1, -1]))\n >>> tf.map_fn(lambda x: x[0] * x[1], elems, fn_output_signature=tf.int64)\n \n\n```\n >>> elems = np.array([1, 2, 3])\n >>> tf.map_fn(lambda x: (x, -x), elems,\n ... fn_output_signature=(tf.int64, tf.int64))\n (,\n )"},{name:"matmul",path:"./tf/linalg/matmul.md",desc:"Multiplies matrix `a` by matrix `b`, producing `a` * `b`.",type:"Functions",docs:"Multiplies matrix `a` by matrix `b`, producing `a` * `b`.\n\n The inputs must, following any transpositions, be tensors of rank >= 2\n where the inner 2 dimensions specify valid matrix multiplication dimensions,\n and any further outer dimensions specify matching batch size.\n\n Both matrices must be of the same type. The supported types are:\n `bfloat16`, `float16`, `float32`, `float64`, `int32`, `int64`,\n `complex64`, `complex128`.\n\n Either matrix can be transposed or adjointed (conjugated and transposed) on\n the fly by setting one of the corresponding flag to `True`. These are `False`\n by default.\n\n If one or both of the matrices contain a lot of zeros, a more efficient\n multiplication algorithm can be used by setting the corresponding\n `a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.\n This optimization is only available for plain matrices (rank-2 tensors) with\n datatypes `bfloat16` or `float32`.\n\n A simple 2-D tensor matrix multiplication:\n\n ```>>> a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])\n >>> a # 2-D tensor\n \n >>> b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])\n >>> b # 2-D tensor\n \n >>> c = tf.matmul(a, b)\n >>> c # `a` * `b`\n \n\n```\n A batch matrix multiplication with batch shape [2]:\n\n ```>>> a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])\n >>> a # 3-D tensor\n \n >>> b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])\n >>> b # 3-D tensor\n \n >>> c = tf.matmul(a, b)\n >>> c # `a` * `b`\n \n\n```\n Since python >= 3.5 the @ operator is supported\n (see [PEP 465](https://www.python.org/dev/peps/pep-0465/)). In TensorFlow,\n it simply calls the `tf.matmul()` function, so the following lines are\n equivalent:\n\n ```>>> d = a @ b @ [[10], [11]]\n >>> d = tf.matmul(tf.matmul(a, b), [[10], [11]])\n\n```\n Args:\n a: `tf.Tensor` of type `float16`, `float32`, `float64`, `int32`,\n `complex64`, `complex128` and rank > 1.\n b: `tf.Tensor` with same type and rank as `a`.\n transpose_a: If `True`, `a` is transposed before multiplication.\n transpose_b: If `True`, `b` is transposed before multiplication.\n adjoint_a: If `True`, `a` is conjugated and transposed before\n multiplication.\n adjoint_b: If `True`, `b` is conjugated and transposed before\n multiplication.\n a_is_sparse: If `True`, `a` is treated as a sparse matrix. Notice, this\n **does not support `tf.sparse.SparseTensor`**, it just makes optimizations\n that assume most values in `a` are zero.\n See `tf.sparse.sparse_dense_matmul`\n for some support for `tf.sparse.SparseTensor` multiplication.\n b_is_sparse: If `True`, `b` is treated as a sparse matrix. Notice, this\n **does not support `tf.sparse.SparseTensor`**, it just makes optimizations\n that assume most values in `a` are zero.\n See `tf.sparse.sparse_dense_matmul`\n for some support for `tf.sparse.SparseTensor` multiplication.\n output_type: The output datatype if needed. Defaults to None in which case\n the output_type is the same as input type. Currently only works when input\n tensors are type (u)int8 and output_type can be int32.\n name: Name for the operation (optional).\n\n Returns:\n A `tf.Tensor` of the same type as `a` and `b` where each inner-most matrix\n is the product of the corresponding matrices in `a` and `b`, e.g. if all\n transpose or adjoint attributes are `False`:\n\n `output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j])`,\n for all indices `i`, `j`.\n\n Note: This is matrix product, not element-wise product.\n\n\n Raises:\n ValueError: If `transpose_a` and `adjoint_a`, or `transpose_b` and\n `adjoint_b` are both set to `True`.\n TypeError: If output_type is specified but the types of `a`, `b` and\n `output_type` is not (u)int8, (u)int8 and int32.\n "},{name:"matrix_square_root",path:"./tf/linalg/sqrtm.md",desc:"Computes the matrix square root of one or more square matrices:",type:"Functions",docs:`Computes the matrix square root of one or more square matrices: matmul(sqrtm(A), sqrtm(A)) = A @@ -5161,7 +4403,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`input\`. - `},{name:"maximum",path:"./tf/math/maximum.md",desc:"Returns the max of x and y (i.e. x > y ? x y",type:"Functions",docs:"Returns the max of x and y (i.e. x > y ? x : y) element-wise.\n\n Example:\n\n >>> x = tf.constant([0., 0., 0., 0.])\n >>> y = tf.constant([-2., 0., 2., 5.])\n >>> tf.math.maximum(x, y)\n \n\n Note that `maximum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.\n\n >>> x = tf.constant([-5., 0., 0., 0.])\n >>> y = tf.constant([-3.])\n >>> tf.math.maximum(x, y)\n \n\n The reduction version of this elementwise operation is `tf.math.reduce_max`\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"meshgrid",path:"./tf/meshgrid.md",desc:"Broadcasts parameters for evaluation on an N-D grid.",type:"Functions",docs:`Broadcasts parameters for evaluation on an N-D grid. + `},{name:"maximum",path:"./tf/math/maximum.md",desc:"Returns the max of x and y (i.e. x > y ? x y",type:"Functions",docs:"Returns the max of x and y (i.e. x > y ? x : y) element-wise.\n\n Example:\n\n ```>>> x = tf.constant([0., 0., 0., 0.])\n >>> y = tf.constant([-2., 0., 2., 5.])\n >>> tf.math.maximum(x, y)\n \n\n```\n Note that `maximum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.\n\n ```>>> x = tf.constant([-5., 0., 0., 0.])\n >>> y = tf.constant([-3.])\n >>> tf.math.maximum(x, y)\n \n\n```\n The reduction version of this elementwise operation is `tf.math.reduce_max`\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"meshgrid",path:"./tf/meshgrid.md",desc:"Broadcasts parameters for evaluation on an N-D grid.",type:"Functions",docs:`Broadcasts parameters for evaluation on an N-D grid. Given N one-dimensional coordinate arrays \`*args\`, returns a list \`outputs\` of N-D coordinate arrays for evaluating expressions on an N-D grid. @@ -5200,7 +4442,7 @@ Examples: Raises: TypeError: When no keyword arguments (kwargs) are passed. ValueError: When indexing keyword argument is not one of \`xy\` or \`ij\`. - `},{name:"minimum",path:"./tf/math/minimum.md",desc:"Returns the min of x and y (i.e. x < y ? x y",type:"Functions",docs:"Returns the min of x and y (i.e. x < y ? x : y) element-wise.\n\n Both inputs are number-type tensors (except complex). `minimum` expects that\n both tensors have the same `dtype`.\n\n Examples:\n\n >>> x = tf.constant([0., 0., 0., 0.])\n >>> y = tf.constant([-5., -2., 0., 3.])\n >>> tf.math.minimum(x, y)\n \n\n Note that `minimum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.\n\n >>> x = tf.constant([-5., 0., 0., 0.])\n >>> y = tf.constant([-3.])\n >>> tf.math.minimum(x, y)\n \n\n The reduction version of this elementwise operation is `tf.math.reduce_min`\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"multiply",path:"./tf/math/multiply.md",desc:"Returns an element-wise x * y.",type:"Functions",docs:"Returns an element-wise x * y.\n\n For example:\n\n >>> x = tf.constant(([1, 2, 3, 4]))\n >>> tf.math.multiply(x, x)\n \n\n Since `tf.math.multiply` will convert its arguments to `Tensor`s, you can also\n pass in non-`Tensor` arguments:\n\n >>> tf.math.multiply(7,6)\n \n\n If `x.shape` is not the same as `y.shape`, they will be broadcast to a\n compatible shape. (More about broadcasting\n [here](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).)\n\n For example:\n\n >>> x = tf.ones([1, 2]);\n >>> y = tf.ones([2, 1]);\n >>> x * y # Taking advantage of operator overriding\n \n\n The reduction version of this elementwise operation is `tf.math.reduce_prod`\n\n Args:\n x: A Tensor. Must be one of the following types: `bfloat16`,\n `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`,\n `int16`, `int32`, `int64`, `complex64`, `complex128`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n\n A `Tensor`. Has the same type as `x`.\n\n Raises:\n\n * InvalidArgumentError: When `x` and `y` have incompatible shapes or types.\n "},{name:"negative",path:"./tf/math/negative.md",desc:"Computes numerical negative value element-wise.",type:"Functions",docs:"Computes numerical negative value element-wise.\n\n I.e., \\\\(y = -x\\\\).\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.negative(x.values, ...), x.dense_shape)`"},{name:"no_gradient",path:"./tf/no_gradient.md",desc:"Specifies that ops of type `op_type` is not differentiable.",type:"Functions",docs:`Specifies that ops of type \`op_type\` is not differentiable. + `},{name:"minimum",path:"./tf/math/minimum.md",desc:"Returns the min of x and y (i.e. x < y ? x y",type:"Functions",docs:"Returns the min of x and y (i.e. x < y ? x : y) element-wise.\n\n Both inputs are number-type tensors (except complex). `minimum` expects that\n both tensors have the same `dtype`.\n\n Examples:\n\n ```>>> x = tf.constant([0., 0., 0., 0.])\n >>> y = tf.constant([-5., -2., 0., 3.])\n >>> tf.math.minimum(x, y)\n \n\n```\n Note that `minimum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.\n\n ```>>> x = tf.constant([-5., 0., 0., 0.])\n >>> y = tf.constant([-3.])\n >>> tf.math.minimum(x, y)\n \n\n```\n The reduction version of this elementwise operation is `tf.math.reduce_min`\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"multiply",path:"./tf/math/multiply.md",desc:"Returns an element-wise x * y.",type:"Functions",docs:"Returns an element-wise x * y.\n\n For example:\n\n ```>>> x = tf.constant(([1, 2, 3, 4]))\n >>> tf.math.multiply(x, x)\n \n\n```\n Since `tf.math.multiply` will convert its arguments to `Tensor`s, you can also\n pass in non-`Tensor` arguments:\n\n ```>>> tf.math.multiply(7,6)\n \n\n```\n If `x.shape` is not the same as `y.shape`, they will be broadcast to a\n compatible shape. (More about broadcasting\n [here](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).)\n\n For example:\n\n ```>>> x = tf.ones([1, 2]);\n >>> y = tf.ones([2, 1]);\n >>> x * y # Taking advantage of operator overriding\n \n\n```\n The reduction version of this elementwise operation is `tf.math.reduce_prod`\n\n Args:\n x: A Tensor. Must be one of the following types: `bfloat16`,\n `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`,\n `int16`, `int32`, `int64`, `complex64`, `complex128`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n\n A `Tensor`. Has the same type as `x`.\n\n Raises:\n\n * InvalidArgumentError: When `x` and `y` have incompatible shapes or types.\n "},{name:"negative",path:"./tf/math/negative.md",desc:"Computes numerical negative value element-wise.",type:"Functions",docs:"Computes numerical negative value element-wise.\n\n I.e., \\\\(y = -x\\\\).\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.negative(x.values, ...), x.dense_shape)`"},{name:"no_gradient",path:"./tf/no_gradient.md",desc:"Specifies that ops of type `op_type` is not differentiable.",type:"Functions",docs:`Specifies that ops of type \`op_type\` is not differentiable. This function should *not* be used for operations that have a well-defined gradient that is not yet implemented. @@ -5290,16 +4532,18 @@ Examples: For example: - >>> x = tf.constant([2, 4]) + \`\`\`>>> x = tf.constant([2, 4]) >>> y = tf.constant(2) >>> tf.math.not_equal(x, y) - >>> x = tf.constant([2, 4]) +\`\`\` + \`\`\`>>> x = tf.constant([2, 4]) >>> y = tf.constant([2, 4]) >>> tf.math.not_equal(x, y) +\`\`\` Args: x: A \`tf.Tensor\` or \`tf.sparse.SparseTensor\` or \`tf.IndexedSlices\`. y: A \`tf.Tensor\` or \`tf.sparse.SparseTensor\` or \`tf.IndexedSlices\`. @@ -5310,87 +4554,7 @@ Examples: Raises: \`tf.errors.InvalidArgumentError\`: If shapes of arguments are incompatible - `},{name:"numpy_function",path:"./tf/numpy_function.md",desc:"Wraps a python function and uses it as a TensorFlow op.",type:"Functions",docs:`Wraps a python function and uses it as a TensorFlow op. - - Given a python function \`func\` wrap this function as an operation in a - TensorFlow function. \`func\` must take numpy arrays as its arguments and - return numpy arrays as its outputs. - - The following example creates a TensorFlow graph with \`np.sinh()\` as an - operation in the graph: - - >>> def my_numpy_func(x): - ... # x will be a numpy array with the contents of the input to the - ... # tf.function - ... return np.sinh(x) - >>> @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)]) - ... def tf_function(input): - ... y = tf.numpy_function(my_numpy_func, [input], tf.float32) - ... return y * y - >>> tf_function(tf.constant(1.)) - - - Comparison to \`tf.py_function\`: - \`tf.py_function\` and \`tf.numpy_function\` are very similar, except that - \`tf.numpy_function\` takes numpy arrays, and not \`tf.Tensor\`s. If you want the - function to contain \`tf.Tensors\`, and have any TensorFlow operations executed - in the function be differentiable, please use \`tf.py_function\`. - - Note: We recommend to avoid using \`tf.numpy_function\` outside of - prototyping and experimentation due to the following known limitations: - - * Calling \`tf.numpy_function\` will acquire the Python Global Interpreter Lock - (GIL) that allows only one thread to run at any point in time. This will - preclude efficient parallelization and distribution of the execution of the - program. Therefore, you are discouraged to use \`tf.numpy_function\` outside - of prototyping and experimentation. - - * The body of the function (i.e. \`func\`) will not be serialized in a - \`tf.SavedModel\`. Therefore, you should not use this function if you need to - serialize your model and restore it in a different environment. - - * The operation must run in the same address space as the Python program - that calls \`tf.numpy_function()\`. If you are using distributed - TensorFlow, you must run a \`tf.distribute.Server\` in the same process as the - program that calls \`tf.numpy_function\` you must pin the created - operation to a device in that server (e.g. using \`with tf.device():\`). - - * Currently \`tf.numpy_function\` is not compatible with XLA. Calling - \`tf.numpy_function\` inside \`tf.function(jit_comiple=True)\` will raise an - error. - - * Since the function takes numpy arrays, you cannot take gradients - through a numpy_function. If you require something that is differentiable, - please consider using tf.py_function. - - Args: - func: A Python function, which accepts \`numpy.ndarray\` objects as arguments - and returns a list of \`numpy.ndarray\` objects (or a single - \`numpy.ndarray\`). This function must accept as many arguments as there are - tensors in \`inp\`, and these argument types will match the corresponding - \`tf.Tensor\` objects in \`inp\`. The returns \`numpy.ndarray\`s must match the - number and types defined \`Tout\`. - Important Note: Input and output \`numpy.ndarray\`s of \`func\` are not - guaranteed to be copies. In some cases their underlying memory will be - shared with the corresponding TensorFlow tensors. In-place modification - or storing \`func\` input or return values in python datastructures - without explicit (np.)copy can have non-deterministic consequences. - inp: A list of \`tf.Tensor\` objects. - Tout: A list or tuple of tensorflow data types or a single tensorflow data - type if there is only one, indicating what \`func\` returns. - stateful: (Boolean.) Setting this argument to False tells the runtime to - treat the function as stateless, which enables certain optimizations. - A function is stateless when given the same input it will return the - same output and have no side effects; its only purpose is to have a - return value. - The behavior for a stateful function with the \`stateful\` argument False - is undefined. In particular, caution should be taken when - mutating the input arguments as this is a stateful operation. - name: (Optional) A name for the operation. - - Returns: - Single or list of \`tf.Tensor\` which \`func\` computes. - `},{name:"one_hot",path:"./tf/one_hot.md",desc:"Returns a one-hot tensor.",type:"Functions",docs:"Returns a one-hot tensor.\n\n See also `tf.fill`, `tf.eye`.\n\n The locations represented by indices in `indices` take value `on_value`,\n while all other locations take value `off_value`.\n\n `on_value` and `off_value` must have matching data types. If `dtype` is also\n provided, they must be the same data type as specified by `dtype`.\n\n If `on_value` is not provided, it will default to the value `1` with type\n `dtype`\n\n If `off_value` is not provided, it will default to the value `0` with type\n `dtype`\n\n If the input `indices` is rank `N`, the output will have rank `N+1`. The\n new axis is created at dimension `axis` (default: the new axis is appended\n at the end).\n\n If `indices` is a scalar the output shape will be a vector of length `depth`\n\n If `indices` is a vector of length `features`, the output shape will be:\n\n ```\n features x depth if axis == -1\n depth x features if axis == 0\n ```\n\n If `indices` is a matrix (batch) with shape `[batch, features]`, the output\n shape will be:\n\n ```\n batch x features x depth if axis == -1\n batch x depth x features if axis == 1\n depth x batch x features if axis == 0\n ```\n\n If `indices` is a RaggedTensor, the 'axis' argument must be positive and refer\n to a non-ragged axis. The output will be equivalent to applying 'one_hot' on\n the values of the RaggedTensor, and creating a new RaggedTensor from the\n result.\n\n If `dtype` is not provided, it will attempt to assume the data type of\n `on_value` or `off_value`, if one or both are passed in. If none of\n `on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the\n value `tf.float32`.\n\n Note: If a non-numeric data type output is desired (`tf.string`, `tf.bool`,\n etc.), both `on_value` and `off_value` _must_ be provided to `one_hot`.\n\n For example:\n\n ```python\n indices = [0, 1, 2]\n depth = 3\n tf.one_hot(indices, depth) # output: [3 x 3]\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n indices = [0, 2, -1, 1]\n depth = 3\n tf.one_hot(indices, depth,\n on_value=5.0, off_value=0.0,\n axis=-1) # output: [4 x 3]\n # [[5.0, 0.0, 0.0], # one_hot(0)\n # [0.0, 0.0, 5.0], # one_hot(2)\n # [0.0, 0.0, 0.0], # one_hot(-1)\n # [0.0, 5.0, 0.0]] # one_hot(1)\n\n indices = [[0, 2], [1, -1]]\n depth = 3\n tf.one_hot(indices, depth,\n on_value=1.0, off_value=0.0,\n axis=-1) # output: [2 x 2 x 3]\n # [[[1.0, 0.0, 0.0], # one_hot(0)\n # [0.0, 0.0, 1.0]], # one_hot(2)\n # [[0.0, 1.0, 0.0], # one_hot(1)\n # [0.0, 0.0, 0.0]]] # one_hot(-1)\n\n indices = tf.ragged.constant([[0, 1], [2]])\n depth = 3\n tf.one_hot(indices, depth) # output: [2 x None x 3]\n # [[[1., 0., 0.],\n # [0., 1., 0.]],\n # [[0., 0., 1.]]]\n ```\n\n Args:\n indices: A `Tensor` of indices.\n depth: A scalar defining the depth of the one hot dimension.\n on_value: A scalar defining the value to fill in output when `indices[j]\n = i`. (default: 1)\n off_value: A scalar defining the value to fill in output when `indices[j]\n != i`. (default: 0)\n axis: The axis to fill (default: -1, a new inner-most axis).\n dtype: The data type of the output tensor.\n name: A name for the operation (optional).\n\n Returns:\n output: The one-hot tensor.\n\n Raises:\n TypeError: If dtype of either `on_value` or `off_value` don't match `dtype`\n TypeError: If dtype of `on_value` and `off_value` don't match one another\n "},{name:"ones",path:"./tf/ones.md",desc:"Creates a tensor with all elements set to one (1",type:"Functions",docs:"Creates a tensor with all elements set to one (1).\n\n See also `tf.ones_like`, `tf.zeros`, `tf.fill`, `tf.eye`.\n\n This operation returns a tensor of type `dtype` with shape `shape` and\n all elements set to one.\n\n >>> tf.ones([3, 4], tf.int32)\n \n\n Args:\n shape: A `list` of integers, a `tuple` of integers, or\n a 1-D `Tensor` of type `int32`.\n dtype: Optional DType of an element in the resulting `Tensor`. Default is\n `tf.float32`.\n name: Optional string. A name for the operation.\n\n Returns:\n A `Tensor` with all elements set to one (1).\n "},{name:"ones_like",path:"./tf/ones_like.md",desc:"Creates a tensor of all ones that has the same shape as the input.",type:"Functions",docs:"Creates a tensor of all ones that has the same shape as the input.\n\n See also `tf.ones`.\n\n Given a single tensor (`tensor`), this operation returns a tensor of the\n same type and shape as `tensor` with all elements set to 1. Optionally,\n you can use `dtype` to specify a new type for the returned tensor.\n\n For example:\n\n >>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])\n >>> tf.ones_like(tensor)\n \n\n Args:\n input: A `Tensor`.\n dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,\n `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,\n `complex64`, `complex128`, `bool` or `string`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` with all elements set to one.\n "},{name:"pad",path:"./tf/pad.md",desc:"Pads a tensor.",type:"Functions",docs:'Pads a tensor.\n\n This operation pads a `tensor` according to the `paddings` you specify.\n `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of\n `tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how\n many values to add before the contents of `tensor` in that dimension, and\n `paddings[D, 1]` indicates how many values to add after the contents of\n `tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`\n and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If\n `mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be\n no greater than `tensor.dim_size(D)`.\n\n The padded size of each dimension D of the output is:\n\n `paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`\n\n For example:\n\n ```python\n t = tf.constant([[1, 2, 3], [4, 5, 6]])\n paddings = tf.constant([[1, 1,], [2, 2]])\n # \'constant_values\' is 0.\n # rank of \'t\' is 2.\n tf.pad(t, paddings, "CONSTANT") # [[0, 0, 0, 0, 0, 0, 0],\n # [0, 0, 1, 2, 3, 0, 0],\n # [0, 0, 4, 5, 6, 0, 0],\n # [0, 0, 0, 0, 0, 0, 0]]\n\n tf.pad(t, paddings, "REFLECT") # [[6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1],\n # [6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1]]\n\n tf.pad(t, paddings, "SYMMETRIC") # [[2, 1, 1, 2, 3, 3, 2],\n # [2, 1, 1, 2, 3, 3, 2],\n # [5, 4, 4, 5, 6, 6, 5],\n # [5, 4, 4, 5, 6, 6, 5]]\n ```\n\n Args:\n tensor: A `Tensor`.\n paddings: A `Tensor` of type `int32`.\n mode: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)\n constant_values: In "CONSTANT" mode, the scalar pad value to use. Must be\n same type as `tensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n\n Raises:\n ValueError: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".\n '},{name:"parallel_stack",path:"./tf/parallel_stack.md",desc:"Stacks a list of rank-`R` tensors into one rank-`(R+1",type:"Functions",docs:`Stacks a list of rank-\`R\` tensors into one rank-\`(R+1)\` tensor in parallel. + `},{name:"numpy_function",path:"./tf/numpy_function.md",desc:"Wraps a python function and uses it as a TensorFlow op.",type:"Functions",docs:"Wraps a python function and uses it as a TensorFlow op.\n\n Given a python function `func` wrap this function as an operation in a\n TensorFlow function. `func` must take numpy arrays as its arguments and\n return numpy arrays as its outputs.\n\n The following example creates a TensorFlow graph with `np.sinh()` as an\n operation in the graph:\n\n ```>>> def my_numpy_func(x):\n ... # x will be a numpy array with the contents of the input to the\n ... # tf.function\n ... return np.sinh(x)\n >>> @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])\n ... def tf_function(input):\n ... y = tf.numpy_function(my_numpy_func, [input], tf.float32)\n ... return y * y\n >>> tf_function(tf.constant(1.))\n \n\n```\n Comparison to `tf.py_function`:\n `tf.py_function` and `tf.numpy_function` are very similar, except that\n `tf.numpy_function` takes numpy arrays, and not `tf.Tensor`s. If you want the\n function to contain `tf.Tensors`, and have any TensorFlow operations executed\n in the function be differentiable, please use `tf.py_function`.\n\n Note: We recommend to avoid using `tf.numpy_function` outside of\n prototyping and experimentation due to the following known limitations:\n\n * Calling `tf.numpy_function` will acquire the Python Global Interpreter Lock\n (GIL) that allows only one thread to run at any point in time. This will\n preclude efficient parallelization and distribution of the execution of the\n program. Therefore, you are discouraged to use `tf.numpy_function` outside\n of prototyping and experimentation.\n\n * The body of the function (i.e. `func`) will not be serialized in a\n `tf.SavedModel`. Therefore, you should not use this function if you need to\n serialize your model and restore it in a different environment.\n\n * The operation must run in the same address space as the Python program\n that calls `tf.numpy_function()`. If you are using distributed\n TensorFlow, you must run a `tf.distribute.Server` in the same process as the\n program that calls `tf.numpy_function` you must pin the created\n operation to a device in that server (e.g. using `with tf.device():`).\n\n * Currently `tf.numpy_function` is not compatible with XLA. Calling\n `tf.numpy_function` inside `tf.function(jit_comiple=True)` will raise an\n error.\n\n * Since the function takes numpy arrays, you cannot take gradients\n through a numpy_function. If you require something that is differentiable,\n please consider using tf.py_function.\n\n Args:\n func: A Python function, which accepts `numpy.ndarray` objects as arguments\n and returns a list of `numpy.ndarray` objects (or a single\n `numpy.ndarray`). This function must accept as many arguments as there are\n tensors in `inp`, and these argument types will match the corresponding\n `tf.Tensor` objects in `inp`. The returns `numpy.ndarray`s must match the\n number and types defined `Tout`.\n Important Note: Input and output `numpy.ndarray`s of `func` are not\n guaranteed to be copies. In some cases their underlying memory will be\n shared with the corresponding TensorFlow tensors. In-place modification\n or storing `func` input or return values in python datastructures\n without explicit (np.)copy can have non-deterministic consequences.\n inp: A list of `tf.Tensor` objects.\n Tout: A list or tuple of tensorflow data types or a single tensorflow data\n type if there is only one, indicating what `func` returns.\n stateful: (Boolean.) Setting this argument to False tells the runtime to\n treat the function as stateless, which enables certain optimizations.\n A function is stateless when given the same input it will return the\n same output and have no side effects; its only purpose is to have a\n return value.\n The behavior for a stateful function with the `stateful` argument False\n is undefined. In particular, caution should be taken when\n mutating the input arguments as this is a stateful operation.\n name: (Optional) A name for the operation.\n\n Returns:\n Single or list of `tf.Tensor` which `func` computes.\n "},{name:"one_hot",path:"./tf/one_hot.md",desc:"Returns a one-hot tensor.",type:"Functions",docs:"Returns a one-hot tensor.\n\n See also `tf.fill`, `tf.eye`.\n\n The locations represented by indices in `indices` take value `on_value`,\n while all other locations take value `off_value`.\n\n `on_value` and `off_value` must have matching data types. If `dtype` is also\n provided, they must be the same data type as specified by `dtype`.\n\n If `on_value` is not provided, it will default to the value `1` with type\n `dtype`\n\n If `off_value` is not provided, it will default to the value `0` with type\n `dtype`\n\n If the input `indices` is rank `N`, the output will have rank `N+1`. The\n new axis is created at dimension `axis` (default: the new axis is appended\n at the end).\n\n If `indices` is a scalar the output shape will be a vector of length `depth`\n\n If `indices` is a vector of length `features`, the output shape will be:\n\n ```\n features x depth if axis == -1\n depth x features if axis == 0\n ```\n\n If `indices` is a matrix (batch) with shape `[batch, features]`, the output\n shape will be:\n\n ```\n batch x features x depth if axis == -1\n batch x depth x features if axis == 1\n depth x batch x features if axis == 0\n ```\n\n If `indices` is a RaggedTensor, the 'axis' argument must be positive and refer\n to a non-ragged axis. The output will be equivalent to applying 'one_hot' on\n the values of the RaggedTensor, and creating a new RaggedTensor from the\n result.\n\n If `dtype` is not provided, it will attempt to assume the data type of\n `on_value` or `off_value`, if one or both are passed in. If none of\n `on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the\n value `tf.float32`.\n\n Note: If a non-numeric data type output is desired (`tf.string`, `tf.bool`,\n etc.), both `on_value` and `off_value` _must_ be provided to `one_hot`.\n\n For example:\n\n ```python\n indices = [0, 1, 2]\n depth = 3\n tf.one_hot(indices, depth) # output: [3 x 3]\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n indices = [0, 2, -1, 1]\n depth = 3\n tf.one_hot(indices, depth,\n on_value=5.0, off_value=0.0,\n axis=-1) # output: [4 x 3]\n # [[5.0, 0.0, 0.0], # one_hot(0)\n # [0.0, 0.0, 5.0], # one_hot(2)\n # [0.0, 0.0, 0.0], # one_hot(-1)\n # [0.0, 5.0, 0.0]] # one_hot(1)\n\n indices = [[0, 2], [1, -1]]\n depth = 3\n tf.one_hot(indices, depth,\n on_value=1.0, off_value=0.0,\n axis=-1) # output: [2 x 2 x 3]\n # [[[1.0, 0.0, 0.0], # one_hot(0)\n # [0.0, 0.0, 1.0]], # one_hot(2)\n # [[0.0, 1.0, 0.0], # one_hot(1)\n # [0.0, 0.0, 0.0]]] # one_hot(-1)\n\n indices = tf.ragged.constant([[0, 1], [2]])\n depth = 3\n tf.one_hot(indices, depth) # output: [2 x None x 3]\n # [[[1., 0., 0.],\n # [0., 1., 0.]],\n # [[0., 0., 1.]]]\n ```\n\n Args:\n indices: A `Tensor` of indices.\n depth: A scalar defining the depth of the one hot dimension.\n on_value: A scalar defining the value to fill in output when `indices[j]\n = i`. (default: 1)\n off_value: A scalar defining the value to fill in output when `indices[j]\n != i`. (default: 0)\n axis: The axis to fill (default: -1, a new inner-most axis).\n dtype: The data type of the output tensor.\n name: A name for the operation (optional).\n\n Returns:\n output: The one-hot tensor.\n\n Raises:\n TypeError: If dtype of either `on_value` or `off_value` don't match `dtype`\n TypeError: If dtype of `on_value` and `off_value` don't match one another\n "},{name:"ones",path:"./tf/ones.md",desc:"Creates a tensor with all elements set to one (1",type:"Functions",docs:"Creates a tensor with all elements set to one (1).\n\n See also `tf.ones_like`, `tf.zeros`, `tf.fill`, `tf.eye`.\n\n This operation returns a tensor of type `dtype` with shape `shape` and\n all elements set to one.\n\n ```>>> tf.ones([3, 4], tf.int32)\n \n\n```\n Args:\n shape: A `list` of integers, a `tuple` of integers, or\n a 1-D `Tensor` of type `int32`.\n dtype: Optional DType of an element in the resulting `Tensor`. Default is\n `tf.float32`.\n name: Optional string. A name for the operation.\n\n Returns:\n A `Tensor` with all elements set to one (1).\n "},{name:"ones_like",path:"./tf/ones_like.md",desc:"Creates a tensor of all ones that has the same shape as the input.",type:"Functions",docs:"Creates a tensor of all ones that has the same shape as the input.\n\n See also `tf.ones`.\n\n Given a single tensor (`tensor`), this operation returns a tensor of the\n same type and shape as `tensor` with all elements set to 1. Optionally,\n you can use `dtype` to specify a new type for the returned tensor.\n\n For example:\n\n ```>>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])\n >>> tf.ones_like(tensor)\n \n\n```\n Args:\n input: A `Tensor`.\n dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,\n `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,\n `complex64`, `complex128`, `bool` or `string`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` with all elements set to one.\n "},{name:"pad",path:"./tf/pad.md",desc:"Pads a tensor.",type:"Functions",docs:'Pads a tensor.\n\n This operation pads a `tensor` according to the `paddings` you specify.\n `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of\n `tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how\n many values to add before the contents of `tensor` in that dimension, and\n `paddings[D, 1]` indicates how many values to add after the contents of\n `tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`\n and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If\n `mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be\n no greater than `tensor.dim_size(D)`.\n\n The padded size of each dimension D of the output is:\n\n `paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`\n\n For example:\n\n ```python\n t = tf.constant([[1, 2, 3], [4, 5, 6]])\n paddings = tf.constant([[1, 1,], [2, 2]])\n # \'constant_values\' is 0.\n # rank of \'t\' is 2.\n tf.pad(t, paddings, "CONSTANT") # [[0, 0, 0, 0, 0, 0, 0],\n # [0, 0, 1, 2, 3, 0, 0],\n # [0, 0, 4, 5, 6, 0, 0],\n # [0, 0, 0, 0, 0, 0, 0]]\n\n tf.pad(t, paddings, "REFLECT") # [[6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1],\n # [6, 5, 4, 5, 6, 5, 4],\n # [3, 2, 1, 2, 3, 2, 1]]\n\n tf.pad(t, paddings, "SYMMETRIC") # [[2, 1, 1, 2, 3, 3, 2],\n # [2, 1, 1, 2, 3, 3, 2],\n # [5, 4, 4, 5, 6, 6, 5],\n # [5, 4, 4, 5, 6, 6, 5]]\n ```\n\n Args:\n tensor: A `Tensor`.\n paddings: A `Tensor` of type `int32`.\n mode: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)\n constant_values: In "CONSTANT" mode, the scalar pad value to use. Must be\n same type as `tensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n\n Raises:\n ValueError: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".\n '},{name:"parallel_stack",path:"./tf/parallel_stack.md",desc:"Stacks a list of rank-`R` tensors into one rank-`(R+1",type:"Functions",docs:`Stacks a list of rank-\`R\` tensors into one rank-\`(R+1)\` tensor in parallel. Requires that the shape of inputs be known at graph construction time. @@ -5556,25 +4720,28 @@ Examples: For example: - >>> start = 3 + \`\`\`>>> start = 3 >>> limit = 18 >>> delta = 3 >>> tf.range(start, limit, delta) - >>> start = 3 +\`\`\` + \`\`\`>>> start = 3 >>> limit = 1 >>> delta = -0.5 >>> tf.range(start, limit, delta) - >>> limit = 5 +\`\`\` + \`\`\`>>> limit = 5 >>> tf.range(limit) +\`\`\` Args: start: A 0-D \`Tensor\` (scalar). Acts as first entry in the range if \`limit\` is not None; otherwise, acts as range limit and first entry defaults to 0. @@ -5634,16 +4801,19 @@ Examples: This recomputation of the function during backpropagation performs redundant calculation, but reduces the overall memory usage of the Tape. - >>> y = tf.Variable(1.0) + \`\`\`>>> y = tf.Variable(1.0) - >>> def my_function(x): +\`\`\` + \`\`\`>>> def my_function(x): ... tf.print('running') ... z = x*y ... return z - >>> my_function_recompute = tf.recompute_grad(my_function) +\`\`\` + \`\`\`>>> my_function_recompute = tf.recompute_grad(my_function) - >>> with tf.GradientTape() as tape: +\`\`\` + \`\`\`>>> with tf.GradientTape() as tape: ... r = tf.constant(1.0) ... for i in range(4): ... r = my_function_recompute(r) @@ -5652,16 +4822,18 @@ Examples: running running - >>> grad = tape.gradient(r, [y]) +\`\`\` + \`\`\`>>> grad = tape.gradient(r, [y]) running running running running +\`\`\` Without \`recompute_grad\`, the tape contains all intermitate steps, and no recomputation is performed. - >>> with tf.GradientTape() as tape: + \`\`\`>>> with tf.GradientTape() as tape: ... r = tf.constant(1.0) ... for i in range(4): ... r = my_function(r) @@ -5670,8 +4842,10 @@ Examples: running running - >>> grad = tape.gradient(r, [y]) +\`\`\` + \`\`\`>>> grad = tape.gradient(r, [y]) +\`\`\` If \`f\` was a \`tf.keras\` \`Model\` or \`Layer\` object, methods and attributes such as \`f.variables\` are not available on the returned function \`g\`. @@ -5679,18 +4853,21 @@ Examples: these variables and methods. - >>> def print_running_and_return(x): + \`\`\`>>> def print_running_and_return(x): ... tf.print("running") ... return x - >>> model = tf.keras.Sequential([ +\`\`\` + \`\`\`>>> model = tf.keras.Sequential([ ... tf.keras.layers.Lambda(print_running_and_return), ... tf.keras.layers.Dense(2) ... ]) - >>> model_recompute = tf.recompute_grad(model) +\`\`\` + \`\`\`>>> model_recompute = tf.recompute_grad(model) - >>> with tf.GradientTape(persistent=True) as tape: +\`\`\` + \`\`\`>>> with tf.GradientTape(persistent=True) as tape: ... r = tf.constant([[1,2]]) ... for i in range(4): ... r = model_recompute(r) @@ -5699,21 +4876,24 @@ Examples: running running - >>> grad = tape.gradient(r, model.variables) +\`\`\` + \`\`\`>>> grad = tape.gradient(r, model.variables) running running running running +\`\`\` Alternatively, use the \`__wrapped__\` attribute to access the original model object. - >>> grad = tape.gradient(r, model_recompute.__wrapped__.variables) + \`\`\`>>> grad = tape.gradient(r, model_recompute.__wrapped__.variables) running running running running +\`\`\` Args: f: function \`f(*x)\` that returns a \`Tensor\` or sequence of \`Tensor\` outputs. @@ -5735,7 +4915,7 @@ Examples: For example: - >>> x = tf.constant([[True, True], [False, False]]) + \`\`\`>>> x = tf.constant([[True, True], [False, False]]) >>> tf.math.reduce_all(x) >>> tf.math.reduce_all(x, 0) @@ -5743,6 +4923,7 @@ Examples: >>> tf.math.reduce_all(x, 1) +\`\`\` Args: input_tensor: The boolean tensor to reduce. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -5771,7 +4952,7 @@ Examples: For example: - >>> x = tf.constant([[True, True], [False, False]]) + \`\`\`>>> x = tf.constant([[True, True], [False, False]]) >>> tf.reduce_any(x) >>> tf.reduce_any(x, 0) @@ -5779,6 +4960,7 @@ Examples: >>> tf.reduce_any(x, 1) +\`\`\` Args: input_tensor: The boolean tensor to reduce. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -5842,7 +5024,7 @@ Examples: Usage example: - >>> x = tf.constant([5, 1, 2, 4]) + \`\`\`>>> x = tf.constant([5, 1, 2, 4]) >>> tf.reduce_max(x) >>> x = tf.constant([-5, -1, -2, -4]) @@ -5858,6 +5040,7 @@ Examples: >>> tf.reduce_max(x) +\`\`\` See the numpy docs for \`np.amax\` and \`np.nanmax\` behavior. Args: @@ -5883,7 +5066,7 @@ Examples: For example: - >>> x = tf.constant([[1., 1.], [2., 2.]]) + \`\`\`>>> x = tf.constant([[1., 1.], [2., 2.]]) >>> tf.reduce_mean(x) >>> tf.reduce_mean(x, 0) @@ -5891,6 +5074,7 @@ Examples: >>> tf.reduce_mean(x, 1) +\`\`\` Args: input_tensor: The tensor to reduce. Should have numeric type. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -5910,13 +5094,14 @@ Examples: hand, \`tf.reduce_mean\` has an aggressive type inference from \`input_tensor\`, for example: - >>> x = tf.constant([1, 0, 1, 0]) + \`\`\`>>> x = tf.constant([1, 0, 1, 0]) >>> tf.reduce_mean(x) >>> y = tf.constant([1., 0., 1., 0.]) >>> tf.reduce_mean(y) +\`\`\` @end_compatibility `},{name:"reduce_min",path:"./tf/math/reduce_min.md",desc:'Computes the tf.math.minimum of elements across dimensions of a tensor.',type:"Functions",docs:`Computes the \`tf.math.minimum\` of elements across dimensions of a tensor. @@ -5932,30 +5117,33 @@ Examples: For example: - >>> a = tf.constant([ + \`\`\`>>> a = tf.constant([ ... [[1, 2], [3, 4]], ... [[1, 2], [3, 4]] ... ]) >>> tf.reduce_min(a) +\`\`\` Choosing a specific axis returns minimum element in the given axis: - >>> b = tf.constant([[1, 2, 3], [4, 5, 6]]) + \`\`\`>>> b = tf.constant([[1, 2, 3], [4, 5, 6]]) >>> tf.reduce_min(b, axis=0) >>> tf.reduce_min(b, axis=1) +\`\`\` Setting \`keepdims\` to \`True\` retains the dimension of \`input_tensor\`: - >>> tf.reduce_min(a, keepdims=True) + \`\`\`>>> tf.reduce_min(a, keepdims=True) >>> tf.math.reduce_min(a, axis=0, keepdims=True) +\`\`\` Args: input_tensor: The tensor to reduce. Should have real numeric type. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -5984,7 +5172,7 @@ Examples: For example: - >>> x = tf.constant([[1., 2.], [3., 4.]]) + \`\`\`>>> x = tf.constant([[1., 2.], [3., 4.]]) >>> tf.math.reduce_prod(x) >>> tf.math.reduce_prod(x, 0) @@ -5993,6 +5181,7 @@ Examples: +\`\`\` Args: input_tensor: The tensor to reduce. Should have numeric type. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -6021,7 +5210,7 @@ Examples: For example: - >>> # x has a shape of (2, 3) (two rows and three columns): + \`\`\`>>> # x has a shape of (2, 3) (two rows and three columns): >>> x = tf.constant([[1, 1, 1], [1, 1, 1]]) >>> x.numpy() array([[1, 1, 1], @@ -6050,6 +5239,7 @@ Examples: >>> tf.reduce_sum(x, [0, 1]).numpy() 6 +\`\`\` Args: input_tensor: The tensor to reduce. Should have numeric type. axis: The dimensions to reduce. If \`None\` (the default), reduces all @@ -6085,11 +5275,12 @@ Examples: Example usage: - >>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0) + \`\`\`>>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0) - >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0) +\`\`\` + \`\`\`>>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0) - >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1) +\`\`\` + \`\`\`>>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1) - >>> repeat(3, repeats=4) +\`\`\` + \`\`\`>>> repeat(3, repeats=4) - >>> repeat([[1,2], [3,4]], repeats=2) +\`\`\` + \`\`\`>>> repeat([[1,2], [3,4]], repeats=2) +\`\`\` `},{name:"required_space_to_batch_paddings",path:"./tf/required_space_to_batch_paddings.md",desc:"Calculate padding required to make block_shape divide input_shape.",type:"Functions",docs:`Calculate padding required to make block_shape divide input_shape. This function can be used to calculate a suitable paddings argument for use @@ -6142,7 +5337,7 @@ Examples: values as \`tensor\` in the same order, except with a new shape given by \`shape\`. - >>> t1 = [[1, 2, 3], + \`\`\`>>> t1 = [[1, 2, 3], ... [4, 5, 6]] >>> print(tf.shape(t1).numpy()) [2 3] @@ -6156,20 +5351,22 @@ Examples: [3, 4], [5, 6]], dtype=int32)> +\`\`\` The \`tf.reshape\` does not change the order of or the total number of elements in the tensor, and so it can reuse the underlying data buffer. This makes it a fast operation independent of how big of a tensor it is operating on. - >>> tf.reshape([1, 2, 3], [2, 2]) + \`\`\`>>> tf.reshape([1, 2, 3], [2, 2]) Traceback (most recent call last): ... InvalidArgumentError: Input to reshape is a tensor with 3 values, but the requested shape has 4 +\`\`\` To instead reorder the data to rearrange the dimensions of a tensor, see \`tf.transpose\`. - >>> t = [[1, 2, 3], + \`\`\`>>> t = [[1, 2, 3], ... [4, 5, 6]] >>> tf.reshape(t, [3, 2]).numpy() array([[1, 2], @@ -6180,12 +5377,13 @@ Examples: [2, 5], [3, 6]], dtype=int32) +\`\`\` If one component of \`shape\` is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a \`shape\` of \`[-1]\` flattens into 1-D. At most one component of \`shape\` can be -1. - >>> t = [[1, 2, 3], + \`\`\`>>> t = [[1, 2, 3], ... [4, 5, 6]] >>> tf.reshape(t, [-1]) +\`\`\` \`tf.reshape(t, [])\` reshapes a tensor \`t\` with one element to a scalar. - >>> tf.reshape([7], []).numpy() + \`\`\`>>> tf.reshape([7], []).numpy() 7 +\`\`\` More examples: - >>> t = [1, 2, 3, 4, 5, 6, 7, 8, 9] + \`\`\`>>> t = [1, 2, 3, 4, 5, 6, 7, 8, 9] >>> print(tf.shape(t).numpy()) [9] >>> tf.reshape(t, [3, 3]) @@ -6217,7 +5417,8 @@ Examples: [4, 5, 6], [7, 8, 9]], dtype=int32)> - >>> t = [[[1, 1], [2, 2]], +\`\`\` + \`\`\`>>> t = [[[1, 1], [2, 2]], ... [[3, 3], [4, 4]]] >>> print(tf.shape(t).numpy()) [2 2 2] @@ -6226,7 +5427,8 @@ Examples: array([[1, 1, 2, 2], [3, 3, 4, 4]], dtype=int32)> - >>> t = [[[1, 1, 1], +\`\`\` + \`\`\`>>> t = [[[1, 1, 1], ... [2, 2, 2]], ... [[3, 3, 3], ... [4, 4, 4]], @@ -6260,6 +5462,7 @@ Examples: [5, 5, 5], [6, 6, 6]]], dtype=int32)> +\`\`\` Args: tensor: A \`Tensor\`. shape: A \`Tensor\`. Must be one of the following types: \`int32\`, \`int64\`. @@ -6268,7 +5471,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`tensor\`. - `},{name:"reverse",path:"./tf/reverse.md",desc:"Reverses specific dimensions of a tensor.",type:"Functions",docs:"Reverses specific dimensions of a tensor.\n\n Given a `tensor`, and a `int32` tensor `axis` representing the set of\n dimensions of `tensor` to reverse. This operation reverses each dimension\n `i` for which there exists `j` s.t. `axis[j] == i`.\n\n `tensor` can have up to 8 dimensions. The number of dimensions specified\n in `axis` may be 0 or more entries. If an index is specified more than\n once, a InvalidArgument error is raised.\n\n For example:\n\n ```\n # tensor 't' is [[[[ 0, 1, 2, 3],\n # [ 4, 5, 6, 7],\n # [ 8, 9, 10, 11]],\n # [[12, 13, 14, 15],\n # [16, 17, 18, 19],\n # [20, 21, 22, 23]]]]\n # tensor 't' shape is [1, 2, 3, 4]\n\n # 'dims' is [3] or 'dims' is [-1]\n reverse(t, dims) ==> [[[[ 3, 2, 1, 0],\n [ 7, 6, 5, 4],\n [ 11, 10, 9, 8]],\n [[15, 14, 13, 12],\n [19, 18, 17, 16],\n [23, 22, 21, 20]]]]\n\n # 'dims' is '[1]' (or 'dims' is '[-3]')\n reverse(t, dims) ==> [[[[12, 13, 14, 15],\n [16, 17, 18, 19],\n [20, 21, 22, 23]\n [[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11]]]]\n\n # 'dims' is '[2]' (or 'dims' is '[-2]')\n reverse(t, dims) ==> [[[[8, 9, 10, 11],\n [4, 5, 6, 7],\n [0, 1, 2, 3]]\n [[20, 21, 22, 23],\n [16, 17, 18, 19],\n [12, 13, 14, 15]]]]\n ```\n\n Args:\n tensor: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `uint16`, `int16`, `int32`, `uint32`, `int64`, `uint64`, `bool`, `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`, `string`.\n Up to 8-D.\n axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D. The indices of the dimensions to reverse. Must be in the range\n `[-rank(tensor), rank(tensor))`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n "},{name:"reverse_sequence",path:"./tf/reverse_sequence.md",desc:"Reverses variable length slices.",type:"Functions",docs:"Reverses variable length slices.\n\n This op first slices `input` along the dimension `batch_axis`, and for\n each slice `i`, reverses the first `seq_lengths[i]` elements along the\n dimension `seq_axis`.\n\n The elements of `seq_lengths` must obey `seq_lengths[i] <=\n input.dims[seq_axis]`, and `seq_lengths` must be a vector of length\n `input.dims[batch_axis]`.\n\n The output slice `i` along dimension `batch_axis` is then given by\n input slice `i`, with the first `seq_lengths[i]` slices along\n dimension `seq_axis` reversed.\n\n Example usage:\n\n >>> seq_lengths = [7, 2, 3, 5]\n >>> input = [[1, 2, 3, 4, 5, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0],\n ... [1, 2, 3, 4, 0, 0, 0, 0], [1, 2, 3, 4, 5, 6, 7, 8]]\n >>> output = tf.reverse_sequence(input, seq_lengths, seq_axis=1, batch_axis=0)\n >>> output\n \n\n Args:\n input: A `Tensor`. The input to reverse.\n seq_lengths: A `Tensor`. Must be one of the following types: `int32`,\n `int64`. 1-D with length `input.dims(batch_axis)` and `max(seq_lengths) <=\n input.dims(seq_axis)`\n seq_axis: An `int`. The dimension which is partially reversed.\n batch_axis: An optional `int`. Defaults to `0`. The dimension along which\n reversal is performed.\n name: A name for the operation (optional).\n\n Returns:\n A Tensor. Has the same type as input.\n "},{name:"roll",path:"./tf/roll.md",desc:"Rolls the elements of a tensor along an axis.",type:"Functions",docs:`Rolls the elements of a tensor along an axis. + `},{name:"reverse",path:"./tf/reverse.md",desc:"Reverses specific dimensions of a tensor.",type:"Functions",docs:"Reverses specific dimensions of a tensor.\n\n Given a `tensor`, and a `int32` tensor `axis` representing the set of\n dimensions of `tensor` to reverse. This operation reverses each dimension\n `i` for which there exists `j` s.t. `axis[j] == i`.\n\n `tensor` can have up to 8 dimensions. The number of dimensions specified\n in `axis` may be 0 or more entries. If an index is specified more than\n once, a InvalidArgument error is raised.\n\n For example:\n\n ```\n # tensor 't' is [[[[ 0, 1, 2, 3],\n # [ 4, 5, 6, 7],\n # [ 8, 9, 10, 11]],\n # [[12, 13, 14, 15],\n # [16, 17, 18, 19],\n # [20, 21, 22, 23]]]]\n # tensor 't' shape is [1, 2, 3, 4]\n\n # 'dims' is [3] or 'dims' is [-1]\n reverse(t, dims) ==> [[[[ 3, 2, 1, 0],\n [ 7, 6, 5, 4],\n [ 11, 10, 9, 8]],\n [[15, 14, 13, 12],\n [19, 18, 17, 16],\n [23, 22, 21, 20]]]]\n\n # 'dims' is '[1]' (or 'dims' is '[-3]')\n reverse(t, dims) ==> [[[[12, 13, 14, 15],\n [16, 17, 18, 19],\n [20, 21, 22, 23]\n [[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11]]]]\n\n # 'dims' is '[2]' (or 'dims' is '[-2]')\n reverse(t, dims) ==> [[[[8, 9, 10, 11],\n [4, 5, 6, 7],\n [0, 1, 2, 3]]\n [[20, 21, 22, 23],\n [16, 17, 18, 19],\n [12, 13, 14, 15]]]]\n ```\n\n Args:\n tensor: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `uint16`, `int16`, `int32`, `uint32`, `int64`, `uint64`, `bool`, `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`, `string`.\n Up to 8-D.\n axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D. The indices of the dimensions to reverse. Must be in the range\n `[-rank(tensor), rank(tensor))`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n "},{name:"reverse_sequence",path:"./tf/reverse_sequence.md",desc:"Reverses variable length slices.",type:"Functions",docs:"Reverses variable length slices.\n\n This op first slices `input` along the dimension `batch_axis`, and for\n each slice `i`, reverses the first `seq_lengths[i]` elements along the\n dimension `seq_axis`.\n\n The elements of `seq_lengths` must obey `seq_lengths[i] <=\n input.dims[seq_axis]`, and `seq_lengths` must be a vector of length\n `input.dims[batch_axis]`.\n\n The output slice `i` along dimension `batch_axis` is then given by\n input slice `i`, with the first `seq_lengths[i]` slices along\n dimension `seq_axis` reversed.\n\n Example usage:\n\n ```>>> seq_lengths = [7, 2, 3, 5]\n >>> input = [[1, 2, 3, 4, 5, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0],\n ... [1, 2, 3, 4, 0, 0, 0, 0], [1, 2, 3, 4, 5, 6, 7, 8]]\n >>> output = tf.reverse_sequence(input, seq_lengths, seq_axis=1, batch_axis=0)\n >>> output\n \n\n```\n Args:\n input: A `Tensor`. The input to reverse.\n seq_lengths: A `Tensor`. Must be one of the following types: `int32`,\n `int64`. 1-D with length `input.dims(batch_axis)` and `max(seq_lengths) <=\n input.dims(seq_axis)`\n seq_axis: An `int`. The dimension which is partially reversed.\n batch_axis: An optional `int`. Defaults to `0`. The dimension along which\n reversal is performed.\n name: A name for the operation (optional).\n\n Returns:\n A Tensor. Has the same type as input.\n "},{name:"roll",path:"./tf/roll.md",desc:"Rolls the elements of a tensor along an axis.",type:"Functions",docs:`Rolls the elements of a tensor along an axis. The elements are shifted positively (towards larger indices) by the offset of \`shift\` along the dimension of \`axis\`. Negative \`shift\` values will shift @@ -6307,7 +5510,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`input\`. - `},{name:"round",path:"./tf/math/round.md",desc:"Rounds the values of a tensor to the nearest integer, element-wise.",type:"Functions",docs:"Rounds the values of a tensor to the nearest integer, element-wise.\n\n Rounds half to even. Also known as bankers rounding. If you want to round\n according to the current system rounding mode use tf::cint.\n For example:\n\n ```python\n x = tf.constant([0.9, 2.5, 2.3, 1.5, -4.5])\n tf.round(x) # [ 1.0, 2.0, 2.0, 2.0, -4.0 ]\n ```\n\n Args:\n x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of same shape and type as `x`.\n "},{name:"saturate_cast",path:"./tf/dtypes/saturate_cast.md",desc:"Performs a safe saturating cast of `value` to `dtype`.",type:"Functions",docs:"Performs a safe saturating cast of `value` to `dtype`.\n\n This function casts the input to `dtype` without applying any scaling. If\n there is a danger that values would over or underflow in the cast, this op\n applies the appropriate clamping before the cast.\n\n Args:\n value: A `Tensor`.\n dtype: The desired output `DType`.\n name: A name for the operation (optional).\n\n Returns:\n `value` safely cast to `dtype`.\n "},{name:"scalar_mul",path:"./tf/math/scalar_mul.md",desc:"Multiplies a scalar times a `Tensor` or `IndexedSlices` object.",type:"Functions",docs:"Multiplies a scalar times a `Tensor` or `IndexedSlices` object.\n\n This is a special case of `tf.math.multiply`, where the first value must be a\n `scalar`. Unlike the general form of `tf.math.multiply`, this is operation is\n guaranteed to be efficient for `tf.IndexedSlices`.\n\n >>> x = tf.reshape(tf.range(30, dtype=tf.float32), [10, 3])\n >>> with tf.GradientTape() as g:\n ... g.watch(x)\n ... y = tf.gather(x, [1, 2]) # IndexedSlices\n ... z = tf.math.scalar_mul(10.0, y)\n\n Args:\n scalar: A 0-D scalar `Tensor`. Must have known shape.\n x: A `Tensor` or `IndexedSlices` to be scaled.\n name: A name for the operation (optional).\n\n Returns:\n `scalar * x` of the same type (`Tensor` or `IndexedSlices`) as `x`.\n\n Raises:\n ValueError: if scalar is not a 0-D `scalar`.\n "},{name:"scan",path:"./tf/scan.md",desc:"scan on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values",type:"Functions",docs:"scan on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values)\n\nDeprecated: SOME ARGUMENT VALUES ARE DEPRECATED: `(back_prop=False)`. They will be removed in a future version.\nInstructions for updating:\nback_prop=False is deprecated. Consider using tf.stop_gradient instead.\nInstead of:\nresults = tf.scan(fn, elems, back_prop=False)\nUse:\nresults = tf.nest.map_structure(tf.stop_gradient, tf.scan(fn, elems))\n\nThe simplest version of `scan` repeatedly applies the callable `fn` to a\nsequence of elements from first to last. The elements are made of the tensors\nunpacked from `elems` on dimension 0. The callable fn takes two tensors as\narguments. The first argument is the accumulated value computed from the\npreceding invocation of fn, and the second is the value at the current\nposition of `elems`. If `initializer` is None, `elems` must contain at least\none element, and its first element is used as the initializer.\n\nSuppose that `elems` is unpacked into `values`, a list of tensors. The shape\nof the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.\nIf reverse=True, it's fn(initializer, values[-1]).shape.\n\nThis method also allows multi-arity `elems` and accumulator. If `elems`\nis a (possibly nested) list or tuple of tensors, then each of these tensors\nmust have a matching first (unpack) dimension. The second argument of\n`fn` must match the structure of `elems`.\n\nIf no `initializer` is provided, the output structure and dtypes of `fn`\nare assumed to be the same as its input; and in this case, the first\nargument of `fn` must match the structure of `elems`.\n\nIf an `initializer` is provided, then the output of `fn` must have the same\nstructure as `initializer`; and the first argument of `fn` must match\nthis structure.\n\nFor example, if `elems` is `(t1, [t2, t3])` and `initializer` is\n`[i1, i2]` then an appropriate signature for `fn` in `python2` is:\n`fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list,\n`[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the\n one that works in `python3`, is:\n`fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.\n\nArgs:\n fn: The callable to be performed. It accepts two arguments. The first will\n have the same structure as `initializer` if one is provided, otherwise it\n will have the same structure as `elems`. The second will have the same\n (possibly nested) structure as `elems`. Its output must have the same\n structure as `initializer` if one is provided, otherwise it must have the\n same structure as `elems`.\n elems: A tensor or (possibly nested) sequence of tensors, each of which will\n be unpacked along their first dimension. The nested sequence of the\n resulting slices will be the first argument to `fn`.\n initializer: (optional) A tensor or (possibly nested) sequence of tensors,\n initial value for the accumulator, and the expected output type of `fn`.\n parallel_iterations: (optional) The number of iterations allowed to run in\n parallel.\n back_prop: (optional) Deprecated. False disables support for back\n propagation. Prefer using `tf.stop_gradient` instead.\n swap_memory: (optional) True enables GPU-CPU memory swapping.\n infer_shape: (optional) False disables tests for consistent output shapes.\n reverse: (optional) True scans the tensor last to first (instead of first to\n last).\n name: (optional) Name prefix for the returned tensors.\n\nReturns:\n A tensor or (possibly nested) sequence of tensors. Each tensor packs the\n results of applying `fn` to tensors unpacked from `elems` along the first\n dimension, and the previous accumulator value(s), from first to last (or\n last to first, if `reverse=True`).\n\nRaises:\n TypeError: if `fn` is not callable or the structure of the output of\n `fn` and `initializer` do not match.\n ValueError: if the lengths of the output of `fn` and `initializer`\n do not match.\n\nExamples:\n ```python\n elems = np.array([1, 2, 3, 4, 5, 6])\n sum = scan(lambda a, x: a + x, elems)\n # sum == [1, 3, 6, 10, 15, 21]\n sum = scan(lambda a, x: a + x, elems, reverse=True)\n # sum == [21, 20, 18, 15, 11, 6]\n ```\n\n ```python\n elems = np.array([1, 2, 3, 4, 5, 6])\n initializer = np.array(0)\n sum_one = scan(\n lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)\n # sum_one == [1, 2, 3, 4, 5, 6]\n ```\n\n ```python\n elems = np.array([1, 0, 0, 0, 0, 0])\n initializer = (np.array(0), np.array(1))\n fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)\n # fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])\n ```"},{name:"scatter_nd",path:"./tf/scatter_nd.md",desc:"Scatters `updates` into a tensor of shape `shape` according to `indices`.",type:"Functions",docs:`Scatters \`updates\` into a tensor of shape \`shape\` according to \`indices\`. + `},{name:"round",path:"./tf/math/round.md",desc:"Rounds the values of a tensor to the nearest integer, element-wise.",type:"Functions",docs:"Rounds the values of a tensor to the nearest integer, element-wise.\n\n Rounds half to even. Also known as bankers rounding. If you want to round\n according to the current system rounding mode use tf::cint.\n For example:\n\n ```python\n x = tf.constant([0.9, 2.5, 2.3, 1.5, -4.5])\n tf.round(x) # [ 1.0, 2.0, 2.0, 2.0, -4.0 ]\n ```\n\n Args:\n x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, or `int64`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of same shape and type as `x`.\n "},{name:"saturate_cast",path:"./tf/dtypes/saturate_cast.md",desc:"Performs a safe saturating cast of `value` to `dtype`.",type:"Functions",docs:"Performs a safe saturating cast of `value` to `dtype`.\n\n This function casts the input to `dtype` without applying any scaling. If\n there is a danger that values would over or underflow in the cast, this op\n applies the appropriate clamping before the cast.\n\n Args:\n value: A `Tensor`.\n dtype: The desired output `DType`.\n name: A name for the operation (optional).\n\n Returns:\n `value` safely cast to `dtype`.\n "},{name:"scalar_mul",path:"./tf/math/scalar_mul.md",desc:"Multiplies a scalar times a `Tensor` or `IndexedSlices` object.",type:"Functions",docs:"Multiplies a scalar times a `Tensor` or `IndexedSlices` object.\n\n This is a special case of `tf.math.multiply`, where the first value must be a\n `scalar`. Unlike the general form of `tf.math.multiply`, this is operation is\n guaranteed to be efficient for `tf.IndexedSlices`.\n\n ```>>> x = tf.reshape(tf.range(30, dtype=tf.float32), [10, 3])\n >>> with tf.GradientTape() as g:\n ... g.watch(x)\n ... y = tf.gather(x, [1, 2]) # IndexedSlices\n ... z = tf.math.scalar_mul(10.0, y)\n\n```\n Args:\n scalar: A 0-D scalar `Tensor`. Must have known shape.\n x: A `Tensor` or `IndexedSlices` to be scaled.\n name: A name for the operation (optional).\n\n Returns:\n `scalar * x` of the same type (`Tensor` or `IndexedSlices`) as `x`.\n\n Raises:\n ValueError: if scalar is not a 0-D `scalar`.\n "},{name:"scan",path:"./tf/scan.md",desc:"scan on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values",type:"Functions",docs:"scan on the list of tensors unpacked from `elems` on dimension 0. (deprecated argument values)\n\nDeprecated: SOME ARGUMENT VALUES ARE DEPRECATED: `(back_prop=False)`. They will be removed in a future version.\nInstructions for updating:\nback_prop=False is deprecated. Consider using tf.stop_gradient instead.\nInstead of:\nresults = tf.scan(fn, elems, back_prop=False)\nUse:\nresults = tf.nest.map_structure(tf.stop_gradient, tf.scan(fn, elems))\n\nThe simplest version of `scan` repeatedly applies the callable `fn` to a\nsequence of elements from first to last. The elements are made of the tensors\nunpacked from `elems` on dimension 0. The callable fn takes two tensors as\narguments. The first argument is the accumulated value computed from the\npreceding invocation of fn, and the second is the value at the current\nposition of `elems`. If `initializer` is None, `elems` must contain at least\none element, and its first element is used as the initializer.\n\nSuppose that `elems` is unpacked into `values`, a list of tensors. The shape\nof the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.\nIf reverse=True, it's fn(initializer, values[-1]).shape.\n\nThis method also allows multi-arity `elems` and accumulator. If `elems`\nis a (possibly nested) list or tuple of tensors, then each of these tensors\nmust have a matching first (unpack) dimension. The second argument of\n`fn` must match the structure of `elems`.\n\nIf no `initializer` is provided, the output structure and dtypes of `fn`\nare assumed to be the same as its input; and in this case, the first\nargument of `fn` must match the structure of `elems`.\n\nIf an `initializer` is provided, then the output of `fn` must have the same\nstructure as `initializer`; and the first argument of `fn` must match\nthis structure.\n\nFor example, if `elems` is `(t1, [t2, t3])` and `initializer` is\n`[i1, i2]` then an appropriate signature for `fn` in `python2` is:\n`fn = lambda (acc_p1, acc_p2), (t1, [t2, t3]):` and `fn` must return a list,\n`[acc_n1, acc_n2]`. An alternative correct signature for `fn`, and the\n one that works in `python3`, is:\n`fn = lambda a, t:`, where `a` and `t` correspond to the input tuples.\n\nArgs:\n fn: The callable to be performed. It accepts two arguments. The first will\n have the same structure as `initializer` if one is provided, otherwise it\n will have the same structure as `elems`. The second will have the same\n (possibly nested) structure as `elems`. Its output must have the same\n structure as `initializer` if one is provided, otherwise it must have the\n same structure as `elems`.\n elems: A tensor or (possibly nested) sequence of tensors, each of which will\n be unpacked along their first dimension. The nested sequence of the\n resulting slices will be the first argument to `fn`.\n initializer: (optional) A tensor or (possibly nested) sequence of tensors,\n initial value for the accumulator, and the expected output type of `fn`.\n parallel_iterations: (optional) The number of iterations allowed to run in\n parallel.\n back_prop: (optional) Deprecated. False disables support for back\n propagation. Prefer using `tf.stop_gradient` instead.\n swap_memory: (optional) True enables GPU-CPU memory swapping.\n infer_shape: (optional) False disables tests for consistent output shapes.\n reverse: (optional) True scans the tensor last to first (instead of first to\n last).\n name: (optional) Name prefix for the returned tensors.\n\nReturns:\n A tensor or (possibly nested) sequence of tensors. Each tensor packs the\n results of applying `fn` to tensors unpacked from `elems` along the first\n dimension, and the previous accumulator value(s), from first to last (or\n last to first, if `reverse=True`).\n\nRaises:\n TypeError: if `fn` is not callable or the structure of the output of\n `fn` and `initializer` do not match.\n ValueError: if the lengths of the output of `fn` and `initializer`\n do not match.\n\nExamples:\n ```python\n elems = np.array([1, 2, 3, 4, 5, 6])\n sum = scan(lambda a, x: a + x, elems)\n # sum == [1, 3, 6, 10, 15, 21]\n sum = scan(lambda a, x: a + x, elems, reverse=True)\n # sum == [21, 20, 18, 15, 11, 6]\n ```\n\n ```python\n elems = np.array([1, 2, 3, 4, 5, 6])\n initializer = np.array(0)\n sum_one = scan(\n lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)\n # sum_one == [1, 2, 3, 4, 5, 6]\n ```\n\n ```python\n elems = np.array([1, 0, 0, 0, 0, 0])\n initializer = (np.array(0), np.array(1))\n fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)\n # fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])\n ```"},{name:"scatter_nd",path:"./tf/scatter_nd.md",desc:"Scatters `updates` into a tensor of shape `shape` according to `indices`.",type:"Functions",docs:`Scatters \`updates\` into a tensor of shape \`shape\` according to \`indices\`. Update the input tensor by scattering sparse \`updates\` according to individual values at the specified \`indices\`. This op returns an \`output\` tensor with the \`shape\` you specify. This op is the @@ -6413,26 +5616,28 @@ Examples: **edges** listed in \`sorted_sequence\`. This operation returns the bucket-index for each value. - >>> edges = [-1, 3.3, 9.1, 10.0] + \`\`\`>>> edges = [-1, 3.3, 9.1, 10.0] >>> values = [0.0, 4.1, 12.0] >>> tf.searchsorted(edges, values).numpy() array([1, 2, 4], dtype=int32) +\`\`\` The \`side\` argument controls which index is returned if a value lands exactly on an edge: - >>> seq = [0, 3, 9, 10, 10] + \`\`\`>>> seq = [0, 3, 9, 10, 10] >>> values = [0, 4, 10] >>> tf.searchsorted(seq, values).numpy() array([0, 2, 3], dtype=int32) >>> tf.searchsorted(seq, values, side="right").numpy() array([1, 2, 5], dtype=int32) +\`\`\` The \`axis\` is not settable for this operation. It always operates on the innermost dimension (\`axis=-1\`). The operation will accept any number of outer dimensions. Here it is applied to the rows of a matrix: - >>> sorted_sequence = [[0., 3., 8., 9., 10.], + \`\`\`>>> sorted_sequence = [[0., 3., 8., 9., 10.], ... [1., 2., 3., 4., 5.]] >>> values = [[9.8, 2.1, 4.3], ... [0.1, 6.6, 4.5, ]] @@ -6440,6 +5645,7 @@ Examples: array([[4, 1, 2], [0, 5, 4]], dtype=int32) +\`\`\` Note: This operation assumes that \`sorted_sequence\` **is sorted** along the innermost axis, maybe using \`tf.sort(..., axis=-1)\`. **If the sequence is not sorted no error is raised** and the content of the returned tensor is not well @@ -6496,51 +5702,7 @@ Examples: A mask tensor of shape \`lengths.shape + (maxlen,)\`, cast to specified dtype. Raises: ValueError: if \`maxlen\` is not a scalar. - `},{name:"shape",path:"./tf/shape.md",desc:"Returns a tensor containing the shape of the input tensor.",type:"Functions",docs:`Returns a tensor containing the shape of the input tensor. - - See also \`tf.size\`, \`tf.rank\`. - - \`tf.shape\` returns a 1-D integer tensor representing the shape of \`input\`. - For a scalar input, the tensor returned has a shape of (0,) and its value is - the empty vector (i.e. []). - - For example: - - >>> tf.shape(1.) - - - >>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]) - >>> tf.shape(t) - - - Note: When using symbolic tensors, such as when using the Keras API, - tf.shape() will return the shape of the symbolic tensor. - - >>> a = tf.keras.layers.Input((None, 10)) - >>> tf.shape(a) - <... shape=(3,) dtype=int32...> - - In these cases, using \`tf.Tensor.shape\` will return more informative results. - - >>> a.shape - TensorShape([None, None, 10]) - - (The first \`None\` represents the as yet unknown batch size.) - - \`tf.shape\` and \`Tensor.shape\` should be identical in eager mode. Within - \`tf.function\` or within a \`compat.v1\` context, not all dimensions may be - known until execution time. Hence when defining custom layers and models - for graph mode, prefer the dynamic \`tf.shape(x)\` over the static \`x.shape\`. - - Args: - input: A \`Tensor\` or \`SparseTensor\`. - out_type: (Optional) The specified output type of the operation (\`int32\` or - \`int64\`). Defaults to \`tf.int32\`. - name: A name for the operation (optional). - - Returns: - A \`Tensor\` of type \`out_type\`. - `},{name:"shape_n",path:"./tf/shape_n.md",desc:"Returns shape of tensors.",type:"Functions",docs:"Returns shape of tensors.\n\n Args:\n input: A list of at least 1 `Tensor` object with the same type.\n out_type: The specified output type of the operation (`int32` or `int64`).\n Defaults to `tf.int32`(optional).\n name: A name for the operation (optional).\n\n Returns:\n A list with the same length as `input` of `Tensor` objects with\n type `out_type`.\n "},{name:"sigmoid",path:"./tf/math/sigmoid.md",desc:"Computes sigmoid of `x` element-wise.",type:"Functions",docs:`Computes sigmoid of \`x\` element-wise. + `},{name:"shape",path:"./tf/shape.md",desc:"Returns a tensor containing the shape of the input tensor.",type:"Functions",docs:"Returns a tensor containing the shape of the input tensor.\n\n See also `tf.size`, `tf.rank`.\n\n `tf.shape` returns a 1-D integer tensor representing the shape of `input`.\n For a scalar input, the tensor returned has a shape of (0,) and its value is\n the empty vector (i.e. []).\n\n For example:\n\n ```>>> tf.shape(1.)\n \n\n```\n ```>>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])\n >>> tf.shape(t)\n \n\n```\n Note: When using symbolic tensors, such as when using the Keras API,\n tf.shape() will return the shape of the symbolic tensor.\n\n ```>>> a = tf.keras.layers.Input((None, 10))\n >>> tf.shape(a)\n <... shape=(3,) dtype=int32...>\n\n```\n In these cases, using `tf.Tensor.shape` will return more informative results.\n\n ```>>> a.shape\n TensorShape([None, None, 10])\n\n```\n (The first `None` represents the as yet unknown batch size.)\n\n `tf.shape` and `Tensor.shape` should be identical in eager mode. Within\n `tf.function` or within a `compat.v1` context, not all dimensions may be\n known until execution time. Hence when defining custom layers and models\n for graph mode, prefer the dynamic `tf.shape(x)` over the static `x.shape`.\n\n Args:\n input: A `Tensor` or `SparseTensor`.\n out_type: (Optional) The specified output type of the operation (`int32` or\n `int64`). Defaults to `tf.int32`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` of type `out_type`.\n "},{name:"shape_n",path:"./tf/shape_n.md",desc:"Returns shape of tensors.",type:"Functions",docs:"Returns shape of tensors.\n\n Args:\n input: A list of at least 1 `Tensor` object with the same type.\n out_type: The specified output type of the operation (`int32` or `int64`).\n Defaults to `tf.int32`(optional).\n name: A name for the operation (optional).\n\n Returns:\n A list with the same length as `input` of `Tensor` objects with\n type `out_type`.\n "},{name:"sigmoid",path:"./tf/math/sigmoid.md",desc:"Computes sigmoid of `x` element-wise.",type:"Functions",docs:`Computes sigmoid of \`x\` element-wise. Formula for calculating $\\mathrm{sigmoid}(x) = y = 1 / (1 + \\exp(-x))$. @@ -6551,20 +5713,22 @@ Examples: If a positive number is large, then its sigmoid will approach to 1 since the formula will be \`y = / (1 + )\` - >>> x = tf.constant([0.0, 1.0, 50.0, 100.0]) + \`\`\`>>> x = tf.constant([0.0, 1.0, 50.0, 100.0]) >>> tf.math.sigmoid(x) +\`\`\` If a negative number is large, its sigmoid will approach to 0 since the formula will be \`y = 1 / (1 + )\` - >>> x = tf.constant([-100.0, -50.0, -1.0, 0.0]) + \`\`\`>>> x = tf.constant([-100.0, -50.0, -1.0, 0.0]) >>> tf.math.sigmoid(x) +\`\`\` Args: x: A Tensor with type \`float16\`, \`float32\`, \`float64\`, \`complex64\`, or \`complex128\`. @@ -6575,11 +5739,12 @@ Examples: Usage Example: - >>> x = tf.constant([-128.0, 0.0, 128.0], dtype=tf.float32) + \`\`\`>>> x = tf.constant([-128.0, 0.0, 128.0], dtype=tf.float32) >>> tf.sigmoid(x) +\`\`\` @compatibility(scipy) Equivalent to scipy.special.expit @end_compatibility @@ -6591,16 +5756,18 @@ Examples: Example usage: - >>> # real number + \`\`\`>>> # real number >>> tf.math.sign([0., 2., -3.]) - >>> # complex number +\`\`\` + \`\`\`>>> # complex number >>> tf.math.sign([1 + 1j, 0 + 0j]) +\`\`\` Args: x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128. @@ -6613,46 +5780,23 @@ Examples: tf.math.sign(x.values, ...), x.dense_shape). If \`x\` is a \`SparseTensor\`, returns - \`SparseTensor(x.indices, tf.math.sign(x.values, ...), x.dense_shape)\``},{name:"sin",path:"./tf/math/sin.md",desc:"Computes sine of x element-wise.",type:"Functions",docs:'Computes sine of x element-wise.\n\n Given an input tensor, this function computes sine of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `[-1,1]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])\n tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"sinh",path:"./tf/math/sinh.md",desc:"Computes hyperbolic sine of x element-wise.",type:"Functions",docs:'Computes hyperbolic sine of x element-wise.\n\n Given an input tensor, this function computes hyperbolic sine of every\n element in the tensor. Input range is `[-inf,inf]` and output range\n is `[-inf,inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])\n tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"size",path:"./tf/size.md",desc:"Returns the size of a tensor.",type:"Functions",docs:`Returns the size of a tensor. - - See also \`tf.shape\`. - - Returns a 0-D \`Tensor\` representing the number of elements in \`input\` - of type \`out_type\`. Defaults to tf.int32. - - For example: - - >>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]) - >>> tf.size(t) - - - Args: - input: A \`Tensor\` or \`SparseTensor\`. - name: A name for the operation (optional). - out_type: (Optional) The specified non-quantized numeric output type of the - operation. Defaults to \`tf.int32\`. - - Returns: - A \`Tensor\` of type \`out_type\`. Defaults to \`tf.int32\`. - - @compatibility(numpy) - Equivalent to np.size() - @end_compatibility - `},{name:"slice",path:"./tf/slice.md",desc:"Extracts a slice from a tensor.",type:"Functions",docs:"Extracts a slice from a tensor.\n\n See also `tf.strided_slice`.\n\n This operation extracts a slice of size `size` from a tensor `input_` starting\n at the location specified by `begin`. The slice `size` is represented as a\n tensor shape, where `size[i]` is the number of elements of the 'i'th dimension\n of `input_` that you want to slice. The starting location (`begin`) for the\n slice is represented as an offset in each dimension of `input_`. In other\n words, `begin[i]` is the offset into the i'th dimension of `input_` that you\n want to slice from.\n\n Note that `tf.Tensor.__getitem__` is typically a more pythonic way to\n perform slices, as it allows you to write `foo[3:7, :-2]` instead of\n `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`.\n\n `begin` is zero-based; `size` is one-based. If `size[i]` is -1,\n all remaining elements in dimension i are included in the\n slice. In other words, this is equivalent to setting:\n\n `size[i] = input_.dim_size(i) - begin[i]`\n\n This operation requires that:\n\n `0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]`\n\n For example:\n\n ```python\n t = tf.constant([[[1, 1, 1], [2, 2, 2]],\n [[3, 3, 3], [4, 4, 4]],\n [[5, 5, 5], [6, 6, 6]]])\n tf.slice(t, [1, 0, 0], [1, 1, 3]) # [[[3, 3, 3]]]\n tf.slice(t, [1, 0, 0], [1, 2, 3]) # [[[3, 3, 3],\n # [4, 4, 4]]]\n tf.slice(t, [1, 0, 0], [2, 1, 3]) # [[[3, 3, 3]],\n # [[5, 5, 5]]]\n ```\n\n Args:\n input_: A `Tensor`.\n begin: An `int32` or `int64` `Tensor`.\n size: An `int32` or `int64` `Tensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` the same type as `input_`.\n "},{name:"sort",path:"./tf/sort.md",desc:"Sorts a tensor.",type:"Functions",docs:`Sorts a tensor. + \`SparseTensor(x.indices, tf.math.sign(x.values, ...), x.dense_shape)\``},{name:"sin",path:"./tf/math/sin.md",desc:"Computes sine of x element-wise.",type:"Functions",docs:'Computes sine of x element-wise.\n\n Given an input tensor, this function computes sine of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `[-1,1]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])\n tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"sinh",path:"./tf/math/sinh.md",desc:"Computes hyperbolic sine of x element-wise.",type:"Functions",docs:'Computes hyperbolic sine of x element-wise.\n\n Given an input tensor, this function computes hyperbolic sine of every\n element in the tensor. Input range is `[-inf,inf]` and output range\n is `[-inf,inf]`.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])\n tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"size",path:"./tf/size.md",desc:"Returns the size of a tensor.",type:"Functions",docs:"Returns the size of a tensor.\n\n See also `tf.shape`.\n\n Returns a 0-D `Tensor` representing the number of elements in `input`\n of type `out_type`. Defaults to tf.int32.\n\n For example:\n\n ```>>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])\n >>> tf.size(t)\n \n\n```\n Args:\n input: A `Tensor` or `SparseTensor`.\n name: A name for the operation (optional).\n out_type: (Optional) The specified non-quantized numeric output type of the\n operation. Defaults to `tf.int32`.\n\n Returns:\n A `Tensor` of type `out_type`. Defaults to `tf.int32`.\n\n @compatibility(numpy)\n Equivalent to np.size()\n @end_compatibility\n "},{name:"slice",path:"./tf/slice.md",desc:"Extracts a slice from a tensor.",type:"Functions",docs:"Extracts a slice from a tensor.\n\n See also `tf.strided_slice`.\n\n This operation extracts a slice of size `size` from a tensor `input_` starting\n at the location specified by `begin`. The slice `size` is represented as a\n tensor shape, where `size[i]` is the number of elements of the 'i'th dimension\n of `input_` that you want to slice. The starting location (`begin`) for the\n slice is represented as an offset in each dimension of `input_`. In other\n words, `begin[i]` is the offset into the i'th dimension of `input_` that you\n want to slice from.\n\n Note that `tf.Tensor.__getitem__` is typically a more pythonic way to\n perform slices, as it allows you to write `foo[3:7, :-2]` instead of\n `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`.\n\n `begin` is zero-based; `size` is one-based. If `size[i]` is -1,\n all remaining elements in dimension i are included in the\n slice. In other words, this is equivalent to setting:\n\n `size[i] = input_.dim_size(i) - begin[i]`\n\n This operation requires that:\n\n `0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]`\n\n For example:\n\n ```python\n t = tf.constant([[[1, 1, 1], [2, 2, 2]],\n [[3, 3, 3], [4, 4, 4]],\n [[5, 5, 5], [6, 6, 6]]])\n tf.slice(t, [1, 0, 0], [1, 1, 3]) # [[[3, 3, 3]]]\n tf.slice(t, [1, 0, 0], [1, 2, 3]) # [[[3, 3, 3],\n # [4, 4, 4]]]\n tf.slice(t, [1, 0, 0], [2, 1, 3]) # [[[3, 3, 3]],\n # [[5, 5, 5]]]\n ```\n\n Args:\n input_: A `Tensor`.\n begin: An `int32` or `int64` `Tensor`.\n size: An `int32` or `int64` `Tensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor` the same type as `input_`.\n "},{name:"sort",path:"./tf/sort.md",desc:"Sorts a tensor.",type:"Functions",docs:`Sorts a tensor. Usage: - >>> a = [1, 10, 26.9, 2.8, 166.32, 62.3] + \`\`\`>>> a = [1, 10, 26.9, 2.8, 166.32, 62.3] >>> tf.sort(a).numpy() array([ 1. , 2.8 , 10. , 26.9 , 62.3 , 166.32], dtype=float32) - >>> tf.sort(a, direction='DESCENDING').numpy() +\`\`\` + \`\`\`>>> tf.sort(a, direction='DESCENDING').numpy() array([166.32, 62.3 , 26.9 , 10. , 2.8 , 1. ], dtype=float32) +\`\`\` For multidimensional inputs you can control which axis the sort is applied along. The default \`axis=-1\` sorts the innermost axis. - >>> mat = [[3,2,1], + \`\`\`>>> mat = [[3,2,1], ... [2,1,3], ... [1,3,2]] >>> tf.sort(mat, axis=-1).numpy() @@ -6664,6 +5808,7 @@ Examples: [2, 2, 2], [3, 3, 3]], dtype=int32) +\`\`\` See also: * \`tf.argsort\`: Like sort, but it returns the sort indices. @@ -6687,39 +5832,7 @@ Examples: tf.errors.InvalidArgumentError: If the \`values.dtype\` is not a \`float\` or \`int\` type. ValueError: If axis is not a constant scalar, or the direction is invalid. - `},{name:"space_to_batch",path:"./tf/space_to_batch.md",desc:"SpaceToBatch for N-D tensors of type T.",type:"Functions",docs:'SpaceToBatch for N-D tensors of type T.\n\n This operation divides "spatial" dimensions `[1, ..., M]` of the input into a\n grid of blocks of shape `block_shape`, and interleaves these blocks with the\n "batch" dimension (0) such that in the output, the spatial dimensions\n `[1, ..., M]` correspond to the position within the grid, and the batch\n dimension combines both the position within a spatial block and the original\n batch position. Prior to division into blocks, the spatial dimensions of the\n input are optionally zero padded according to `paddings`. See below for a\n precise description.\n\n This operation is equivalent to the following steps:\n\n 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the\n input according to `paddings` to produce `padded` of shape `padded_shape`.\n\n 2. Reshape `padded` to `reshaped_padded` of shape:\n\n [batch] +\n [padded_shape[1] / block_shape[0],\n block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1],\n block_shape[M-1]] +\n remaining_shape\n\n 3. Permute dimensions of `reshaped_padded` to produce\n `permuted_reshaped_padded` of shape:\n\n block_shape +\n [batch] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch\n dimension, producing an output tensor of shape:\n\n [batch * prod(block_shape)] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n Some examples:\n\n (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2]], [[3], [4]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 1]` and value:\n\n ```\n [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]\n ```\n\n (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1, 2, 3], [4, 5, 6]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 3]` and value:\n\n ```\n [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]\n ```\n\n (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[4, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [3]], [[9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and\n paddings = `[[0, 0], [2, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]]],\n [[[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[8, 1, 3, 1]` and value:\n\n ```\n x = [[[[0], [1], [3]]], [[[0], [9], [11]]],\n [[[0], [2], [4]]], [[[0], [10], [12]]],\n [[[0], [5], [7]]], [[[0], [13], [15]]],\n [[[0], [6], [8]]], [[[0], [14], [16]]]]\n ```\n\n Among others, this operation is useful for reducing atrous convolution into\n regular convolution.\n\n Args:\n input: A `Tensor`.\n N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,\n where spatial_shape has `M` dimensions.\n block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D with shape `[M]`, all values must be >= 1.\n paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 2-D with shape `[M, 2]`, all values must be >= 0.\n `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension\n `i + 1`, which corresponds to spatial dimension `i`. It is required that\n `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"space_to_batch_nd",path:"./tf/space_to_batch_nd.md",desc:"SpaceToBatch for N-D tensors of type T.",type:"Functions",docs:'SpaceToBatch for N-D tensors of type T.\n\n This operation divides "spatial" dimensions `[1, ..., M]` of the input into a\n grid of blocks of shape `block_shape`, and interleaves these blocks with the\n "batch" dimension (0) such that in the output, the spatial dimensions\n `[1, ..., M]` correspond to the position within the grid, and the batch\n dimension combines both the position within a spatial block and the original\n batch position. Prior to division into blocks, the spatial dimensions of the\n input are optionally zero padded according to `paddings`. See below for a\n precise description.\n\n This operation is equivalent to the following steps:\n\n 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the\n input according to `paddings` to produce `padded` of shape `padded_shape`.\n\n 2. Reshape `padded` to `reshaped_padded` of shape:\n\n [batch] +\n [padded_shape[1] / block_shape[0],\n block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1],\n block_shape[M-1]] +\n remaining_shape\n\n 3. Permute dimensions of `reshaped_padded` to produce\n `permuted_reshaped_padded` of shape:\n\n block_shape +\n [batch] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch\n dimension, producing an output tensor of shape:\n\n [batch * prod(block_shape)] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n Some examples:\n\n (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2]], [[3], [4]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 1]` and value:\n\n ```\n [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]\n ```\n\n (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1, 2, 3], [4, 5, 6]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 3]` and value:\n\n ```\n [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]\n ```\n\n (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[4, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [3]], [[9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and\n paddings = `[[0, 0], [2, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]]],\n [[[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[8, 1, 3, 1]` and value:\n\n ```\n x = [[[[0], [1], [3]]], [[[0], [9], [11]]],\n [[[0], [2], [4]]], [[[0], [10], [12]]],\n [[[0], [5], [7]]], [[[0], [13], [15]]],\n [[[0], [6], [8]]], [[[0], [14], [16]]]]\n ```\n\n Among others, this operation is useful for reducing atrous convolution into\n regular convolution.\n\n Args:\n input: A `Tensor`.\n N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,\n where spatial_shape has `M` dimensions.\n block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D with shape `[M]`, all values must be >= 1.\n paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 2-D with shape `[M, 2]`, all values must be >= 0.\n `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension\n `i + 1`, which corresponds to spatial dimension `i`. It is required that\n `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"split",path:"./tf/split.md",desc:"Splits a tensor `value` into a list of sub tensors.",type:"Functions",docs:"Splits a tensor `value` into a list of sub tensors.\n\n See also `tf.unstack`.\n\n If `num_or_size_splits` is an `int`, then it splits `value` along the\n dimension `axis` into `num_or_size_splits` smaller tensors. This requires that\n `value.shape[axis]` is divisible by `num_or_size_splits`.\n\n If `num_or_size_splits` is a 1-D Tensor (or list), then `value` is split into\n `len(num_or_size_splits)` elements. The shape of the `i`-th\n element has the same size as the `value` except along dimension `axis` where\n the size is `num_or_size_splits[i]`.\n\n For example:\n\n >>> x = tf.Variable(tf.random.uniform([5, 30], -1, 1))\n >>>\n >>> # Split `x` into 3 tensors along dimension 1\n >>> s0, s1, s2 = tf.split(x, num_or_size_splits=3, axis=1)\n >>> tf.shape(s0).numpy()\n array([ 5, 10], dtype=int32)\n >>>\n >>> # Split `x` into 3 tensors with sizes [4, 15, 11] along dimension 1\n >>> split0, split1, split2 = tf.split(x, [4, 15, 11], 1)\n >>> tf.shape(split0).numpy()\n array([5, 4], dtype=int32)\n >>> tf.shape(split1).numpy()\n array([ 5, 15], dtype=int32)\n >>> tf.shape(split2).numpy()\n array([ 5, 11], dtype=int32)\n\n Args:\n value: The `Tensor` to split.\n num_or_size_splits: Either an `int` indicating the number of splits\n along `axis` or a 1-D integer `Tensor` or Python list containing the sizes\n of each output tensor along `axis`. If an `int`, then it must evenly\n divide `value.shape[axis]`; otherwise the sum of sizes along the split\n axis must match that of the `value`.\n axis: An `int` or scalar `int32` `Tensor`. The dimension along which\n to split. Must be in the range `[-rank(value), rank(value))`. Defaults to\n 0.\n num: Optional, an `int`, used to specify the number of outputs when it\n cannot be inferred from the shape of `size_splits`.\n name: A name for the operation (optional).\n\n Returns:\n if `num_or_size_splits` is an `int` returns a list of\n `num_or_size_splits` `Tensor` objects; if `num_or_size_splits` is a 1-D\n list or 1-D `Tensor` returns `num_or_size_splits.get_shape[0]`\n `Tensor` objects resulting from splitting `value`.\n\n Raises:\n ValueError: If `num` is unspecified and cannot be inferred.\n ValueError: If `num_or_size_splits` is a scalar `Tensor`.\n "},{name:"sqrt",path:"./tf/math/sqrt.md",desc:"Computes element-wise square root of the input tensor.",type:"Functions",docs:`Computes element-wise square root of the input tensor. - - Note: This operation does not support integer types. - - >>> x = tf.constant([[4.0], [16.0]]) - >>> tf.sqrt(x) - - >>> y = tf.constant([[-4.0], [16.0]]) - >>> tf.sqrt(y) - - >>> z = tf.constant([[-1.0], [16.0]], dtype=tf.complex128) - >>> tf.sqrt(z) - - - Note: In order to support complex type, please provide an input tensor - of \`complex64\` or \`complex128\`. - - Args: - x: A \`tf.Tensor\` of type \`bfloat16\`, \`half\`, \`float32\`, \`float64\`, - \`complex64\`, \`complex128\` - name: A name for the operation (optional). - - Returns: - A \`tf.Tensor\` of same size, type and sparsity as \`x\`. - - If \`x\` is a \`SparseTensor\`, returns - \`SparseTensor(x.indices, tf.math.sqrt(x.values, ...), x.dense_shape)\``},{name:"square",path:"./tf/math/square.md",desc:"Computes square of x element-wise.",type:"Functions",docs:"Computes square of x element-wise.\n\n I.e., \\\\(y = x * x = x^2\\\\).\n\n >>> tf.math.square([-2., 0., 3.])\n \n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.square(x.values, ...), x.dense_shape)`"},{name:"squeeze",path:"./tf/squeeze.md",desc:"Removes dimensions of size 1 from the shape of a tensor.",type:"Functions",docs:"Removes dimensions of size 1 from the shape of a tensor.\n\n Given a tensor `input`, this operation returns a tensor of the same type with\n all dimensions of size 1 removed. If you don't want to remove all size 1\n dimensions, you can remove specific size 1 dimensions by specifying\n `axis`.\n\n For example:\n\n ```python\n # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]\n tf.shape(tf.squeeze(t)) # [2, 3]\n ```\n\n Or, to remove specific size 1 dimensions:\n\n ```python\n # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]\n tf.shape(tf.squeeze(t, [2, 4])) # [1, 2, 3, 1]\n ```\n\n Unlike the older op `tf.compat.v1.squeeze`, this op does not accept a\n deprecated `squeeze_dims` argument.\n\n Note: if `input` is a `tf.RaggedTensor`, then this operation takes `O(N)`\n time, where `N` is the number of elements in the squeezed dimensions.\n\n Args:\n input: A `Tensor`. The `input` to squeeze.\n axis: An optional list of `ints`. Defaults to `[]`. If specified, only\n squeezes the dimensions listed. The dimension index starts at 0. It is an\n error to squeeze a dimension that is not 1. Must be in the range\n `[-rank(input), rank(input))`. Must be specified if `input` is a\n `RaggedTensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n Contains the same data as `input`, but has one or more dimensions of\n size 1 removed.\n\n Raises:\n ValueError: The input cannot be converted to a tensor, or the specified\n axis cannot be squeezed.\n "},{name:"stack",path:"./tf/stack.md",desc:"Stacks a list of rank-`R` tensors into one rank-`(R+1",type:"Functions",docs:"Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.\n\n See also `tf.concat`, `tf.tile`, `tf.repeat`.\n\n Packs the list of tensors in `values` into a tensor with rank one higher than\n each tensor in `values`, by packing them along the `axis` dimension.\n Given a list of length `N` of tensors of shape `(A, B, C)`;\n\n if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.\n if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.\n Etc.\n\n For example:\n\n >>> x = tf.constant([1, 4])\n >>> y = tf.constant([2, 5])\n >>> z = tf.constant([3, 6])\n >>> tf.stack([x, y, z])\n \n >>> tf.stack([x, y, z], axis=1)\n \n\n This is the opposite of unstack. The numpy equivalent is `np.stack`\n\n >>> np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))\n True\n\n Args:\n values: A list of `Tensor` objects with the same shape and type.\n axis: An `int`. The axis to stack along. Defaults to the first dimension.\n Negative values wrap around, so the valid range is `[-(R+1), R+1)`.\n name: A name for this operation (optional).\n\n Returns:\n output: A stacked `Tensor` with the same type as `values`.\n\n Raises:\n ValueError: If `axis` is out of the range [-(R+1), R+1).\n "},{name:"stop_gradient",path:"./tf/stop_gradient.md",desc:"Stops gradient computation.",type:"Functions",docs:`Stops gradient computation. + `},{name:"space_to_batch",path:"./tf/space_to_batch.md",desc:"SpaceToBatch for N-D tensors of type T.",type:"Functions",docs:'SpaceToBatch for N-D tensors of type T.\n\n This operation divides "spatial" dimensions `[1, ..., M]` of the input into a\n grid of blocks of shape `block_shape`, and interleaves these blocks with the\n "batch" dimension (0) such that in the output, the spatial dimensions\n `[1, ..., M]` correspond to the position within the grid, and the batch\n dimension combines both the position within a spatial block and the original\n batch position. Prior to division into blocks, the spatial dimensions of the\n input are optionally zero padded according to `paddings`. See below for a\n precise description.\n\n This operation is equivalent to the following steps:\n\n 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the\n input according to `paddings` to produce `padded` of shape `padded_shape`.\n\n 2. Reshape `padded` to `reshaped_padded` of shape:\n\n [batch] +\n [padded_shape[1] / block_shape[0],\n block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1],\n block_shape[M-1]] +\n remaining_shape\n\n 3. Permute dimensions of `reshaped_padded` to produce\n `permuted_reshaped_padded` of shape:\n\n block_shape +\n [batch] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch\n dimension, producing an output tensor of shape:\n\n [batch * prod(block_shape)] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n Some examples:\n\n (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2]], [[3], [4]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 1]` and value:\n\n ```\n [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]\n ```\n\n (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1, 2, 3], [4, 5, 6]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 3]` and value:\n\n ```\n [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]\n ```\n\n (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[4, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [3]], [[9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and\n paddings = `[[0, 0], [2, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]]],\n [[[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[8, 1, 3, 1]` and value:\n\n ```\n x = [[[[0], [1], [3]]], [[[0], [9], [11]]],\n [[[0], [2], [4]]], [[[0], [10], [12]]],\n [[[0], [5], [7]]], [[[0], [13], [15]]],\n [[[0], [6], [8]]], [[[0], [14], [16]]]]\n ```\n\n Among others, this operation is useful for reducing atrous convolution into\n regular convolution.\n\n Args:\n input: A `Tensor`.\n N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,\n where spatial_shape has `M` dimensions.\n block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D with shape `[M]`, all values must be >= 1.\n paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 2-D with shape `[M, 2]`, all values must be >= 0.\n `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension\n `i + 1`, which corresponds to spatial dimension `i`. It is required that\n `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"space_to_batch_nd",path:"./tf/space_to_batch_nd.md",desc:"SpaceToBatch for N-D tensors of type T.",type:"Functions",docs:'SpaceToBatch for N-D tensors of type T.\n\n This operation divides "spatial" dimensions `[1, ..., M]` of the input into a\n grid of blocks of shape `block_shape`, and interleaves these blocks with the\n "batch" dimension (0) such that in the output, the spatial dimensions\n `[1, ..., M]` correspond to the position within the grid, and the batch\n dimension combines both the position within a spatial block and the original\n batch position. Prior to division into blocks, the spatial dimensions of the\n input are optionally zero padded according to `paddings`. See below for a\n precise description.\n\n This operation is equivalent to the following steps:\n\n 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the\n input according to `paddings` to produce `padded` of shape `padded_shape`.\n\n 2. Reshape `padded` to `reshaped_padded` of shape:\n\n [batch] +\n [padded_shape[1] / block_shape[0],\n block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1],\n block_shape[M-1]] +\n remaining_shape\n\n 3. Permute dimensions of `reshaped_padded` to produce\n `permuted_reshaped_padded` of shape:\n\n block_shape +\n [batch] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch\n dimension, producing an output tensor of shape:\n\n [batch * prod(block_shape)] +\n [padded_shape[1] / block_shape[0],\n ...,\n padded_shape[M] / block_shape[M-1]] +\n remaining_shape\n\n Some examples:\n\n (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2]], [[3], [4]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 1]` and value:\n\n ```\n [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]\n ```\n\n (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1, 2, 3], [4, 5, 6]],\n [[7, 8, 9], [10, 11, 12]]]]\n ```\n\n The output tensor has shape `[4, 1, 1, 3]` and value:\n\n ```\n [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]\n ```\n\n (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and\n `paddings = [[0, 0], [0, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[4, 2, 2, 1]` and value:\n\n ```\n x = [[[[1], [3]], [[9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n ```\n\n (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and\n paddings = `[[0, 0], [2, 0]]`:\n\n ```\n x = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]]],\n [[[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n ```\n\n The output tensor has shape `[8, 1, 3, 1]` and value:\n\n ```\n x = [[[[0], [1], [3]]], [[[0], [9], [11]]],\n [[[0], [2], [4]]], [[[0], [10], [12]]],\n [[[0], [5], [7]]], [[[0], [13], [15]]],\n [[[0], [6], [8]]], [[[0], [14], [16]]]]\n ```\n\n Among others, this operation is useful for reducing atrous convolution into\n regular convolution.\n\n Args:\n input: A `Tensor`.\n N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,\n where spatial_shape has `M` dimensions.\n block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 1-D with shape `[M]`, all values must be >= 1.\n paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n 2-D with shape `[M, 2]`, all values must be >= 0.\n `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension\n `i + 1`, which corresponds to spatial dimension `i`. It is required that\n `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n '},{name:"split",path:"./tf/split.md",desc:"Splits a tensor `value` into a list of sub tensors.",type:"Functions",docs:"Splits a tensor `value` into a list of sub tensors.\n\n See also `tf.unstack`.\n\n If `num_or_size_splits` is an `int`, then it splits `value` along the\n dimension `axis` into `num_or_size_splits` smaller tensors. This requires that\n `value.shape[axis]` is divisible by `num_or_size_splits`.\n\n If `num_or_size_splits` is a 1-D Tensor (or list), then `value` is split into\n `len(num_or_size_splits)` elements. The shape of the `i`-th\n element has the same size as the `value` except along dimension `axis` where\n the size is `num_or_size_splits[i]`.\n\n For example:\n\n ```>>> x = tf.Variable(tf.random.uniform([5, 30], -1, 1))\n >>>\n >>> # Split `x` into 3 tensors along dimension 1\n >>> s0, s1, s2 = tf.split(x, num_or_size_splits=3, axis=1)\n >>> tf.shape(s0).numpy()\n array([ 5, 10], dtype=int32)\n >>>\n >>> # Split `x` into 3 tensors with sizes [4, 15, 11] along dimension 1\n >>> split0, split1, split2 = tf.split(x, [4, 15, 11], 1)\n >>> tf.shape(split0).numpy()\n array([5, 4], dtype=int32)\n >>> tf.shape(split1).numpy()\n array([ 5, 15], dtype=int32)\n >>> tf.shape(split2).numpy()\n array([ 5, 11], dtype=int32)\n\n```\n Args:\n value: The `Tensor` to split.\n num_or_size_splits: Either an `int` indicating the number of splits\n along `axis` or a 1-D integer `Tensor` or Python list containing the sizes\n of each output tensor along `axis`. If an `int`, then it must evenly\n divide `value.shape[axis]`; otherwise the sum of sizes along the split\n axis must match that of the `value`.\n axis: An `int` or scalar `int32` `Tensor`. The dimension along which\n to split. Must be in the range `[-rank(value), rank(value))`. Defaults to\n 0.\n num: Optional, an `int`, used to specify the number of outputs when it\n cannot be inferred from the shape of `size_splits`.\n name: A name for the operation (optional).\n\n Returns:\n if `num_or_size_splits` is an `int` returns a list of\n `num_or_size_splits` `Tensor` objects; if `num_or_size_splits` is a 1-D\n list or 1-D `Tensor` returns `num_or_size_splits.get_shape[0]`\n `Tensor` objects resulting from splitting `value`.\n\n Raises:\n ValueError: If `num` is unspecified and cannot be inferred.\n ValueError: If `num_or_size_splits` is a scalar `Tensor`.\n "},{name:"sqrt",path:"./tf/math/sqrt.md",desc:"Computes element-wise square root of the input tensor.",type:"Functions",docs:"Computes element-wise square root of the input tensor.\n\n Note: This operation does not support integer types.\n\n ```>>> x = tf.constant([[4.0], [16.0]])\n >>> tf.sqrt(x)\n \n >>> y = tf.constant([[-4.0], [16.0]])\n >>> tf.sqrt(y)\n \n >>> z = tf.constant([[-1.0], [16.0]], dtype=tf.complex128)\n >>> tf.sqrt(z)\n \n\n```\n Note: In order to support complex type, please provide an input tensor\n of `complex64` or `complex128`.\n\n Args:\n x: A `tf.Tensor` of type `bfloat16`, `half`, `float32`, `float64`,\n `complex64`, `complex128`\n name: A name for the operation (optional).\n\n Returns:\n A `tf.Tensor` of same size, type and sparsity as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.sqrt(x.values, ...), x.dense_shape)`"},{name:"square",path:"./tf/math/square.md",desc:"Computes square of x element-wise.",type:"Functions",docs:"Computes square of x element-wise.\n\n I.e., \\\\(y = x * x = x^2\\\\).\n\n ```>>> tf.math.square([-2., 0., 3.])\n \n\n```\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.square(x.values, ...), x.dense_shape)`"},{name:"squeeze",path:"./tf/squeeze.md",desc:"Removes dimensions of size 1 from the shape of a tensor.",type:"Functions",docs:"Removes dimensions of size 1 from the shape of a tensor.\n\n Given a tensor `input`, this operation returns a tensor of the same type with\n all dimensions of size 1 removed. If you don't want to remove all size 1\n dimensions, you can remove specific size 1 dimensions by specifying\n `axis`.\n\n For example:\n\n ```python\n # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]\n tf.shape(tf.squeeze(t)) # [2, 3]\n ```\n\n Or, to remove specific size 1 dimensions:\n\n ```python\n # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]\n tf.shape(tf.squeeze(t, [2, 4])) # [1, 2, 3, 1]\n ```\n\n Unlike the older op `tf.compat.v1.squeeze`, this op does not accept a\n deprecated `squeeze_dims` argument.\n\n Note: if `input` is a `tf.RaggedTensor`, then this operation takes `O(N)`\n time, where `N` is the number of elements in the squeezed dimensions.\n\n Args:\n input: A `Tensor`. The `input` to squeeze.\n axis: An optional list of `ints`. Defaults to `[]`. If specified, only\n squeezes the dimensions listed. The dimension index starts at 0. It is an\n error to squeeze a dimension that is not 1. Must be in the range\n `[-rank(input), rank(input))`. Must be specified if `input` is a\n `RaggedTensor`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `input`.\n Contains the same data as `input`, but has one or more dimensions of\n size 1 removed.\n\n Raises:\n ValueError: The input cannot be converted to a tensor, or the specified\n axis cannot be squeezed.\n "},{name:"stack",path:"./tf/stack.md",desc:"Stacks a list of rank-`R` tensors into one rank-`(R+1",type:"Functions",docs:"Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.\n\n See also `tf.concat`, `tf.tile`, `tf.repeat`.\n\n Packs the list of tensors in `values` into a tensor with rank one higher than\n each tensor in `values`, by packing them along the `axis` dimension.\n Given a list of length `N` of tensors of shape `(A, B, C)`;\n\n if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.\n if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.\n Etc.\n\n For example:\n\n ```>>> x = tf.constant([1, 4])\n >>> y = tf.constant([2, 5])\n >>> z = tf.constant([3, 6])\n >>> tf.stack([x, y, z])\n \n >>> tf.stack([x, y, z], axis=1)\n \n\n```\n This is the opposite of unstack. The numpy equivalent is `np.stack`\n\n ```>>> np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))\n True\n\n```\n Args:\n values: A list of `Tensor` objects with the same shape and type.\n axis: An `int`. The axis to stack along. Defaults to the first dimension.\n Negative values wrap around, so the valid range is `[-(R+1), R+1)`.\n name: A name for this operation (optional).\n\n Returns:\n output: A stacked `Tensor` with the same type as `values`.\n\n Raises:\n ValueError: If `axis` is out of the range [-(R+1), R+1).\n "},{name:"stop_gradient",path:"./tf/stop_gradient.md",desc:"Stops gradient computation.",type:"Functions",docs:`Stops gradient computation. When executed in a graph, this op outputs its input tensor as-is. @@ -6796,7 +5909,7 @@ Examples: Subtract operation between an array and a scalar: - >>> x = [1, 2, 3, 4, 5] + \`\`\`>>> x = [1, 2, 3, 4, 5] >>> y = 1 >>> tf.subtract(x, y) @@ -6804,21 +5917,24 @@ Examples: +\`\`\` Note that binary \`-\` operator can be used instead: - >>> x = tf.convert_to_tensor([1, 2, 3, 4, 5]) + \`\`\`>>> x = tf.convert_to_tensor([1, 2, 3, 4, 5]) >>> y = tf.convert_to_tensor(1) >>> x - y +\`\`\` Subtract operation between an array and a tensor of same shape: - >>> x = [1, 2, 3, 4, 5] + \`\`\`>>> x = [1, 2, 3, 4, 5] >>> y = tf.constant([5, 4, 3, 2, 1]) >>> tf.subtract(y, x) +\`\`\` **Warning**: If one of the inputs (\`x\` or \`y\`) is a tensor and the other is a non-tensor, the non-tensor input will adopt (or get casted to) the data type of the tensor input. This can potentially cause unwanted overflow or underflow @@ -6826,11 +5942,12 @@ Examples: For example, - >>> x = tf.constant([1, 2], dtype=tf.int8) + \`\`\`>>> x = tf.constant([1, 2], dtype=tf.int8) >>> y = [2**8 + 1, 2**8 + 2] >>> tf.subtract(x, y) +\`\`\` When subtracting two input values of different shapes, \`tf.subtract\` follows the [general broadcasting rules](https://numpy.org/doc/stable/user/basics.broadcasting.html#general-broadcasting-rules) . The two input array shapes are compared element-wise. Starting with the @@ -6839,7 +5956,7 @@ Examples: For example, - >>> x = np.ones(6).reshape(2, 3, 1) + \`\`\`>>> x = np.ones(6).reshape(2, 3, 1) >>> y = np.ones(6).reshape(2, 1, 3) >>> tf.subtract(x, y) +\`\`\` Example with inputs of different dimensions: - >>> x = np.ones(6).reshape(2, 3, 1) + \`\`\`>>> x = np.ones(6).reshape(2, 3, 1) >>> y = np.ones(6).reshape(1, 6) >>> tf.subtract(x, y) +\`\`\` Args: x: A \`Tensor\`. Must be one of the following types: \`bfloat16\`, \`half\`, \`float32\`, \`float64\`, \`uint8\`, \`int8\`, \`uint16\`, \`int16\`, \`int32\`, \`int64\`, \`complex64\`, \`complex128\`, \`uint32\`, \`uint64\`. y: A \`Tensor\`. Must have the same type as \`x\`. @@ -6870,7 +5989,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`x\`. - `},{name:"switch_case",path:"./tf/switch_case.md",desc:"Create a switch/case operation, i.e. an integer-indexed conditional.",type:"Functions",docs:"Create a switch/case operation, i.e. an integer-indexed conditional.\n\n See also `tf.case`.\n\n This op can be substantially more efficient than `tf.case` when exactly one\n branch will be selected. `tf.switch_case` is more like a C++ switch/case\n statement than `tf.case`, which is more like an if/elif/elif/else chain.\n\n The `branch_fns` parameter is either a dict from `int` to callables, or list\n of (`int`, callable) pairs, or simply a list of callables (in which case the\n index is implicitly the key). The `branch_index` `Tensor` is used to select an\n element in `branch_fns` with matching `int` key, falling back to `default`\n if none match, or `max(keys)` if no `default` is provided. The keys must form\n a contiguous set from `0` to `len(branch_fns) - 1`.\n\n `tf.switch_case` supports nested structures as implemented in `tf.nest`. All\n callables must return the same (possibly nested) value structure of lists,\n tuples, and/or named tuples.\n\n **Example:**\n\n Pseudocode:\n\n ```c++\n switch (branch_index) { // c-style switch\n case 0: return 17;\n case 1: return 31;\n default: return -1;\n }\n ```\n or\n ```python\n branches = {0: lambda: 17, 1: lambda: 31}\n branches.get(branch_index, lambda: -1)()\n ```\n\n Expressions:\n\n ```python\n def f1(): return tf.constant(17)\n def f2(): return tf.constant(31)\n def f3(): return tf.constant(-1)\n r = tf.switch_case(branch_index, branch_fns={0: f1, 1: f2}, default=f3)\n # Equivalent: tf.switch_case(branch_index, branch_fns={0: f1, 1: f2, 2: f3})\n ```\n\n Args:\n branch_index: An int Tensor specifying which of `branch_fns` should be\n executed.\n branch_fns: A `dict` mapping `int`s to callables, or a `list` of\n (`int`, callable) pairs, or simply a list of callables (in which case the\n index serves as the key). Each callable must return a matching structure\n of tensors.\n default: Optional callable that returns a structure of tensors.\n name: A name for this operation (optional).\n\n Returns:\n The tensors returned by the callable identified by `branch_index`, or those\n returned by `default` if no key matches and `default` was provided, or those\n returned by the max-keyed `branch_fn` if no `default` is provided.\n\n Raises:\n TypeError: If `branch_fns` is not a list/dictionary.\n TypeError: If `branch_fns` is a list but does not contain 2-tuples or\n callables.\n TypeError: If `fns[i]` is not callable for any i, or `default` is not\n callable.\n "},{name:"tan",path:"./tf/math/tan.md",desc:"Computes tan of x element-wise.",type:"Functions",docs:'Computes tan of x element-wise.\n\n Given an input tensor, this function computes tangent of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `(-inf, inf)`. If input lies outside the boundary, `nan`\n is returned.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"tanh",path:"./tf/math/tanh.md",desc:"Computes hyperbolic tangent of `x` element-wise.",type:"Functions",docs:'Computes hyperbolic tangent of `x` element-wise.\n\n Given an input tensor, this function computes hyperbolic tangent of every\n element in the tensor. Input range is `[-inf, inf]` and\n output range is `[-1,1]`.\n\n >>> x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")])\n >>> tf.math.tanh(x)\n \n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.tanh(x.values, ...), x.dense_shape)`'},{name:"tensor_scatter_nd_add",path:"./tf/tensor_scatter_nd_add.md",desc:"Adds sparse `updates` to an existing tensor according to `indices`.",type:"Functions",docs:`Adds sparse \`updates\` to an existing tensor according to \`indices\`. + `},{name:"switch_case",path:"./tf/switch_case.md",desc:"Create a switch/case operation, i.e. an integer-indexed conditional.",type:"Functions",docs:"Create a switch/case operation, i.e. an integer-indexed conditional.\n\n See also `tf.case`.\n\n This op can be substantially more efficient than `tf.case` when exactly one\n branch will be selected. `tf.switch_case` is more like a C++ switch/case\n statement than `tf.case`, which is more like an if/elif/elif/else chain.\n\n The `branch_fns` parameter is either a dict from `int` to callables, or list\n of (`int`, callable) pairs, or simply a list of callables (in which case the\n index is implicitly the key). The `branch_index` `Tensor` is used to select an\n element in `branch_fns` with matching `int` key, falling back to `default`\n if none match, or `max(keys)` if no `default` is provided. The keys must form\n a contiguous set from `0` to `len(branch_fns) - 1`.\n\n `tf.switch_case` supports nested structures as implemented in `tf.nest`. All\n callables must return the same (possibly nested) value structure of lists,\n tuples, and/or named tuples.\n\n **Example:**\n\n Pseudocode:\n\n ```c++\n switch (branch_index) { // c-style switch\n case 0: return 17;\n case 1: return 31;\n default: return -1;\n }\n ```\n or\n ```python\n branches = {0: lambda: 17, 1: lambda: 31}\n branches.get(branch_index, lambda: -1)()\n ```\n\n Expressions:\n\n ```python\n def f1(): return tf.constant(17)\n def f2(): return tf.constant(31)\n def f3(): return tf.constant(-1)\n r = tf.switch_case(branch_index, branch_fns={0: f1, 1: f2}, default=f3)\n # Equivalent: tf.switch_case(branch_index, branch_fns={0: f1, 1: f2, 2: f3})\n ```\n\n Args:\n branch_index: An int Tensor specifying which of `branch_fns` should be\n executed.\n branch_fns: A `dict` mapping `int`s to callables, or a `list` of\n (`int`, callable) pairs, or simply a list of callables (in which case the\n index serves as the key). Each callable must return a matching structure\n of tensors.\n default: Optional callable that returns a structure of tensors.\n name: A name for this operation (optional).\n\n Returns:\n The tensors returned by the callable identified by `branch_index`, or those\n returned by `default` if no key matches and `default` was provided, or those\n returned by the max-keyed `branch_fn` if no `default` is provided.\n\n Raises:\n TypeError: If `branch_fns` is not a list/dictionary.\n TypeError: If `branch_fns` is a list but does not contain 2-tuples or\n callables.\n TypeError: If `fns[i]` is not callable for any i, or `default` is not\n callable.\n "},{name:"tan",path:"./tf/math/tan.md",desc:"Computes tan of x element-wise.",type:"Functions",docs:'Computes tan of x element-wise.\n\n Given an input tensor, this function computes tangent of every\n element in the tensor. Input range is `(-inf, inf)` and\n output range is `(-inf, inf)`. If input lies outside the boundary, `nan`\n is returned.\n\n ```python\n x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])\n tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan]\n ```\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n '},{name:"tanh",path:"./tf/math/tanh.md",desc:"Computes hyperbolic tangent of `x` element-wise.",type:"Functions",docs:'Computes hyperbolic tangent of `x` element-wise.\n\n Given an input tensor, this function computes hyperbolic tangent of every\n element in the tensor. Input range is `[-inf, inf]` and\n output range is `[-1,1]`.\n\n ```>>> x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")])\n >>> tf.math.tanh(x)\n \n\n```\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n\n If `x` is a `SparseTensor`, returns\n `SparseTensor(x.indices, tf.math.tanh(x.values, ...), x.dense_shape)`'},{name:"tensor_scatter_nd_add",path:"./tf/tensor_scatter_nd_add.md",desc:"Adds sparse `updates` to an existing tensor according to `indices`.",type:"Functions",docs:`Adds sparse \`updates\` to an existing tensor according to \`indices\`. This operation creates a new tensor by adding sparse \`updates\` to the passed in \`tensor\`. @@ -6901,7 +6020,7 @@ Examples: In Python, this scatter add operation would look like this: - >>> indices = tf.constant([[4], [3], [1], [7]]) + \`\`\`>>> indices = tf.constant([[4], [3], [1], [7]]) >>> updates = tf.constant([9, 10, 11, 12]) >>> tensor = tf.ones([8], dtype=tf.int32) >>> updated = tf.tensor_scatter_nd_add(tensor, indices, updates) @@ -6909,13 +6028,14 @@ Examples: +\`\`\` We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: - >>> indices = tf.constant([[0], [2]]) + \`\`\`>>> indices = tf.constant([[0], [2]]) >>> updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], ... [7, 7, 7, 7], [8, 8, 8, 8]], ... [[5, 5, 5, 5], [6, 6, 6, 6], @@ -6929,6 +6049,7 @@ Examples: [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int32)> +\`\`\` Note: on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored. @@ -6942,30 +6063,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`tensor\`. - `},{name:"tensor_scatter_nd_max",path:"./tf/tensor_scatter_nd_max.md",desc:"Apply a sparse update to a tensor taking the element-wise maximum.",type:"Functions",docs:`Apply a sparse update to a tensor taking the element-wise maximum. - - Returns a new tensor copied from \`tensor\` whose values are element-wise maximum between - tensor and updates according to the indices. - - >>> tensor = [0, 0, 0, 0, 0, 0, 0, 0] - >>> indices = [[1], [4], [5]] - >>> updates = [1, -1, 1] - >>> tf.tensor_scatter_nd_max(tensor, indices, updates).numpy() - array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int32) - - Refer to \`tf.tensor_scatter_nd_update\` for more details. - - Args: - tensor: A \`Tensor\`. Tensor to update. - indices: A \`Tensor\`. Must be one of the following types: \`int32\`, \`int64\`. - Index tensor. - updates: A \`Tensor\`. Must have the same type as \`tensor\`. - Updates to scatter into output. - name: A name for the operation (optional). - - Returns: - A \`Tensor\`. Has the same type as \`tensor\`. - `},{name:"tensor_scatter_nd_min",path:"./tf/tensor_scatter_nd_min.md",desc:"",type:"Functions",docs:"TODO: add doc.\n\n Args:\n tensor: A `Tensor`. Tensor to update.\n indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n Index tensor.\n updates: A `Tensor`. Must have the same type as `tensor`.\n Updates to scatter into output.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n "},{name:"tensor_scatter_nd_sub",path:"./tf/tensor_scatter_nd_sub.md",desc:"Subtracts sparse `updates` from an existing tensor according to `indices`.",type:"Functions",docs:`Subtracts sparse \`updates\` from an existing tensor according to \`indices\`. + `},{name:"tensor_scatter_nd_max",path:"./tf/tensor_scatter_nd_max.md",desc:"Apply a sparse update to a tensor taking the element-wise maximum.",type:"Functions",docs:"Apply a sparse update to a tensor taking the element-wise maximum.\n\n Returns a new tensor copied from `tensor` whose values are element-wise maximum between\n tensor and updates according to the indices.\n\n ```>>> tensor = [0, 0, 0, 0, 0, 0, 0, 0] \n >>> indices = [[1], [4], [5]]\n >>> updates = [1, -1, 1]\n >>> tf.tensor_scatter_nd_max(tensor, indices, updates).numpy()\n array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int32)\n\n```\n Refer to `tf.tensor_scatter_nd_update` for more details.\n\n Args:\n tensor: A `Tensor`. Tensor to update.\n indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n Index tensor.\n updates: A `Tensor`. Must have the same type as `tensor`.\n Updates to scatter into output.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n "},{name:"tensor_scatter_nd_min",path:"./tf/tensor_scatter_nd_min.md",desc:"",type:"Functions",docs:"TODO: add doc.\n\n Args:\n tensor: A `Tensor`. Tensor to update.\n indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.\n Index tensor.\n updates: A `Tensor`. Must have the same type as `tensor`.\n Updates to scatter into output.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `tensor`.\n "},{name:"tensor_scatter_nd_sub",path:"./tf/tensor_scatter_nd_sub.md",desc:"Subtracts sparse `updates` from an existing tensor according to `indices`.",type:"Functions",docs:`Subtracts sparse \`updates\` from an existing tensor according to \`indices\`. This operation creates a new tensor by subtracting sparse \`updates\` from the passed in \`tensor\`. @@ -7116,12 +6214,13 @@ Examples: This scatter operation would look like this: - >>> tensor = [0, 0, 0, 0, 0, 0, 0, 0] # tf.rank(tensor) == 1 + \`\`\`>>> tensor = [0, 0, 0, 0, 0, 0, 0, 0] # tf.rank(tensor) == 1 >>> indices = [[1], [3], [4], [7]] # num_updates == 4, index_depth == 1 >>> updates = [9, 10, 11, 12] # num_updates == 4 >>> print(tf.tensor_scatter_nd_update(tensor, indices, updates)) tf.Tensor([ 0 9 0 10 11 0 0 12], shape=(8,), dtype=int32) +\`\`\` The length (first axis) of \`updates\` must equal the length of the \`indices\`: \`num_updates\`. This is the number of updates being inserted. Each scalar update is inserted into \`tensor\` at the indexed location. @@ -7129,7 +6228,7 @@ Examples: For a higher rank input \`tensor\` scalar updates can be inserted by using an \`index_depth\` that matches \`tf.rank(tensor)\`: - >>> tensor = [[1, 1], [1, 1], [1, 1]] # tf.rank(tensor) == 2 + \`\`\`>>> tensor = [[1, 1], [1, 1], [1, 1]] # tf.rank(tensor) == 2 >>> indices = [[0, 1], [2, 0]] # num_updates == 2, index_depth == 2 >>> updates = [5, 10] # num_updates == 2 >>> print(tf.tensor_scatter_nd_update(tensor, indices, updates)) @@ -7138,6 +6237,7 @@ Examples: [ 1 1] [10 1]], shape=(3, 2), dtype=int32) +\`\`\` ### Slice updates When the input \`tensor\` has more than one axis scatter can be used to update @@ -7162,28 +6262,32 @@ Examples: For example, to update rows of a \`(6, 3)\` \`tensor\`: - >>> tensor = tf.zeros([6, 3], dtype=tf.int32) + \`\`\`>>> tensor = tf.zeros([6, 3], dtype=tf.int32) +\`\`\` Use an index depth of one. - >>> indices = tf.constant([[2], [4]]) # num_updates == 2, index_depth == 1 + \`\`\`>>> indices = tf.constant([[2], [4]]) # num_updates == 2, index_depth == 1 >>> num_updates, index_depth = indices.shape.as_list() +\`\`\` The \`outer_shape\` is \`6\`, the inner shape is \`3\`: - >>> outer_shape = tensor.shape[:index_depth] + \`\`\`>>> outer_shape = tensor.shape[:index_depth] >>> inner_shape = tensor.shape[index_depth:] +\`\`\` 2 rows are being indexed so 2 \`updates\` must be supplied. Each update must be shaped to match the \`inner_shape\`. - >>> # num_updates == 2, inner_shape==3 + \`\`\`>>> # num_updates == 2, inner_shape==3 >>> updates = tf.constant([[1, 2, 3], ... [4, 5, 6]]) +\`\`\` Altogether this gives: - >>> tf.tensor_scatter_nd_update(tensor, indices, updates).numpy() + \`\`\`>>> tf.tensor_scatter_nd_update(tensor, indices, updates).numpy() array([[0, 0, 0], [0, 0, 0], [1, 2, 3], @@ -7191,6 +6295,7 @@ Examples: [4, 5, 6], [0, 0, 0]], dtype=int32) +\`\`\` #### More slice update examples A tensor representing a batch of uniformly sized video clips naturally has 5 @@ -7198,9 +6303,10 @@ Examples: For example: - >>> batch_size, time, width, height, channels = 13,11,7,5,3 + \`\`\`>>> batch_size, time, width, height, channels = 13,11,7,5,3 >>> video_batch = tf.zeros([batch_size, time, width, height, channels]) +\`\`\` To replace a selection of video clips: * Use an \`index_depth\` of 1 (indexing the \`outer_shape\`: \`[batch_size]\`) * Provide updates each with a shape matching the \`inner_shape\`: @@ -7208,10 +6314,11 @@ Examples: To replace the first two clips with ones: - >>> indices = [[0],[1]] + \`\`\`>>> indices = [[0],[1]] >>> new_clips = tf.ones([2, time, width, height, channels]) >>> tf.tensor_scatter_nd_update(video_batch, indices, new_clips) +\`\`\` To replace a selection of frames in the videos: * \`indices\` must have an \`index_depth\` of 2 for the \`outer_shape\`: @@ -7221,12 +6328,13 @@ Examples: To replace the first frame of the first three video clips: - >>> indices = [[0, 0], [1, 0], [2, 0]] # num_updates=3, index_depth=2 + \`\`\`>>> indices = [[0, 0], [1, 0], [2, 0]] # num_updates=3, index_depth=2 >>> new_images = tf.ones([ ... # num_updates=3, inner_shape=(width, height, channels) ... 3, width, height, channels]) >>> tf.tensor_scatter_nd_update(video_batch, indices, new_images) +\`\`\` ### Folded indices In simple cases it's convenient to think of \`indices\` and \`updates\` as @@ -7268,7 +6376,7 @@ Examples: For example, to draw an \`X\` on a \`(5,5)\` matrix start with these indices: - >>> tensor = tf.zeros([5,5]) + \`\`\`>>> tensor = tf.zeros([5,5]) >>> indices = tf.constant([ ... [[0,0], ... [1,1], @@ -7284,6 +6392,7 @@ Examples: >>> indices.shape.as_list() # batch_shape == [2, 5], index_depth == 2 [2, 5, 2] +\`\`\` Here the \`indices\` do not have a shape of \`[num_updates, index_depth]\`, but a shape of \`batch_shape+[index_depth]\`. @@ -7293,20 +6402,22 @@ Examples: * \`inner_shape\` is \`()\` - each update is scalar * \`updates.shape\` is \`batch_shape + inner_shape == (5,2) + ()\` - >>> updates = [ + \`\`\`>>> updates = [ ... [1,1,1,1,1], ... [1,1,1,1,1], ... ] +\`\`\` Putting this together gives: - >>> tf.tensor_scatter_nd_update(tensor, indices, updates).numpy() + \`\`\`>>> tf.tensor_scatter_nd_update(tensor, indices, updates).numpy() array([[1., 0., 0., 0., 1.], [0., 1., 0., 1., 0.], [0., 0., 1., 0., 0.], [0., 1., 0., 1., 0.], [1., 0., 0., 0., 1.]], dtype=float32) +\`\`\` Args: tensor: Tensor to copy/update. indices: Indices to update. @@ -7324,7 +6435,7 @@ Examples: dimension. For example, tiling \`[a b c d]\` by \`[2]\` produces \`[a b c d a b c d]\`. - >>> a = tf.constant([[1,2,3],[4,5,6]], tf.int32) + \`\`\`>>> a = tf.constant([[1,2,3],[4,5,6]], tf.int32) >>> b = tf.constant([1,2], tf.int32) >>> tf.tile(a, b) +\`\`\` Args: input: A \`Tensor\`. 1-D or higher. multiples: A \`Tensor\`. Must be one of the following types: \`int32\`, \`int64\`. @@ -7387,18 +6499,19 @@ Examples: For example: - >>> x = tf.constant([[1, 2, 3], [4, 5, 6]]) + \`\`\`>>> x = tf.constant([[1, 2, 3], [4, 5, 6]]) >>> tf.transpose(x) +\`\`\` Equivalently, you could call \`tf.transpose(x, perm=[1, 0])\`. If \`x\` is complex, setting conjugate=True gives the conjugate transpose: - >>> x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j], + \`\`\`>>> x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j], ... [4 + 4j, 5 + 5j, 6 + 6j]]) >>> tf.transpose(x, conjugate=True) +\`\`\` 'perm' is more useful for n-dimensional tensors where n > 2: - >>> x = tf.constant([[[ 1, 2, 3], + \`\`\`>>> x = tf.constant([[[ 1, 2, 3], ... [ 4, 5, 6]], ... [[ 7, 8, 9], ... [10, 11, 12]]]) +\`\`\` As above, simply calling \`tf.transpose\` will default to \`perm=[2,1,0]\`. To take the transpose of the matrices in dimension-0 (such as when you are transposing matrices where 0 is the batch dimension), you would set \`perm=[0,2,1]\`. - >>> tf.transpose(x, perm=[0, 2, 1]) + \`\`\`>>> tf.transpose(x, perm=[0, 2, 1]) +\`\`\` Note: This has a shorthand \`linalg.matrix_transpose\`): Args: @@ -7439,35 +6555,7 @@ Examples: Returns: A transposed \`Tensor\`. - `},{name:"truediv",path:"./tf/math/truediv.md",desc:"Divides x / y elementwise (using Python 3 division operator semantics",type:"Functions",docs:"Divides x / y elementwise (using Python 3 division operator semantics).\n\n NOTE: Prefer using the Tensor operator or tf.divide which obey Python\n division operator semantics.\n\n This function forces Python 3 division operator semantics where all integer\n arguments are cast to floating types first. This op is generated by normal\n `x / y` division in Python 3 and in Python 2.7 with\n `from __future__ import division`. If you want integer division that rounds\n down, use `x // y` or `tf.math.floordiv`.\n\n `x` and `y` must have the same numeric type. If the inputs are floating\n point, the output will have the same type. If the inputs are integral, the\n inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`\n and `int64` (matching the behavior of Numpy).\n\n Args:\n x: `Tensor` numerator of numeric type.\n y: `Tensor` denominator of numeric type.\n name: A name for the operation (optional).\n\n Returns:\n `x / y` evaluated in floating point.\n\n Raises:\n TypeError: If `x` and `y` have different dtypes.\n "},{name:"truncatediv",path:"./tf/truncatediv.md",desc:"Returns x / y element-wise for integer types.",type:"Functions",docs:"Returns x / y element-wise for integer types.\n\n Truncation designates that negative numbers will round fractional quantities\n toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different\n than Python semantics. See `FloorDiv` for a division function that matches\n Python Semantics.\n\n *NOTE*: `truncatediv` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `uint32`, `uint64`, `int64`, `complex64`, `complex128`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"truncatemod",path:"./tf/truncatemod.md",desc:"Returns element-wise remainder of division. This emulates C semantics in that",type:"Functions",docs:"Returns element-wise remainder of division. This emulates C semantics in that\n\n the result here is consistent with a truncating divide. E.g. `truncate(x / y) *\n y + truncate_mod(x, y) = x`.\n\n *NOTE*: `truncatemod` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Args:\n x: A `Tensor`. Must be one of the following types: `int32`, `int64`, `bfloat16`, `half`, `float32`, `float64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"tuple",path:"./tf/tuple.md",desc:"Groups tensors together.",type:"Functions",docs:"Groups tensors together.\n\n The returned tensors have the same value as the input tensors, but they\n are computed only after all the input tensors have been computed.\n\n Note: *In TensorFlow 2 with eager and/or Autograph, you should not require\n this method, as ops execute in the expected order thanks to automatic control\n dependencies.* Only use `tf.tuple` when working with v1 `tf.Graph` code.\n\n See also `tf.group` and `tf.control_dependencies`.\n\n Args:\n tensors: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.\n control_inputs: List of additional ops to finish before returning.\n name: (optional) A name to use as a `name_scope` for the operation.\n\n Returns:\n Same as `tensors`.\n\n Raises:\n ValueError: If `tensors` does not contain any `Tensor` or `IndexedSlices`.\n TypeError: If `control_inputs` is not a list of `Operation` or `Tensor`\n objects.\n\n "},{name:"type_spec_from_value",path:"./tf/type_spec_from_value.md",desc:'Returns a tf.TypeSpec that represents the given `value`.',type:"Functions",docs:`Returns a \`tf.TypeSpec\` that represents the given \`value\`. - - Examples: - - >>> tf.type_spec_from_value(tf.constant([1, 2, 3])) - TensorSpec(shape=(3,), dtype=tf.int32, name=None) - >>> tf.type_spec_from_value(np.array([4.0, 5.0], np.float64)) - TensorSpec(shape=(2,), dtype=tf.float64, name=None) - >>> tf.type_spec_from_value(tf.ragged.constant([[1, 2], [3, 4, 5]])) - RaggedTensorSpec(TensorShape([2, None]), tf.int32, 1, tf.int64) - - >>> example_input = tf.ragged.constant([[1, 2], [3]]) - >>> @tf.function(input_signature=[tf.type_spec_from_value(example_input)]) - ... def f(x): - ... return tf.reduce_sum(x, axis=1) - - Args: - value: A value that can be accepted or returned by TensorFlow APIs. Accepted - types for \`value\` include \`tf.Tensor\`, any value that can be converted to - \`tf.Tensor\` using \`tf.convert_to_tensor\`, and any subclass of - \`CompositeTensor\` (such as \`tf.RaggedTensor\`). - - Returns: - A \`TypeSpec\` that is compatible with \`value\`. - - Raises: - TypeError: If a TypeSpec cannot be built for \`value\`, because its type - is not supported. - `},{name:"unique",path:"./tf/unique.md",desc:"Finds unique elements in a 1-D tensor.",type:"Functions",docs:"Finds unique elements in a 1-D tensor.\n\n This operation returns a tensor `y` containing all of the unique elements of `x`\n sorted in the same order that they occur in `x`; `x` does not need to be sorted.\n This operation also returns a tensor `idx` the same size as `x` that contains\n the index of each value of `x` in the unique output `y`. In other words:\n\n `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`\n\n Examples:\n\n ```\n # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]\n y, idx = unique(x)\n y ==> [1, 2, 4, 7, 8]\n idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]\n ```\n\n ```\n # tensor 'x' is [4, 5, 1, 2, 3, 3, 4, 5]\n y, idx = unique(x)\n y ==> [4, 5, 1, 2, 3]\n idx ==> [0, 1, 2, 3, 4, 4, 0, 1]\n ```\n\n Args:\n x: A `Tensor`. 1-D.\n out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.\n name: A name for the operation (optional).\n\n Returns:\n A tuple of `Tensor` objects (y, idx).\n\n y: A `Tensor`. Has the same type as `x`.\n idx: A `Tensor` of type `out_idx`.\n "},{name:"unique_with_counts",path:"./tf/unique_with_counts.md",desc:"Finds unique elements in a 1-D tensor.",type:"Functions",docs:"Finds unique elements in a 1-D tensor.\n\n This operation returns a tensor `y` containing all of the unique elements of `x`\n sorted in the same order that they occur in `x`. This operation also returns a\n tensor `idx` the same size as `x` that contains the index of each value of `x`\n in the unique output `y`. Finally, it returns a third tensor `count` that\n contains the count of each element of `y` in `x`. In other words:\n\n `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`\n\n For example:\n\n ```\n # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]\n y, idx, count = unique_with_counts(x)\n y ==> [1, 2, 4, 7, 8]\n idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]\n count ==> [2, 1, 3, 1, 2]\n ```\n\n Args:\n x: A `Tensor`. 1-D.\n out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.\n name: A name for the operation (optional).\n\n Returns:\n A tuple of `Tensor` objects (y, idx, count).\n\n y: A `Tensor`. Has the same type as `x`.\n idx: A `Tensor` of type `out_idx`.\n count: A `Tensor` of type `out_idx`.\n "},{name:"unravel_index",path:"./tf/unravel_index.md",desc:"Converts an array of flat indices into a tuple of coordinate arrays.",type:"Functions",docs:`Converts an array of flat indices into a tuple of coordinate arrays. + `},{name:"truediv",path:"./tf/math/truediv.md",desc:"Divides x / y elementwise (using Python 3 division operator semantics",type:"Functions",docs:"Divides x / y elementwise (using Python 3 division operator semantics).\n\n NOTE: Prefer using the Tensor operator or tf.divide which obey Python\n division operator semantics.\n\n This function forces Python 3 division operator semantics where all integer\n arguments are cast to floating types first. This op is generated by normal\n `x / y` division in Python 3 and in Python 2.7 with\n `from __future__ import division`. If you want integer division that rounds\n down, use `x // y` or `tf.math.floordiv`.\n\n `x` and `y` must have the same numeric type. If the inputs are floating\n point, the output will have the same type. If the inputs are integral, the\n inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`\n and `int64` (matching the behavior of Numpy).\n\n Args:\n x: `Tensor` numerator of numeric type.\n y: `Tensor` denominator of numeric type.\n name: A name for the operation (optional).\n\n Returns:\n `x / y` evaluated in floating point.\n\n Raises:\n TypeError: If `x` and `y` have different dtypes.\n "},{name:"truncatediv",path:"./tf/truncatediv.md",desc:"Returns x / y element-wise for integer types.",type:"Functions",docs:"Returns x / y element-wise for integer types.\n\n Truncation designates that negative numbers will round fractional quantities\n toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different\n than Python semantics. See `FloorDiv` for a division function that matches\n Python Semantics.\n\n *NOTE*: `truncatediv` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Args:\n x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `uint32`, `uint64`, `int64`, `complex64`, `complex128`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"truncatemod",path:"./tf/truncatemod.md",desc:"Returns element-wise remainder of division. This emulates C semantics in that",type:"Functions",docs:"Returns element-wise remainder of division. This emulates C semantics in that\n\n the result here is consistent with a truncating divide. E.g. `truncate(x / y) *\n y + truncate_mod(x, y) = x`.\n\n *NOTE*: `truncatemod` supports broadcasting. More about broadcasting\n [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)\n\n Args:\n x: A `Tensor`. Must be one of the following types: `int32`, `int64`, `bfloat16`, `half`, `float32`, `float64`.\n y: A `Tensor`. Must have the same type as `x`.\n name: A name for the operation (optional).\n\n Returns:\n A `Tensor`. Has the same type as `x`.\n "},{name:"tuple",path:"./tf/tuple.md",desc:"Groups tensors together.",type:"Functions",docs:"Groups tensors together.\n\n The returned tensors have the same value as the input tensors, but they\n are computed only after all the input tensors have been computed.\n\n Note: *In TensorFlow 2 with eager and/or Autograph, you should not require\n this method, as ops execute in the expected order thanks to automatic control\n dependencies.* Only use `tf.tuple` when working with v1 `tf.Graph` code.\n\n See also `tf.group` and `tf.control_dependencies`.\n\n Args:\n tensors: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.\n control_inputs: List of additional ops to finish before returning.\n name: (optional) A name to use as a `name_scope` for the operation.\n\n Returns:\n Same as `tensors`.\n\n Raises:\n ValueError: If `tensors` does not contain any `Tensor` or `IndexedSlices`.\n TypeError: If `control_inputs` is not a list of `Operation` or `Tensor`\n objects.\n\n "},{name:"type_spec_from_value",path:"./tf/type_spec_from_value.md",desc:'Returns a tf.TypeSpec that represents the given `value`.',type:"Functions",docs:"Returns a `tf.TypeSpec` that represents the given `value`.\n\n Examples:\n\n ```>>> tf.type_spec_from_value(tf.constant([1, 2, 3]))\n TensorSpec(shape=(3,), dtype=tf.int32, name=None)\n >>> tf.type_spec_from_value(np.array([4.0, 5.0], np.float64))\n TensorSpec(shape=(2,), dtype=tf.float64, name=None)\n >>> tf.type_spec_from_value(tf.ragged.constant([[1, 2], [3, 4, 5]]))\n RaggedTensorSpec(TensorShape([2, None]), tf.int32, 1, tf.int64)\n\n```\n ```>>> example_input = tf.ragged.constant([[1, 2], [3]])\n >>> @tf.function(input_signature=[tf.type_spec_from_value(example_input)])\n ... def f(x):\n ... return tf.reduce_sum(x, axis=1)\n\n```\n Args:\n value: A value that can be accepted or returned by TensorFlow APIs. Accepted\n types for `value` include `tf.Tensor`, any value that can be converted to\n `tf.Tensor` using `tf.convert_to_tensor`, and any subclass of\n `CompositeTensor` (such as `tf.RaggedTensor`).\n\n Returns:\n A `TypeSpec` that is compatible with `value`.\n\n Raises:\n TypeError: If a TypeSpec cannot be built for `value`, because its type\n is not supported.\n "},{name:"unique",path:"./tf/unique.md",desc:"Finds unique elements in a 1-D tensor.",type:"Functions",docs:"Finds unique elements in a 1-D tensor.\n\n This operation returns a tensor `y` containing all of the unique elements of `x`\n sorted in the same order that they occur in `x`; `x` does not need to be sorted.\n This operation also returns a tensor `idx` the same size as `x` that contains\n the index of each value of `x` in the unique output `y`. In other words:\n\n `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`\n\n Examples:\n\n ```\n # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]\n y, idx = unique(x)\n y ==> [1, 2, 4, 7, 8]\n idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]\n ```\n\n ```\n # tensor 'x' is [4, 5, 1, 2, 3, 3, 4, 5]\n y, idx = unique(x)\n y ==> [4, 5, 1, 2, 3]\n idx ==> [0, 1, 2, 3, 4, 4, 0, 1]\n ```\n\n Args:\n x: A `Tensor`. 1-D.\n out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.\n name: A name for the operation (optional).\n\n Returns:\n A tuple of `Tensor` objects (y, idx).\n\n y: A `Tensor`. Has the same type as `x`.\n idx: A `Tensor` of type `out_idx`.\n "},{name:"unique_with_counts",path:"./tf/unique_with_counts.md",desc:"Finds unique elements in a 1-D tensor.",type:"Functions",docs:"Finds unique elements in a 1-D tensor.\n\n This operation returns a tensor `y` containing all of the unique elements of `x`\n sorted in the same order that they occur in `x`. This operation also returns a\n tensor `idx` the same size as `x` that contains the index of each value of `x`\n in the unique output `y`. Finally, it returns a third tensor `count` that\n contains the count of each element of `y` in `x`. In other words:\n\n `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`\n\n For example:\n\n ```\n # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]\n y, idx, count = unique_with_counts(x)\n y ==> [1, 2, 4, 7, 8]\n idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]\n count ==> [2, 1, 3, 1, 2]\n ```\n\n Args:\n x: A `Tensor`. 1-D.\n out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.\n name: A name for the operation (optional).\n\n Returns:\n A tuple of `Tensor` objects (y, idx, count).\n\n y: A `Tensor`. Has the same type as `x`.\n idx: A `Tensor` of type `out_idx`.\n count: A `Tensor` of type `out_idx`.\n "},{name:"unravel_index",path:"./tf/unravel_index.md",desc:"Converts an array of flat indices into a tuple of coordinate arrays.",type:"Functions",docs:`Converts an array of flat indices into a tuple of coordinate arrays. Example: @@ -7501,119 +6589,7 @@ Examples: Returns: A \`Tensor\`. Has the same type as \`indices\`. - `},{name:"unstack",path:"./tf/unstack.md",desc:"Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1",type:"Functions",docs:`Unpacks the given dimension of a rank-\`R\` tensor into rank-\`(R-1)\` tensors. - - Unpacks tensors from \`value\` by chipping it along the \`axis\` dimension. - - >>> x = tf.reshape(tf.range(12), (3,4)) - >>> - >>> p, q, r = tf.unstack(x) - >>> p.shape.as_list() - [4] - - >>> i, j, k, l = tf.unstack(x, axis=1) - >>> i.shape.as_list() - [3] - - This is the opposite of stack. - - >>> x = tf.stack([i, j, k, l], axis=1) - - More generally if you have a tensor of shape \`(A, B, C, D)\`: - - >>> A, B, C, D = [2, 3, 4, 5] - >>> t = tf.random.normal(shape=[A, B, C, D]) - - The number of tensor returned is equal to the length of the target \`axis\`: - - >>> axis = 2 - >>> items = tf.unstack(t, axis=axis) - >>> len(items) == t.shape[axis] - True - - The shape of each result tensor is equal to the shape of the input tensor, - with the target \`axis\` removed. - - >>> items[0].shape.as_list() # [A, B, D] - [2, 3, 5] - - The value of each tensor \`items[i]\` is equal to the slice of \`input\` across - \`axis\` at index \`i\`: - - >>> for i in range(len(items)): - ... slice = t[:,:,i,:] - ... assert tf.reduce_all(slice == items[i]) - - #### Python iterable unpacking - - With eager execution you _can_ unstack the 0th axis of a tensor using python's - iterable unpacking: - - >>> t = tf.constant([1,2,3]) - >>> a,b,c = t - - \`unstack\` is still necessary because Iterable unpacking doesn't work in - a \`@tf.function\`: Symbolic tensors are not iterable. - - You need to use \`tf.unstack\` here: - - >>> @tf.function - ... def bad(t): - ... a,b,c = t - ... return a - >>> - >>> bad(t) - Traceback (most recent call last): - ... - OperatorNotAllowedInGraphError: ... - - >>> @tf.function - ... def good(t): - ... a,b,c = tf.unstack(t) - ... return a - >>> - >>> good(t).numpy() - 1 - - #### Unknown shapes - - Eager tensors have concrete values, so their shape is always known. - Inside a \`tf.function\` the symbolic tensors may have unknown shapes. - If the length of \`axis\` is unknown \`tf.unstack\` will fail because it cannot - handle an unknown number of tensors: - - >>> @tf.function(input_signature=[tf.TensorSpec([None], tf.float32)]) - ... def bad(t): - ... tensors = tf.unstack(t) - ... return tensors[0] - >>> - >>> bad(tf.constant([1,2,3])) - Traceback (most recent call last): - ... - ValueError: Cannot infer argument \`num\` from shape (None,) - - If you know the \`axis\` length you can pass it as the \`num\` argument. But this - must be a constant value. - - If you actually need a variable number of tensors in a single \`tf.function\` - trace, you will need to use exlicit loops and a \`tf.TensorArray\` instead. - - Args: - value: A rank \`R > 0\` \`Tensor\` to be unstacked. - num: An \`int\`. The length of the dimension \`axis\`. Automatically inferred if - \`None\` (the default). - axis: An \`int\`. The axis to unstack along. Defaults to the first dimension. - Negative values wrap around, so the valid range is \`[-R, R)\`. - name: A name for the operation (optional). - - Returns: - The list of \`Tensor\` objects unstacked from \`value\`. - - Raises: - ValueError: If \`axis\` is out of the range \`[-R, R)\`. - ValueError: If \`num\` is unspecified and cannot be inferred. - InvalidArgumentError: If \`num\` does not match the shape of \`value\`. - `},{name:"variable_creator_scope",path:"./tf/variable_creator_scope.md",desc:"Scope which defines a variable creation function to be used by variable(",type:"Functions",docs:`Scope which defines a variable creation function to be used by variable(). + `},{name:"unstack",path:"./tf/unstack.md",desc:"Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1",type:"Functions",docs:"Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.\n\n Unpacks tensors from `value` by chipping it along the `axis` dimension.\n\n ```>>> x = tf.reshape(tf.range(12), (3,4))\n >>>\n >>> p, q, r = tf.unstack(x)\n >>> p.shape.as_list()\n [4]\n\n```\n ```>>> i, j, k, l = tf.unstack(x, axis=1)\n >>> i.shape.as_list()\n [3]\n\n```\n This is the opposite of stack.\n\n ```>>> x = tf.stack([i, j, k, l], axis=1)\n\n```\n More generally if you have a tensor of shape `(A, B, C, D)`:\n\n ```>>> A, B, C, D = [2, 3, 4, 5]\n >>> t = tf.random.normal(shape=[A, B, C, D])\n\n```\n The number of tensor returned is equal to the length of the target `axis`:\n\n ```>>> axis = 2\n >>> items = tf.unstack(t, axis=axis)\n >>> len(items) == t.shape[axis]\n True\n\n```\n The shape of each result tensor is equal to the shape of the input tensor,\n with the target `axis` removed.\n\n ```>>> items[0].shape.as_list() # [A, B, D]\n [2, 3, 5]\n\n```\n The value of each tensor `items[i]` is equal to the slice of `input` across\n `axis` at index `i`:\n\n ```>>> for i in range(len(items)):\n ... slice = t[:,:,i,:]\n ... assert tf.reduce_all(slice == items[i])\n\n```\n #### Python iterable unpacking\n\n With eager execution you _can_ unstack the 0th axis of a tensor using python's\n iterable unpacking:\n\n ```>>> t = tf.constant([1,2,3])\n >>> a,b,c = t\n\n```\n `unstack` is still necessary because Iterable unpacking doesn't work in\n a `@tf.function`: Symbolic tensors are not iterable.\n\n You need to use `tf.unstack` here:\n\n ```>>> @tf.function\n ... def bad(t):\n ... a,b,c = t\n ... return a\n >>>\n >>> bad(t)\n Traceback (most recent call last):\n ...\n OperatorNotAllowedInGraphError: ...\n\n```\n ```>>> @tf.function\n ... def good(t):\n ... a,b,c = tf.unstack(t)\n ... return a\n >>>\n >>> good(t).numpy()\n 1\n\n```\n #### Unknown shapes\n\n Eager tensors have concrete values, so their shape is always known.\n Inside a `tf.function` the symbolic tensors may have unknown shapes.\n If the length of `axis` is unknown `tf.unstack` will fail because it cannot\n handle an unknown number of tensors:\n\n ```>>> @tf.function(input_signature=[tf.TensorSpec([None], tf.float32)])\n ... def bad(t):\n ... tensors = tf.unstack(t)\n ... return tensors[0]\n >>>\n >>> bad(tf.constant([1,2,3]))\n Traceback (most recent call last):\n ...\n ValueError: Cannot infer argument `num` from shape (None,)\n\n```\n If you know the `axis` length you can pass it as the `num` argument. But this\n must be a constant value.\n\n If you actually need a variable number of tensors in a single `tf.function`\n trace, you will need to use exlicit loops and a `tf.TensorArray` instead.\n\n Args:\n value: A rank `R > 0` `Tensor` to be unstacked.\n num: An `int`. The length of the dimension `axis`. Automatically inferred if\n `None` (the default).\n axis: An `int`. The axis to unstack along. Defaults to the first dimension.\n Negative values wrap around, so the valid range is `[-R, R)`.\n name: A name for the operation (optional).\n\n Returns:\n The list of `Tensor` objects unstacked from `value`.\n\n Raises:\n ValueError: If `axis` is out of the range `[-R, R)`.\n ValueError: If `num` is unspecified and cannot be inferred.\n InvalidArgumentError: If `num` does not match the shape of `value`.\n "},{name:"variable_creator_scope",path:"./tf/variable_creator_scope.md",desc:"Scope which defines a variable creation function to be used by variable(",type:"Functions",docs:`Scope which defines a variable creation function to be used by variable(). variable_creator is expected to be a function with the following signature: @@ -7768,7 +6744,7 @@ Examples: Raises: ValueError: If vectorization fails and fallback_to_while_loop is False. - `},{name:"where",path:"./tf/where.md",desc:"Returns the indices of non-zero elements, or multiplexes `x` and `y`.",type:"Functions",docs:"Returns the indices of non-zero elements, or multiplexes `x` and `y`.\n\n This operation has two modes:\n\n 1. **Return the indices of non-zero elements** - When only\n `condition` is provided the result is an `int64` tensor where each row is\n the index of a non-zero element of `condition`. The result's shape\n is `[tf.math.count_nonzero(condition), tf.rank(condition)]`.\n 2. **Multiplex `x` and `y`** - When both `x` and `y` are provided the\n result has the shape of `x`, `y`, and `condition` broadcast together. The\n result is taken from `x` where `condition` is non-zero\n or `y` where `condition` is zero.\n\n #### 1. Return the indices of non-zero elements\n\n Note: In this mode `condition` can have a dtype of `bool` or any numeric\n dtype.\n\n If `x` and `y` are not provided (both are None):\n\n `tf.where` will return the indices of `condition` that are non-zero,\n in the form of a 2-D tensor with shape `[n, d]`, where `n` is the number of\n non-zero elements in `condition` (`tf.count_nonzero(condition)`), and `d` is\n the number of axes of `condition` (`tf.rank(condition)`).\n\n Indices are output in row-major order. The `condition` can have a `dtype` of\n `tf.bool`, or any numeric `dtype`.\n\n Here `condition` is a 1-axis `bool` tensor with 2 `True` values. The result\n has a shape of `[2,1]`\n\n >>> tf.where([True, False, False, True]).numpy()\n array([[0],\n [3]])\n\n Here `condition` is a 2-axis integer tensor, with 3 non-zero values. The\n result has a shape of `[3, 2]`.\n\n >>> tf.where([[1, 0, 0], [1, 0, 1]]).numpy()\n array([[0, 0],\n [1, 0],\n [1, 2]])\n\n Here `condition` is a 3-axis float tensor, with 5 non-zero values. The output\n shape is `[5, 3]`.\n\n >>> float_tensor = [[[0.1, 0], [0, 2.2], [3.5, 1e6]],\n ... [[0, 0], [0, 0], [99, 0]]]\n >>> tf.where(float_tensor).numpy()\n array([[0, 0, 0],\n [0, 1, 1],\n [0, 2, 0],\n [0, 2, 1],\n [1, 2, 0]])\n\n These indices are the same that `tf.sparse.SparseTensor` would use to\n represent the condition tensor:\n\n >>> sparse = tf.sparse.from_dense(float_tensor)\n >>> sparse.indices.numpy()\n array([[0, 0, 0],\n [0, 1, 1],\n [0, 2, 0],\n [0, 2, 1],\n [1, 2, 0]])\n\n A complex number is considered non-zero if either the real or imaginary\n component is non-zero:\n\n >>> tf.where([complex(0.), complex(1.), 0+1j, 1+1j]).numpy()\n array([[1],\n [2],\n [3]])\n\n #### 2. Multiplex `x` and `y`\n\n Note: In this mode `condition` must have a dtype of `bool`.\n\n If `x` and `y` are also provided (both have non-None values) the `condition`\n tensor acts as a mask that chooses whether the corresponding\n element / row in the output should be taken from `x` (if the element in\n `condition` is `True`) or `y` (if it is `False`).\n\n The shape of the result is formed by\n [broadcasting](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)\n together the shapes of `condition`, `x`, and `y`.\n\n When all three inputs have the same size, each is handled element-wise.\n\n >>> tf.where([True, False, False, True],\n ... [1, 2, 3, 4],\n ... [100, 200, 300, 400]).numpy()\n array([ 1, 200, 300, 4], dtype=int32)\n\n There are two main rules for broadcasting:\n\n 1. If a tensor has fewer axes than the others, length-1 axes are added to the\n left of the shape.\n 2. Axes with length-1 are streched to match the coresponding axes of the other\n tensors.\n\n A length-1 vector is streched to match the other vectors:\n\n >>> tf.where([True, False, False, True], [1, 2, 3, 4], [100]).numpy()\n array([ 1, 100, 100, 4], dtype=int32)\n\n A scalar is expanded to match the other arguments:\n\n >>> tf.where([[True, False], [False, True]], [[1, 2], [3, 4]], 100).numpy()\n array([[ 1, 100], [100, 4]], dtype=int32)\n >>> tf.where([[True, False], [False, True]], 1, 100).numpy()\n array([[ 1, 100], [100, 1]], dtype=int32)\n\n A scalar `condition` returns the complete `x` or `y` tensor, with\n broadcasting applied.\n\n >>> tf.where(True, [1, 2, 3, 4], 100).numpy()\n array([1, 2, 3, 4], dtype=int32)\n >>> tf.where(False, [1, 2, 3, 4], 100).numpy()\n array([100, 100, 100, 100], dtype=int32)\n\n For a non-trivial example of broadcasting, here `condition` has a shape of\n `[3]`, `x` has a shape of `[3,3]`, and `y` has a shape of `[3,1]`.\n Broadcasting first expands the shape of `condition` to `[1,3]`. The final\n broadcast shape is `[3,3]`. `condition` will select columns from `x` and `y`.\n Since `y` only has one column, all columns from `y` will be identical.\n\n >>> tf.where([True, False, True],\n ... x=[[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]],\n ... y=[[100],\n ... [200],\n ... [300]]\n ... ).numpy()\n array([[ 1, 100, 3],\n [ 4, 200, 6],\n [ 7, 300, 9]], dtype=int32)\n\n Note that if the gradient of either branch of the `tf.where` generates\n a `NaN`, then the gradient of the entire `tf.where` will be `NaN`. This is\n because the gradient calculation for `tf.where` combines the two branches, for\n performance reasons.\n\n A workaround is to use an inner `tf.where` to ensure the function has\n no asymptote, and to avoid computing a value whose gradient is `NaN` by\n replacing dangerous inputs with safe inputs.\n\n Instead of this,\n\n >>> x = tf.constant(0., dtype=tf.float32)\n >>> with tf.GradientTape() as tape:\n ... tape.watch(x)\n ... y = tf.where(x < 1., 0., 1. / x)\n >>> print(tape.gradient(y, x))\n tf.Tensor(nan, shape=(), dtype=float32)\n\n Although, the `1. / x` values are never used, its gradient is a `NaN` when\n `x = 0`. Instead, we should guard that with another `tf.where`\n\n >>> x = tf.constant(0., dtype=tf.float32)\n >>> with tf.GradientTape() as tape:\n ... tape.watch(x)\n ... safe_x = tf.where(tf.equal(x, 0.), 1., x)\n ... y = tf.where(x < 1., 0., 1. / safe_x)\n >>> print(tape.gradient(y, x))\n tf.Tensor(0.0, shape=(), dtype=float32)\n\n See also:\n\n * `tf.sparse` - The indices returned by the first form of `tf.where` can be\n useful in `tf.sparse.SparseTensor` objects.\n * `tf.gather_nd`, `tf.scatter_nd`, and related ops - Given the\n list of indices returned from `tf.where` the `scatter` and `gather` family\n of ops can be used fetch values or insert values at those indices.\n * `tf.strings.length` - `tf.string` is not an allowed dtype for the\n `condition`. Use the string length instead.\n\n Args:\n condition: A `tf.Tensor` of dtype bool, or any numeric dtype. `condition`\n must have dtype `bool` when `x` and `y` are provided.\n x: If provided, a Tensor which is of the same type as `y`, and has a shape\n broadcastable with `condition` and `y`.\n y: If provided, a Tensor which is of the same type as `x`, and has a shape\n broadcastable with `condition` and `x`.\n name: A name of the operation (optional).\n\n Returns:\n If `x` and `y` are provided:\n A `Tensor` with the same type as `x` and `y`, and shape that\n is broadcast from `condition`, `x`, and `y`.\n Otherwise, a `Tensor` with shape `[tf.math.count_nonzero(condition),\n tf.rank(condition)]`.\n\n Raises:\n ValueError: When exactly one of `x` or `y` is non-None, or the shapes\n are not all broadcastable.\n "},{name:"while_loop",path:"./tf/while_loop.md",desc:"Repeat `body` while the condition `cond` is true. (deprecated argument values",type:"Functions",docs:`Repeat \`body\` while the condition \`cond\` is true. (deprecated argument values) + `},{name:"where",path:"./tf/where.md",desc:"Returns the indices of non-zero elements, or multiplexes `x` and `y`.",type:"Functions",docs:"Returns the indices of non-zero elements, or multiplexes `x` and `y`.\n\n This operation has two modes:\n\n 1. **Return the indices of non-zero elements** - When only\n `condition` is provided the result is an `int64` tensor where each row is\n the index of a non-zero element of `condition`. The result's shape\n is `[tf.math.count_nonzero(condition), tf.rank(condition)]`.\n 2. **Multiplex `x` and `y`** - When both `x` and `y` are provided the\n result has the shape of `x`, `y`, and `condition` broadcast together. The\n result is taken from `x` where `condition` is non-zero\n or `y` where `condition` is zero.\n\n #### 1. Return the indices of non-zero elements\n\n Note: In this mode `condition` can have a dtype of `bool` or any numeric\n dtype.\n\n If `x` and `y` are not provided (both are None):\n\n `tf.where` will return the indices of `condition` that are non-zero,\n in the form of a 2-D tensor with shape `[n, d]`, where `n` is the number of\n non-zero elements in `condition` (`tf.count_nonzero(condition)`), and `d` is\n the number of axes of `condition` (`tf.rank(condition)`).\n\n Indices are output in row-major order. The `condition` can have a `dtype` of\n `tf.bool`, or any numeric `dtype`.\n\n Here `condition` is a 1-axis `bool` tensor with 2 `True` values. The result\n has a shape of `[2,1]`\n\n ```>>> tf.where([True, False, False, True]).numpy()\n array([[0],\n [3]])\n\n```\n Here `condition` is a 2-axis integer tensor, with 3 non-zero values. The\n result has a shape of `[3, 2]`.\n\n ```>>> tf.where([[1, 0, 0], [1, 0, 1]]).numpy()\n array([[0, 0],\n [1, 0],\n [1, 2]])\n\n```\n Here `condition` is a 3-axis float tensor, with 5 non-zero values. The output\n shape is `[5, 3]`.\n\n ```>>> float_tensor = [[[0.1, 0], [0, 2.2], [3.5, 1e6]],\n ... [[0, 0], [0, 0], [99, 0]]]\n >>> tf.where(float_tensor).numpy()\n array([[0, 0, 0],\n [0, 1, 1],\n [0, 2, 0],\n [0, 2, 1],\n [1, 2, 0]])\n\n```\n These indices are the same that `tf.sparse.SparseTensor` would use to\n represent the condition tensor:\n\n ```>>> sparse = tf.sparse.from_dense(float_tensor)\n >>> sparse.indices.numpy()\n array([[0, 0, 0],\n [0, 1, 1],\n [0, 2, 0],\n [0, 2, 1],\n [1, 2, 0]])\n\n```\n A complex number is considered non-zero if either the real or imaginary\n component is non-zero:\n\n ```>>> tf.where([complex(0.), complex(1.), 0+1j, 1+1j]).numpy()\n array([[1],\n [2],\n [3]])\n\n```\n #### 2. Multiplex `x` and `y`\n\n Note: In this mode `condition` must have a dtype of `bool`.\n\n If `x` and `y` are also provided (both have non-None values) the `condition`\n tensor acts as a mask that chooses whether the corresponding\n element / row in the output should be taken from `x` (if the element in\n `condition` is `True`) or `y` (if it is `False`).\n\n The shape of the result is formed by\n [broadcasting](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)\n together the shapes of `condition`, `x`, and `y`.\n\n When all three inputs have the same size, each is handled element-wise.\n\n ```>>> tf.where([True, False, False, True],\n ... [1, 2, 3, 4],\n ... [100, 200, 300, 400]).numpy()\n array([ 1, 200, 300, 4], dtype=int32)\n\n```\n There are two main rules for broadcasting:\n\n 1. If a tensor has fewer axes than the others, length-1 axes are added to the\n left of the shape.\n 2. Axes with length-1 are streched to match the coresponding axes of the other\n tensors.\n\n A length-1 vector is streched to match the other vectors:\n\n ```>>> tf.where([True, False, False, True], [1, 2, 3, 4], [100]).numpy()\n array([ 1, 100, 100, 4], dtype=int32)\n\n```\n A scalar is expanded to match the other arguments:\n\n ```>>> tf.where([[True, False], [False, True]], [[1, 2], [3, 4]], 100).numpy()\n array([[ 1, 100], [100, 4]], dtype=int32)\n >>> tf.where([[True, False], [False, True]], 1, 100).numpy()\n array([[ 1, 100], [100, 1]], dtype=int32)\n\n```\n A scalar `condition` returns the complete `x` or `y` tensor, with\n broadcasting applied.\n\n ```>>> tf.where(True, [1, 2, 3, 4], 100).numpy()\n array([1, 2, 3, 4], dtype=int32)\n >>> tf.where(False, [1, 2, 3, 4], 100).numpy()\n array([100, 100, 100, 100], dtype=int32)\n\n```\n For a non-trivial example of broadcasting, here `condition` has a shape of\n `[3]`, `x` has a shape of `[3,3]`, and `y` has a shape of `[3,1]`.\n Broadcasting first expands the shape of `condition` to `[1,3]`. The final\n broadcast shape is `[3,3]`. `condition` will select columns from `x` and `y`.\n Since `y` only has one column, all columns from `y` will be identical.\n\n ```>>> tf.where([True, False, True],\n ... x=[[1, 2, 3],\n ... [4, 5, 6],\n ... [7, 8, 9]],\n ... y=[[100],\n ... [200],\n ... [300]]\n ... ).numpy()\n array([[ 1, 100, 3],\n [ 4, 200, 6],\n [ 7, 300, 9]], dtype=int32)\n\n```\n Note that if the gradient of either branch of the `tf.where` generates\n a `NaN`, then the gradient of the entire `tf.where` will be `NaN`. This is\n because the gradient calculation for `tf.where` combines the two branches, for\n performance reasons.\n\n A workaround is to use an inner `tf.where` to ensure the function has\n no asymptote, and to avoid computing a value whose gradient is `NaN` by\n replacing dangerous inputs with safe inputs.\n\n Instead of this,\n\n ```>>> x = tf.constant(0., dtype=tf.float32)\n >>> with tf.GradientTape() as tape:\n ... tape.watch(x)\n ... y = tf.where(x < 1., 0., 1. / x)\n >>> print(tape.gradient(y, x))\n tf.Tensor(nan, shape=(), dtype=float32)\n\n```\n Although, the `1. / x` values are never used, its gradient is a `NaN` when\n `x = 0`. Instead, we should guard that with another `tf.where`\n\n ```>>> x = tf.constant(0., dtype=tf.float32)\n >>> with tf.GradientTape() as tape:\n ... tape.watch(x)\n ... safe_x = tf.where(tf.equal(x, 0.), 1., x)\n ... y = tf.where(x < 1., 0., 1. / safe_x)\n >>> print(tape.gradient(y, x))\n tf.Tensor(0.0, shape=(), dtype=float32)\n\n```\n See also:\n\n * `tf.sparse` - The indices returned by the first form of `tf.where` can be\n useful in `tf.sparse.SparseTensor` objects.\n * `tf.gather_nd`, `tf.scatter_nd`, and related ops - Given the\n list of indices returned from `tf.where` the `scatter` and `gather` family\n of ops can be used fetch values or insert values at those indices.\n * `tf.strings.length` - `tf.string` is not an allowed dtype for the\n `condition`. 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Instructions for updating: @@ -7930,10 +6906,10 @@ with tf.compat.v1.Session() as sess: # meaning that the counter thread is on iteration 9996, # while the other thread is on iteration 9987 print(sess.run(out).shape) -\`\`\``},{name:"zeros",path:"./tf/zeros.md",desc:"Creates a tensor with all elements set to zero.",type:"Functions",docs:"Creates a tensor with all elements set to zero.\n\n See also `tf.zeros_like`, `tf.ones`, `tf.fill`, `tf.eye`.\n\n This operation returns a tensor of type `dtype` with shape `shape` and\n all elements set to zero.\n\n >>> tf.zeros([3, 4], tf.int32)\n \n\n Args:\n shape: A `list` of integers, a `tuple` of integers, or\n a 1-D `Tensor` of type `int32`.\n dtype: The DType of an element in the resulting `Tensor`.\n name: Optional string. A name for the operation.\n\n Returns:\n A `Tensor` with all elements set to zero.\n "},{name:"zeros_like",path:"./tf/zeros_like.md",desc:"Creates a tensor with all elements set to zero.",type:"Functions",docs:"Creates a tensor with all elements set to zero.\n\n See also `tf.zeros`.\n\n Given a single tensor or array-like object (`input`), this operation returns\n a tensor of the same type and shape as `input` with all elements set to zero.\n Optionally, you can use `dtype` to specify a new type for the returned tensor.\n\n Examples:\n\n >>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])\n >>> tf.zeros_like(tensor)\n \n\n >>> tf.zeros_like(tensor, dtype=tf.float32)\n \n\n >>> tf.zeros_like([[1, 2, 3], [4, 5, 6]])\n \n\n Args:\n input: A `Tensor` or array-like object.\n dtype: A type for the returned `Tensor`. 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P of $)if(P===` -`&&(z+=1),z>1)return!0;return!1});!g.loose&&T.length&&x&&(g.loose=!0,g.items[l].loose=!0)}return g}}html(t){const n=this.rules.block.html.exec(t);if(n){const o={type:"html",raw:n[0],pre:!this.options.sanitizer&&(n[1]==="pre"||n[1]==="script"||n[1]==="style"),text:n[0]};return this.options.sanitize&&(o.type="paragraph",o.text=this.options.sanitizer?this.options.sanitizer(n[0]):Jt(n[0]),o.tokens=[],this.lexer.inline(o.text,o.tokens)),o}}def(t){const n=this.rules.block.def.exec(t);if(n){n[3]&&(n[3]=n[3].substring(1,n[3].length-1));const o=n[1].toLowerCase().replace(/\s+/g," ");return{type:"def",tag:o,raw:n[0],href:n[2],title:n[3]}}}table(t){const n=this.rules.block.table.exec(t);if(n){const o={type:"table",header:Wf(n[1]).map(r=>({text:r})),align:n[2].replace(/^ *|\| *$/g,"").split(/ *\| */),rows:n[3]&&n[3].trim()?n[3].replace(/\n[ \t]*$/,"").split(` -`):[]};if(o.header.length===o.align.length){o.raw=n[0];let r=o.align.length,a,i,l,s;for(a=0;a({text:c}));for(r=o.header.length,i=0;i/i.test(n[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&/^<(pre|code|kbd|script)(\s|>)/i.test(n[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&/^<\/(pre|code|kbd|script)(\s|>)/i.test(n[0])&&(this.lexer.state.inRawBlock=!1),{type:this.options.sanitize?"text":"html",raw:n[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,text:this.options.sanitize?this.options.sanitizer?this.options.sanitizer(n[0]):Jt(n[0]):n[0]}}link(t){const n=this.rules.inline.link.exec(t);if(n){const o=n[2].trim();if(!this.options.pedantic&&/^$/.test(o))return;const i=Fl(o.slice(0,-1),"\\");if((o.length-i.length)%2===0)return}else{const i=z2(n[2],"()");if(i>-1){const s=(n[0].indexOf("!")===0?5:4)+n[1].length+i;n[2]=n[2].substring(0,i),n[0]=n[0].substring(0,s).trim(),n[3]=""}}let r=n[2],a="";if(this.options.pedantic){const i=/^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(r);i&&(r=i[1],a=i[3])}else a=n[3]?n[3].slice(1,-1):"";return r=r.trim(),/^$/.test(o)?r=r.slice(1):r=r.slice(1,-1)),Yf(n,{href:r&&r.replace(this.rules.inline._escapes,"$1"),title:a&&a.replace(this.rules.inline._escapes,"$1")},n[0],this.lexer)}}reflink(t,n){let o;if((o=this.rules.inline.reflink.exec(t))||(o=this.rules.inline.nolink.exec(t))){let r=(o[2]||o[1]).replace(/\s+/g," ");if(r=n[r.toLowerCase()],!r||!r.href){const a=o[0].charAt(0);return{type:"text",raw:a,text:a}}return Yf(o,r,o[0],this.lexer)}}emStrong(t,n,o=""){let r=this.rules.inline.emStrong.lDelim.exec(t);if(!r||r[3]&&o.match(/[\p{L}\p{N}]/u))return;const a=r[1]||r[2]||"";if(!a||a&&(o===""||this.rules.inline.punctuation.exec(o))){const i=r[0].length-1;let l,s,c=i,d=0;const u=r[0][0]==="*"?this.rules.inline.emStrong.rDelimAst:this.rules.inline.emStrong.rDelimUnd;for(u.lastIndex=0,n=n.slice(-1*t.length+i);(r=u.exec(n))!=null;){if(l=r[1]||r[2]||r[3]||r[4]||r[5]||r[6],!l)continue;if(s=l.length,r[3]||r[4]){c+=s;continue}else if((r[5]||r[6])&&i%3&&!((i+s)%3)){d+=s;continue}if(c-=s,c>0)continue;if(s=Math.min(s,s+c+d),Math.min(i,s)%2){const f=t.slice(1,i+r.index+s);return{type:"em",raw:t.slice(0,i+r.index+s+1),text:f,tokens:this.lexer.inlineTokens(f,[])}}const p=t.slice(2,i+r.index+s-1);return{type:"strong",raw:t.slice(0,i+r.index+s+1),text:p,tokens:this.lexer.inlineTokens(p,[])}}}}codespan(t){const n=this.rules.inline.code.exec(t);if(n){let o=n[2].replace(/\n/g," ");const r=/[^ ]/.test(o),a=/^ /.test(o)&&/ $/.test(o);return r&&a&&(o=o.substring(1,o.length-1)),o=Jt(o,!0),{type:"codespan",raw:n[0],text:o}}}br(t){const n=this.rules.inline.br.exec(t);if(n)return{type:"br",raw:n[0]}}del(t){const n=this.rules.inline.del.exec(t);if(n)return{type:"del",raw:n[0],text:n[2],tokens:this.lexer.inlineTokens(n[2],[])}}autolink(t,n){const o=this.rules.inline.autolink.exec(t);if(o){let r,a;return o[2]==="@"?(r=Jt(this.options.mangle?n(o[1]):o[1]),a="mailto:"+r):(r=Jt(o[1]),a=r),{type:"link",raw:o[0],text:r,href:a,tokens:[{type:"text",raw:r,text:r}]}}}url(t,n){let o;if(o=this.rules.inline.url.exec(t)){let r,a;if(o[2]==="@")r=Jt(this.options.mangle?n(o[0]):o[0]),a="mailto:"+r;else{let i;do i=o[0],o[0]=this.rules.inline._backpedal.exec(o[0])[0];while(i!==o[0]);r=Jt(o[0]),o[1]==="www."?a="http://"+r:a=r}return{type:"link",raw:o[0],text:r,href:a,tokens:[{type:"text",raw:r,text:r}]}}}inlineText(t,n){const o=this.rules.inline.text.exec(t);if(o){let r;return this.lexer.state.inRawBlock?r=this.options.sanitize?this.options.sanitizer?this.options.sanitizer(o[0]):Jt(o[0]):o[0]:r=Jt(this.options.smartypants?n(o[0]):o[0]),{type:"text",raw:o[0],text:r}}}}const at={newline:/^(?: *(?:\n|$))+/,code:/^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/,fences:/^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/,hr:/^ {0,3}((?:-[\t ]*){3,}|(?:_[ \t]*){3,}|(?:\*[ \t]*){3,})(?:\n+|$)/,heading:/^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/,blockquote:/^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/,list:/^( {0,3}bull)([ \t][^\n]+?)?(?:\n|$)/,html:"^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))",def:/^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/,table:_s,lheading:/^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/,_paragraph:/^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/,text:/^[^\n]+/};at._label=/(?!\s*\])(?:\\.|[^\[\]\\])+/;at._title=/(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/;at.def=Mt(at.def).replace("label",at._label).replace("title",at._title).getRegex();at.bullet=/(?:[*+-]|\d{1,9}[.)])/;at.listItemStart=Mt(/^( *)(bull) */).replace("bull",at.bullet).getRegex();at.list=Mt(at.list).replace(/bull/g,at.bullet).replace("hr","\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))").replace("def","\\n+(?="+at.def.source+")").getRegex();at._tag="address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul";at._comment=/|$)/;at.html=Mt(at.html,"i").replace("comment",at._comment).replace("tag",at._tag).replace("attribute",/ +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/).getRegex();at.paragraph=Mt(at._paragraph).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("|table","").replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.blockquote=Mt(at.blockquote).replace("paragraph",at.paragraph).getRegex();at.normal=so({},at);at.gfm=so({},at.normal,{table:"^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)"});at.gfm.table=Mt(at.gfm.table).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("blockquote"," {0,3}>").replace("code"," {4}[^\\n]").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.gfm.paragraph=Mt(at._paragraph).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("table",at.gfm.table).replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.pedantic=so({},at.normal,{html:Mt(`^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))`).replace("comment",at._comment).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:_s,paragraph:Mt(at.normal._paragraph).replace("hr",at.hr).replace("heading",` *#{1,6} *[^ -]`).replace("lheading",at.lheading).replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").getRegex()});const Xe={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:_s,tag:"^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[^*]+(?=[^*])|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/,rDelimUnd:/^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[^_]+(?=[^_])|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:_s,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\?@\\[\\]`^{|}~";Xe.punctuation=Mt(Xe.punctuation).replace(/punctuation/g,Xe._punctuation).getRegex();Xe.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g;Xe.escapedEmSt=/\\\*|\\_/g;Xe._comment=Mt(at._comment).replace("(?:-->|$)","-->").getRegex();Xe.emStrong.lDelim=Mt(Xe.emStrong.lDelim).replace(/punct/g,Xe._punctuation).getRegex();Xe.emStrong.rDelimAst=Mt(Xe.emStrong.rDelimAst,"g").replace(/punct/g,Xe._punctuation).getRegex();Xe.emStrong.rDelimUnd=Mt(Xe.emStrong.rDelimUnd,"g").replace(/punct/g,Xe._punctuation).getRegex();Xe._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g;Xe._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/;Xe._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/;Xe.autolink=Mt(Xe.autolink).replace("scheme",Xe._scheme).replace("email",Xe._email).getRegex();Xe._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/;Xe.tag=Mt(Xe.tag).replace("comment",Xe._comment).replace("attribute",Xe._attribute).getRegex();Xe._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/;Xe._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/;Xe._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/;Xe.link=Mt(Xe.link).replace("label",Xe._label).replace("href",Xe._href).replace("title",Xe._title).getRegex();Xe.reflink=Mt(Xe.reflink).replace("label",Xe._label).replace("ref",at._label).getRegex();Xe.nolink=Mt(Xe.nolink).replace("ref",at._label).getRegex();Xe.reflinkSearch=Mt(Xe.reflinkSearch,"g").replace("reflink",Xe.reflink).replace("nolink",Xe.nolink).getRegex();Xe.normal=so({},Xe);Xe.pedantic=so({},Xe.normal,{strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Mt(/^!?\[(label)\]\((.*?)\)/).replace("label",Xe._label).getRegex(),reflink:Mt(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",Xe._label).getRegex()});Xe.gfm=so({},Xe.normal,{escape:Mt(Xe.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\.5&&(o="x"+o.toString(16)),t+="&#"+o+";";return t}class Mo{constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||Da,this.options.tokenizer=this.options.tokenizer||new Du,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const n={block:at.normal,inline:Xe.normal};this.options.pedantic?(n.block=at.pedantic,n.inline=Xe.pedantic):this.options.gfm&&(n.block=at.gfm,this.options.breaks?n.inline=Xe.breaks:n.inline=Xe.gfm),this.tokenizer.rules=n}static get rules(){return{block:at,inline:Xe}}static lex(t,n){return new Mo(n).lex(t)}static lexInline(t,n){return new Mo(n).inlineTokens(t)}lex(t){t=t.replace(/\r\n|\r/g,` +`&&(z+=1),z>1)return!0;return!1});!g.loose&&T.length&&x&&(g.loose=!0,g.items[l].loose=!0)}return g}}html(t){const n=this.rules.block.html.exec(t);if(n){const o={type:"html",raw:n[0],pre:!this.options.sanitizer&&(n[1]==="pre"||n[1]==="script"||n[1]==="style"),text:n[0]};return this.options.sanitize&&(o.type="paragraph",o.text=this.options.sanitizer?this.options.sanitizer(n[0]):Jt(n[0]),o.tokens=[],this.lexer.inline(o.text,o.tokens)),o}}def(t){const n=this.rules.block.def.exec(t);if(n){n[3]&&(n[3]=n[3].substring(1,n[3].length-1));const o=n[1].toLowerCase().replace(/\s+/g," ");return{type:"def",tag:o,raw:n[0],href:n[2],title:n[3]}}}table(t){const n=this.rules.block.table.exec(t);if(n){const o={type:"table",header:Kf(n[1]).map(r=>({text:r})),align:n[2].replace(/^ *|\| *$/g,"").split(/ *\| */),rows:n[3]&&n[3].trim()?n[3].replace(/\n[ \t]*$/,"").split(` +`):[]};if(o.header.length===o.align.length){o.raw=n[0];let r=o.align.length,a,i,l,s;for(a=0;a({text:c}));for(r=o.header.length,i=0;i/i.test(n[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&/^<(pre|code|kbd|script)(\s|>)/i.test(n[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&/^<\/(pre|code|kbd|script)(\s|>)/i.test(n[0])&&(this.lexer.state.inRawBlock=!1),{type:this.options.sanitize?"text":"html",raw:n[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,text:this.options.sanitize?this.options.sanitizer?this.options.sanitizer(n[0]):Jt(n[0]):n[0]}}link(t){const n=this.rules.inline.link.exec(t);if(n){const o=n[2].trim();if(!this.options.pedantic&&/^$/.test(o))return;const i=Fl(o.slice(0,-1),"\\");if((o.length-i.length)%2===0)return}else{const i=A2(n[2],"()");if(i>-1){const s=(n[0].indexOf("!")===0?5:4)+n[1].length+i;n[2]=n[2].substring(0,i),n[0]=n[0].substring(0,s).trim(),n[3]=""}}let r=n[2],a="";if(this.options.pedantic){const i=/^([^'"]*[^\s])\s+(['"])(.*)\2/.exec(r);i&&(r=i[1],a=i[3])}else a=n[3]?n[3].slice(1,-1):"";return r=r.trim(),/^$/.test(o)?r=r.slice(1):r=r.slice(1,-1)),Uf(n,{href:r&&r.replace(this.rules.inline._escapes,"$1"),title:a&&a.replace(this.rules.inline._escapes,"$1")},n[0],this.lexer)}}reflink(t,n){let o;if((o=this.rules.inline.reflink.exec(t))||(o=this.rules.inline.nolink.exec(t))){let r=(o[2]||o[1]).replace(/\s+/g," ");if(r=n[r.toLowerCase()],!r||!r.href){const a=o[0].charAt(0);return{type:"text",raw:a,text:a}}return Uf(o,r,o[0],this.lexer)}}emStrong(t,n,o=""){let r=this.rules.inline.emStrong.lDelim.exec(t);if(!r||r[3]&&o.match(/[\p{L}\p{N}]/u))return;const a=r[1]||r[2]||"";if(!a||a&&(o===""||this.rules.inline.punctuation.exec(o))){const i=r[0].length-1;let l,s,c=i,d=0;const u=r[0][0]==="*"?this.rules.inline.emStrong.rDelimAst:this.rules.inline.emStrong.rDelimUnd;for(u.lastIndex=0,n=n.slice(-1*t.length+i);(r=u.exec(n))!=null;){if(l=r[1]||r[2]||r[3]||r[4]||r[5]||r[6],!l)continue;if(s=l.length,r[3]||r[4]){c+=s;continue}else if((r[5]||r[6])&&i%3&&!((i+s)%3)){d+=s;continue}if(c-=s,c>0)continue;if(s=Math.min(s,s+c+d),Math.min(i,s)%2){const f=t.slice(1,i+r.index+s);return{type:"em",raw:t.slice(0,i+r.index+s+1),text:f,tokens:this.lexer.inlineTokens(f,[])}}const p=t.slice(2,i+r.index+s-1);return{type:"strong",raw:t.slice(0,i+r.index+s+1),text:p,tokens:this.lexer.inlineTokens(p,[])}}}}codespan(t){const n=this.rules.inline.code.exec(t);if(n){let o=n[2].replace(/\n/g," ");const r=/[^ ]/.test(o),a=/^ /.test(o)&&/ $/.test(o);return r&&a&&(o=o.substring(1,o.length-1)),o=Jt(o,!0),{type:"codespan",raw:n[0],text:o}}}br(t){const n=this.rules.inline.br.exec(t);if(n)return{type:"br",raw:n[0]}}del(t){const n=this.rules.inline.del.exec(t);if(n)return{type:"del",raw:n[0],text:n[2],tokens:this.lexer.inlineTokens(n[2],[])}}autolink(t,n){const o=this.rules.inline.autolink.exec(t);if(o){let r,a;return o[2]==="@"?(r=Jt(this.options.mangle?n(o[1]):o[1]),a="mailto:"+r):(r=Jt(o[1]),a=r),{type:"link",raw:o[0],text:r,href:a,tokens:[{type:"text",raw:r,text:r}]}}}url(t,n){let o;if(o=this.rules.inline.url.exec(t)){let r,a;if(o[2]==="@")r=Jt(this.options.mangle?n(o[0]):o[0]),a="mailto:"+r;else{let i;do i=o[0],o[0]=this.rules.inline._backpedal.exec(o[0])[0];while(i!==o[0]);r=Jt(o[0]),o[1]==="www."?a="http://"+r:a=r}return{type:"link",raw:o[0],text:r,href:a,tokens:[{type:"text",raw:r,text:r}]}}}inlineText(t,n){const o=this.rules.inline.text.exec(t);if(o){let r;return this.lexer.state.inRawBlock?r=this.options.sanitize?this.options.sanitizer?this.options.sanitizer(o[0]):Jt(o[0]):o[0]:r=Jt(this.options.smartypants?n(o[0]):o[0]),{type:"text",raw:o[0],text:r}}}}const at={newline:/^(?: *(?:\n|$))+/,code:/^( {4}[^\n]+(?:\n(?: *(?:\n|$))*)?)+/,fences:/^ {0,3}(`{3,}(?=[^`\n]*\n)|~{3,})([^\n]*)\n(?:|([\s\S]*?)\n)(?: {0,3}\1[~`]* *(?=\n|$)|$)/,hr:/^ {0,3}((?:-[\t ]*){3,}|(?:_[ \t]*){3,}|(?:\*[ \t]*){3,})(?:\n+|$)/,heading:/^ {0,3}(#{1,6})(?=\s|$)(.*)(?:\n+|$)/,blockquote:/^( {0,3}> ?(paragraph|[^\n]*)(?:\n|$))+/,list:/^( {0,3}bull)([ \t][^\n]+?)?(?:\n|$)/,html:"^ {0,3}(?:<(script|pre|style|textarea)[\\s>][\\s\\S]*?(?:[^\\n]*\\n+|$)|comment[^\\n]*(\\n+|$)|<\\?[\\s\\S]*?(?:\\?>\\n*|$)|\\n*|$)|\\n*|$)|)[\\s\\S]*?(?:(?:\\n *)+\\n|$)|<(?!script|pre|style|textarea)([a-z][\\w-]*)(?:attribute)*? */?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))",def:/^ {0,3}\[(label)\]: *(?:\n *)?]+)>?(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/,table:_s,lheading:/^([^\n]+)\n {0,3}(=+|-+) *(?:\n+|$)/,_paragraph:/^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/,text:/^[^\n]+/};at._label=/(?!\s*\])(?:\\.|[^\[\]\\])+/;at._title=/(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/;at.def=Mt(at.def).replace("label",at._label).replace("title",at._title).getRegex();at.bullet=/(?:[*+-]|\d{1,9}[.)])/;at.listItemStart=Mt(/^( *)(bull) */).replace("bull",at.bullet).getRegex();at.list=Mt(at.list).replace(/bull/g,at.bullet).replace("hr","\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))").replace("def","\\n+(?="+at.def.source+")").getRegex();at._tag="address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul";at._comment=/|$)/;at.html=Mt(at.html,"i").replace("comment",at._comment).replace("tag",at._tag).replace("attribute",/ +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/).getRegex();at.paragraph=Mt(at._paragraph).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("|table","").replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.blockquote=Mt(at.blockquote).replace("paragraph",at.paragraph).getRegex();at.normal=so({},at);at.gfm=so({},at.normal,{table:"^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? *(?:\\| *:?-+:? *)*)(?:\\| *)?(?:\\n((?:(?! *\\n|hr|heading|blockquote|code|fences|list|html).*(?:\\n|$))*)\\n*|$)"});at.gfm.table=Mt(at.gfm.table).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("blockquote"," {0,3}>").replace("code"," {4}[^\\n]").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.gfm.paragraph=Mt(at._paragraph).replace("hr",at.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("table",at.gfm.table).replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html",")|<(?:script|pre|style|textarea|!--)").replace("tag",at._tag).getRegex();at.pedantic=so({},at.normal,{html:Mt(`^ *(?:comment *(?:\\n|\\s*$)|<(tag)[\\s\\S]+? *(?:\\n{2,}|\\s*$)|\\s]*)*?/?> *(?:\\n{2,}|\\s*$))`).replace("comment",at._comment).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:_s,paragraph:Mt(at.normal._paragraph).replace("hr",at.hr).replace("heading",` *#{1,6} *[^ +]`).replace("lheading",at.lheading).replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").getRegex()});const Xe={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:_s,tag:"^comment|^|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^|^",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[^*]+(?=[^*])|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/,rDelimUnd:/^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[^_]+(?=[^_])|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:_s,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\?@\\[\\]`^{|}~";Xe.punctuation=Mt(Xe.punctuation).replace(/punctuation/g,Xe._punctuation).getRegex();Xe.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g;Xe.escapedEmSt=/\\\*|\\_/g;Xe._comment=Mt(at._comment).replace("(?:-->|$)","-->").getRegex();Xe.emStrong.lDelim=Mt(Xe.emStrong.lDelim).replace(/punct/g,Xe._punctuation).getRegex();Xe.emStrong.rDelimAst=Mt(Xe.emStrong.rDelimAst,"g").replace(/punct/g,Xe._punctuation).getRegex();Xe.emStrong.rDelimUnd=Mt(Xe.emStrong.rDelimUnd,"g").replace(/punct/g,Xe._punctuation).getRegex();Xe._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g;Xe._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/;Xe._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/;Xe.autolink=Mt(Xe.autolink).replace("scheme",Xe._scheme).replace("email",Xe._email).getRegex();Xe._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/;Xe.tag=Mt(Xe.tag).replace("comment",Xe._comment).replace("attribute",Xe._attribute).getRegex();Xe._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/;Xe._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/;Xe._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/;Xe.link=Mt(Xe.link).replace("label",Xe._label).replace("href",Xe._href).replace("title",Xe._title).getRegex();Xe.reflink=Mt(Xe.reflink).replace("label",Xe._label).replace("ref",at._label).getRegex();Xe.nolink=Mt(Xe.nolink).replace("ref",at._label).getRegex();Xe.reflinkSearch=Mt(Xe.reflinkSearch,"g").replace("reflink",Xe.reflink).replace("nolink",Xe.nolink).getRegex();Xe.normal=so({},Xe);Xe.pedantic=so({},Xe.normal,{strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Mt(/^!?\[(label)\]\((.*?)\)/).replace("label",Xe._label).getRegex(),reflink:Mt(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",Xe._label).getRegex()});Xe.gfm=so({},Xe.normal,{escape:Mt(Xe.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\.5&&(o="x"+o.toString(16)),t+="&#"+o+";";return t}class Mo{constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||Da,this.options.tokenizer=this.options.tokenizer||new Fu,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const n={block:at.normal,inline:Xe.normal};this.options.pedantic?(n.block=at.pedantic,n.inline=Xe.pedantic):this.options.gfm&&(n.block=at.gfm,this.options.breaks?n.inline=Xe.breaks:n.inline=Xe.gfm),this.tokenizer.rules=n}static get rules(){return{block:at,inline:Xe}}static lex(t,n){return new Mo(n).lex(t)}static lexInline(t,n){return new Mo(n).inlineTokens(t)}lex(t){t=t.replace(/\r\n|\r/g,` `),this.blockTokens(t,this.tokens);let n;for(;n=this.inlineQueue.shift();)this.inlineTokens(n.src,n.tokens);return this.tokens}blockTokens(t,n=[]){this.options.pedantic?t=t.replace(/\t/g," ").replace(/^ +$/gm,""):t=t.replace(/^( *)(\t+)/gm,(l,s,c)=>s+" ".repeat(c.length));let o,r,a,i;for(;t;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some(l=>(o=l.call({lexer:this},t,n))?(t=t.substring(o.raw.length),n.push(o),!0):!1))){if(o=this.tokenizer.space(t)){t=t.substring(o.raw.length),o.raw.length===1&&n.length>0?n[n.length-1].raw+=` `:n.push(o);continue}if(o=this.tokenizer.code(t)){t=t.substring(o.raw.length),r=n[n.length-1],r&&(r.type==="paragraph"||r.type==="text")?(r.raw+=` `+o.raw,r.text+=` @@ -7954,7 +6930,7 @@ with tf.compat.v1.Session() as sess: `+o.raw,r.text+=` `+o.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=r.text):n.push(o),i=a.length!==t.length,t=t.substring(o.raw.length);continue}if(o=this.tokenizer.text(t)){t=t.substring(o.raw.length),r=n[n.length-1],r&&r.type==="text"?(r.raw+=` `+o.raw,r.text+=` -`+o.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=r.text):n.push(o);continue}if(t){const l="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(l);break}else throw new Error(l)}}return this.state.top=!0,n}inline(t,n){this.inlineQueue.push({src:t,tokens:n})}inlineTokens(t,n=[]){let o,r,a,i=t,l,s,c;if(this.tokens.links){const d=Object.keys(this.tokens.links);if(d.length>0)for(;(l=this.tokenizer.rules.inline.reflinkSearch.exec(i))!=null;)d.includes(l[0].slice(l[0].lastIndexOf("[")+1,-1))&&(i=i.slice(0,l.index)+"["+Uf("a",l[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(l=this.tokenizer.rules.inline.blockSkip.exec(i))!=null;)i=i.slice(0,l.index)+"["+Uf("a",l[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(l=this.tokenizer.rules.inline.escapedEmSt.exec(i))!=null;)i=i.slice(0,l.index)+"++"+i.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex);for(;t;)if(s||(c=""),s=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some(d=>(o=d.call({lexer:this},t,n))?(t=t.substring(o.raw.length),n.push(o),!0):!1))){if(o=this.tokenizer.escape(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.tag(t)){t=t.substring(o.raw.length),r=n[n.length-1],r&&o.type==="text"&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(o=this.tokenizer.link(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.reflink(t,this.tokens.links)){t=t.substring(o.raw.length),r=n[n.length-1],r&&o.type==="text"&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(o=this.tokenizer.emStrong(t,i,c)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.codespan(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.br(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.del(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.autolink(t,Gf)){t=t.substring(o.raw.length),n.push(o);continue}if(!this.state.inLink&&(o=this.tokenizer.url(t,Gf))){t=t.substring(o.raw.length),n.push(o);continue}if(a=t,this.options.extensions&&this.options.extensions.startInline){let d=1/0;const u=t.slice(1);let p;this.options.extensions.startInline.forEach(function(f){p=f.call({lexer:this},u),typeof p=="number"&&p>=0&&(d=Math.min(d,p))}),d<1/0&&d>=0&&(a=t.substring(0,d+1))}if(o=this.tokenizer.inlineText(a,N2)){t=t.substring(o.raw.length),o.raw.slice(-1)!=="_"&&(c=o.raw.slice(-1)),s=!0,r=n[n.length-1],r&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(t){const d="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(d);break}else throw new Error(d)}}return n}}class Lu{constructor(t){this.options=t||Da}code(t,n,o){const r=(n||"").match(/\S*/)[0];if(this.options.highlight){const a=this.options.highlight(t,r);a!=null&&a!==t&&(o=!0,t=a)}return t=t.replace(/\n$/,"")+` +`+o.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=r.text):n.push(o);continue}if(t){const l="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(l);break}else throw new Error(l)}}return this.state.top=!0,n}inline(t,n){this.inlineQueue.push({src:t,tokens:n})}inlineTokens(t,n=[]){let o,r,a,i=t,l,s,c;if(this.tokens.links){const d=Object.keys(this.tokens.links);if(d.length>0)for(;(l=this.tokenizer.rules.inline.reflinkSearch.exec(i))!=null;)d.includes(l[0].slice(l[0].lastIndexOf("[")+1,-1))&&(i=i.slice(0,l.index)+"["+Wf("a",l[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(l=this.tokenizer.rules.inline.blockSkip.exec(i))!=null;)i=i.slice(0,l.index)+"["+Wf("a",l[0].length-2)+"]"+i.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(l=this.tokenizer.rules.inline.escapedEmSt.exec(i))!=null;)i=i.slice(0,l.index)+"++"+i.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex);for(;t;)if(s||(c=""),s=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some(d=>(o=d.call({lexer:this},t,n))?(t=t.substring(o.raw.length),n.push(o),!0):!1))){if(o=this.tokenizer.escape(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.tag(t)){t=t.substring(o.raw.length),r=n[n.length-1],r&&o.type==="text"&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(o=this.tokenizer.link(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.reflink(t,this.tokens.links)){t=t.substring(o.raw.length),r=n[n.length-1],r&&o.type==="text"&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(o=this.tokenizer.emStrong(t,i,c)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.codespan(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.br(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.del(t)){t=t.substring(o.raw.length),n.push(o);continue}if(o=this.tokenizer.autolink(t,Yf)){t=t.substring(o.raw.length),n.push(o);continue}if(!this.state.inLink&&(o=this.tokenizer.url(t,Yf))){t=t.substring(o.raw.length),n.push(o);continue}if(a=t,this.options.extensions&&this.options.extensions.startInline){let d=1/0;const u=t.slice(1);let p;this.options.extensions.startInline.forEach(function(f){p=f.call({lexer:this},u),typeof p=="number"&&p>=0&&(d=Math.min(d,p))}),d<1/0&&d>=0&&(a=t.substring(0,d+1))}if(o=this.tokenizer.inlineText(a,z2)){t=t.substring(o.raw.length),o.raw.slice(-1)!=="_"&&(c=o.raw.slice(-1)),s=!0,r=n[n.length-1],r&&r.type==="text"?(r.raw+=o.raw,r.text+=o.text):n.push(o);continue}if(t){const d="Infinite loop on byte: "+t.charCodeAt(0);if(this.options.silent){console.error(d);break}else throw new Error(d)}}return n}}class Du{constructor(t){this.options=t||Da}code(t,n,o){const r=(n||"").match(/\S*/)[0];if(this.options.highlight){const a=this.options.highlight(t,r);a!=null&&a!==t&&(o=!0,t=a)}return t=t.replace(/\n$/,"")+` `,r?'
'+(o?t:Jt(t,!0))+`
`:"
"+(o?t:Jt(t,!0))+`
`}blockquote(t){return`
@@ -7974,14 +6950,14 @@ ${t}
`}tablerow(t){return` ${t} `}tablecell(t,n){const o=n.header?"th":"td";return(n.align?`<${o} align="${n.align}">`:`<${o}>`)+t+` -`}strong(t){return`${t}`}em(t){return`${t}`}codespan(t){return`${t}`}br(){return this.options.xhtml?"
":"
"}del(t){return`${t}`}link(t,n,o){if(t=Kf(this.options.sanitize,this.options.baseUrl,t),t===null)return o;let r='",r}image(t,n,o){if(t=Kf(this.options.sanitize,this.options.baseUrl,t),t===null)return o;let r=`${o}":">",r}text(t){return t}}class Qb{strong(t){return t}em(t){return t}codespan(t){return t}del(t){return t}html(t){return t}text(t){return t}link(t,n,o){return""+o}image(t,n,o){return""+o}br(){return""}}class ev{constructor(){this.seen={}}serialize(t){return t.toLowerCase().trim().replace(/<[!\/a-z].*?>/ig,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(t,n){let o=t,r=0;if(this.seen.hasOwnProperty(o)){r=this.seen[t];do r++,o=t+"-"+r;while(this.seen.hasOwnProperty(o))}return n||(this.seen[t]=r,this.seen[o]=0),o}slug(t,n={}){const o=this.serialize(t);return this.getNextSafeSlug(o,n.dryrun)}}class Ro{constructor(t){this.options=t||Da,this.options.renderer=this.options.renderer||new Lu,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new Qb,this.slugger=new ev}static parse(t,n){return new Ro(n).parse(t)}static parseInline(t,n){return new Ro(n).parseInline(t)}parse(t,n=!0){let o="",r,a,i,l,s,c,d,u,p,f,h,b,m,g,v,y,T,x,k;const $=t.length;for(r=0;r<$;r++){if(f=t[r],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[f.type]&&(k=this.options.extensions.renderers[f.type].call({parser:this},f),k!==!1||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(f.type))){o+=k||"";continue}switch(f.type){case"space":continue;case"hr":{o+=this.renderer.hr();continue}case"heading":{o+=this.renderer.heading(this.parseInline(f.tokens),f.depth,Zb(this.parseInline(f.tokens,this.textRenderer)),this.slugger);continue}case"code":{o+=this.renderer.code(f.text,f.lang,f.escaped);continue}case"table":{for(u="",d="",l=f.header.length,a=0;a0&&v.tokens[0].type==="paragraph"?(v.tokens[0].text=x+" "+v.tokens[0].text,v.tokens[0].tokens&&v.tokens[0].tokens.length>0&&v.tokens[0].tokens[0].type==="text"&&(v.tokens[0].tokens[0].text=x+" "+v.tokens[0].tokens[0].text)):v.tokens.unshift({type:"text",text:x}):g+=x),g+=this.parse(v.tokens,m),p+=this.renderer.listitem(g,T,y);o+=this.renderer.list(p,h,b);continue}case"html":{o+=this.renderer.html(f.text);continue}case"paragraph":{o+=this.renderer.paragraph(this.parseInline(f.tokens));continue}case"text":{for(p=f.tokens?this.parseInline(f.tokens):f.text;r+1<$&&t[r+1].type==="text";)f=t[++r],p+=` +`}strong(t){return`${t}`}em(t){return`${t}`}codespan(t){return`${t}`}br(){return this.options.xhtml?"
":"
"}del(t){return`${t}`}link(t,n,o){if(t=qf(this.options.sanitize,this.options.baseUrl,t),t===null)return o;let r='",r}image(t,n,o){if(t=qf(this.options.sanitize,this.options.baseUrl,t),t===null)return o;let r=`${o}":">",r}text(t){return t}}class Qb{strong(t){return t}em(t){return t}codespan(t){return t}del(t){return t}html(t){return t}text(t){return t}link(t,n,o){return""+o}image(t,n,o){return""+o}br(){return""}}class ev{constructor(){this.seen={}}serialize(t){return t.toLowerCase().trim().replace(/<[!\/a-z].*?>/ig,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(t,n){let o=t,r=0;if(this.seen.hasOwnProperty(o)){r=this.seen[t];do r++,o=t+"-"+r;while(this.seen.hasOwnProperty(o))}return n||(this.seen[t]=r,this.seen[o]=0),o}slug(t,n={}){const o=this.serialize(t);return this.getNextSafeSlug(o,n.dryrun)}}class Ro{constructor(t){this.options=t||Da,this.options.renderer=this.options.renderer||new Du,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new Qb,this.slugger=new ev}static parse(t,n){return new Ro(n).parse(t)}static parseInline(t,n){return new Ro(n).parseInline(t)}parse(t,n=!0){let o="",r,a,i,l,s,c,d,u,p,f,h,b,m,g,v,y,T,x,k;const $=t.length;for(r=0;r<$;r++){if(f=t[r],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[f.type]&&(k=this.options.extensions.renderers[f.type].call({parser:this},f),k!==!1||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(f.type))){o+=k||"";continue}switch(f.type){case"space":continue;case"hr":{o+=this.renderer.hr();continue}case"heading":{o+=this.renderer.heading(this.parseInline(f.tokens),f.depth,Zb(this.parseInline(f.tokens,this.textRenderer)),this.slugger);continue}case"code":{o+=this.renderer.code(f.text,f.lang,f.escaped);continue}case"table":{for(u="",d="",l=f.header.length,a=0;a0&&v.tokens[0].type==="paragraph"?(v.tokens[0].text=x+" "+v.tokens[0].text,v.tokens[0].tokens&&v.tokens[0].tokens.length>0&&v.tokens[0].tokens[0].type==="text"&&(v.tokens[0].tokens[0].text=x+" "+v.tokens[0].tokens[0].text)):v.tokens.unshift({type:"text",text:x}):g+=x),g+=this.parse(v.tokens,m),p+=this.renderer.listitem(g,T,y);o+=this.renderer.list(p,h,b);continue}case"html":{o+=this.renderer.html(f.text);continue}case"paragraph":{o+=this.renderer.paragraph(this.parseInline(f.tokens));continue}case"text":{for(p=f.tokens?this.parseInline(f.tokens):f.text;r+1<$&&t[r+1].type==="text";)f=t[++r],p+=` `+(f.tokens?this.parseInline(f.tokens):f.text);o+=n?this.renderer.paragraph(p):p;continue}default:{const z='Token with "'+f.type+'" type was not found.';if(this.options.silent){console.error(z);return}else throw new Error(z)}}}return o}parseInline(t,n){n=n||this.renderer;let o="",r,a,i;const l=t.length;for(r=0;r{o(l.text,l.lang,function(s,c){if(s)return a(s);c!=null&&c!==l.text&&(l.text=c,l.escaped=!0),i--,i===0&&a()})},0))}),i===0&&a();return}try{const o=Mo.lex(e,t);return t.walkTokens&&ht.walkTokens(o,t.walkTokens),Ro.parse(o,t)}catch(o){if(o.message+=` -Please report this to https://github.com/markedjs/marked.`,t.silent)return"

An error occurred:

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";throw o}}ht.options=ht.setOptions=function(e){return so(ht.defaults,e),g2(ht.defaults),ht};ht.getDefaults=Xb;ht.defaults=Da;ht.use=function(...e){const t=so({},...e),n=ht.defaults.extensions||{renderers:{},childTokens:{}};let o;e.forEach(r=>{if(r.extensions&&(o=!0,r.extensions.forEach(a=>{if(!a.name)throw new Error("extension name required");if(a.renderer){const i=n.renderers?n.renderers[a.name]:null;i?n.renderers[a.name]=function(...l){let s=a.renderer.apply(this,l);return s===!1&&(s=i.apply(this,l)),s}:n.renderers[a.name]=a.renderer}if(a.tokenizer){if(!a.level||a.level!=="block"&&a.level!=="inline")throw new Error("extension level must be 'block' or 'inline'");n[a.level]?n[a.level].unshift(a.tokenizer):n[a.level]=[a.tokenizer],a.start&&(a.level==="block"?n.startBlock?n.startBlock.push(a.start):n.startBlock=[a.start]:a.level==="inline"&&(n.startInline?n.startInline.push(a.start):n.startInline=[a.start]))}a.childTokens&&(n.childTokens[a.name]=a.childTokens)})),r.renderer){const a=ht.defaults.renderer||new Lu;for(const i in r.renderer){const l=a[i];a[i]=(...s)=>{let c=r.renderer[i].apply(a,s);return c===!1&&(c=l.apply(a,s)),c}}t.renderer=a}if(r.tokenizer){const a=ht.defaults.tokenizer||new Du;for(const i in r.tokenizer){const l=a[i];a[i]=(...s)=>{let c=r.tokenizer[i].apply(a,s);return c===!1&&(c=l.apply(a,s)),c}}t.tokenizer=a}if(r.walkTokens){const a=ht.defaults.walkTokens;t.walkTokens=function(i){r.walkTokens.call(this,i),a&&a.call(this,i)}}o&&(t.extensions=n),ht.setOptions(t)})};ht.walkTokens=function(e,t){for(const n of e)switch(t.call(ht,n),n.type){case"table":{for(const o of n.header)ht.walkTokens(o.tokens,t);for(const o of n.rows)for(const r of o)ht.walkTokens(r.tokens,t);break}case"list":{ht.walkTokens(n.items,t);break}default:ht.defaults.extensions&&ht.defaults.extensions.childTokens&&ht.defaults.extensions.childTokens[n.type]?ht.defaults.extensions.childTokens[n.type].forEach(function(o){ht.walkTokens(n[o],t)}):n.tokens&&ht.walkTokens(n.tokens,t)}};ht.parseInline=function(e,t){if(typeof e=="undefined"||e===null)throw new Error("marked.parseInline(): input parameter is undefined or null");if(typeof e!="string")throw new Error("marked.parseInline(): input parameter is of type "+Object.prototype.toString.call(e)+", string expected");t=so({},ht.defaults,t||{}),Jb(t);try{const n=Mo.lexInline(e,t);return t.walkTokens&&ht.walkTokens(n,t.walkTokens),Ro.parseInline(n,t)}catch(n){if(n.message+=` -Please report this to https://github.com/markedjs/marked.`,t.silent)return"

An error occurred:

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";throw n}};ht.Parser=Ro;ht.parser=Ro.parse;ht.Renderer=Lu;ht.TextRenderer=Qb;ht.Lexer=Mo;ht.lexer=Mo.lex;ht.Tokenizer=Du;ht.Slugger=ev;ht.parse=ht;Ro.parse;Mo.lex;var tv=(e,t)=>{const n=e.__vccOpts||e;for(const[o,r]of t)n[o]=r;return n},M2=Object.defineProperty,R2=Object.getOwnPropertyDescriptor,P2=(e,t,n,o)=>{for(var r=o>1?void 0:o?R2(t,n):t,a=e.length-1,i;a>=0;a--)(i=e[a])&&(r=(o?i(t,n,r):i(r))||r);return o&&r&&M2(t,n,r),r};function O2(e,t,n){return n.indexOf(e)===t}let wd=class extends Fu{get types(){return Hf.map(e=>e.type).filter(O2)}mounted(){YG(".api").accordion({collapsible:!0,header:".header",heightStyle:"content",active:!1})}encodeHtml(e){return e?ht(e):""}apis(e){return Hf.filter(t=>t.type==e)}};wd=P2([Gb({components:{}})],wd);const F2=wd,D2={id:"Home"},L2={class:"header clickable"},V2={class:"name"},B2=["innerHTML"],j2={class:"content"},H2=["innerHTML"];function q2(e,t,n,o,r,a){return w(),I("div",D2,[(w(!0),I(Fe,null,ct(e.types,i=>(w(),I("div",{class:"category",key:i},[M("h2",null,ve(i),1),(w(!0),I(Fe,null,ct(e.apis(i),l=>(w(),I("div",{class:"api",key:l.name},[M("div",L2,[M("span",V2,ve(l.name),1),M("span",{class:"desc",innerHTML:l.desc},null,8,B2)]),M("div",j2,[M("div",{class:"md-docstring",innerHTML:e.encodeHtml(l.docs)},null,8,H2)])]))),128))]))),128))])}var K2=tv(F2,[["render",q2],["__scopeId","data-v-743fbe10"]]),W2=Object.defineProperty,U2=Object.getOwnPropertyDescriptor,Y2=(e,t,n,o)=>{for(var r=o>1?void 0:o?U2(t,n):t,a=e.length-1,i;a>=0;a--)(i=e[a])&&(r=(o?i(t,n,r):i(r))||r);return o&&r&&W2(t,n,r),r};let xd=class extends Fu{};xd=Y2([Gb({components:{}})],xd);const G2=xd,X2=e=>(kw("data-v-bdbb760e"),e=e(),Tw(),e),Z2={id:"About"},J2=it(" This is HyDEV's Template "),Q2=X2(()=>M("div",null,"for Vue 3 + Typescript + Vite + Vue Router + SASS + Element Plus + Class Components",-1)),e3=[J2,Q2];function t3(e,t,n,o,r,a){return w(),I("div",Z2,e3)}var n3=tv(G2,[["render",t3],["__scopeId","data-v-bdbb760e"]]);/*! +Please report this to https://github.com/markedjs/marked.`,t.silent)return"

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fB=_e(uB,[["render",pB],["__file","/home/runner/work/element-plus/element-plus/packages/components/menu/src/menu-collapse-transition.vue"]]);function g_(e,t){const n=Ie("rootMenu");n||Wt("useMenu","can not inject root menu");const o=C(()=>{let i=e.parent;const l=[t.value];for(;i.type.name!=="ElMenu";)i.props.index&&l.unshift(i.props.index),i=i.parent;return l}),r=C(()=>{let i=e.parent;for(;i&&!["ElMenu","ElSubMenu"].includes(i.type.name);)i=i.parent;return i}),a=C(()=>{let i=e.parent;if(n.props.mode!=="vertical")return{};let l=20;if(n.props.collapse)l=20;else for(;i&&i.type.name!=="ElMenu";)i.type.name==="ElSubMenu"&&(l+=20),i=i.parent;return{paddingLeft:`${l}px`}});return{parentMenu:r,paddingStyle:a,indexPath:o}}function hB(e){return C(()=>{const n=e.backgroundColor;return n?new e0(n).shade(20).toString():""})}const b_=e=>C(()=>({"--el-menu-text-color":e.textColor||"","--el-menu-hover-text-color":e.textColor||"","--el-menu-bg-color":e.backgroundColor||"","--el-menu-hover-bg-color":hB(e).value||"","--el-menu-active-color":e.activeTextColor||""})),mB=Le({index:{type:String,required:!0},showTimeout:{type:Number,default:300},hideTimeout:{type:Number,default:300},popperClass:String,disabled:Boolean,popperAppendToBody:{type:Boolean,default:void 0},popperOffset:{type:Number,default:6}}),Yc="ElSubMenu";var Fp=ee({name:Yc,props:mB,setup(e,{slots:t,expose:n}){const o=nt(),{paddingStyle:r,indexPath:a,parentMenu:i}=g_(o,C(()=>e.index)),l=Ie("rootMenu");l||Wt(Yc,"can not inject root menu");const s=Ie(`subMenu:${i.value.uid}`);s||Wt(Yc,"can not inject sub menu");const c=A({}),d=A({});let u;const p=A(!1),f=A(),h=A(null),b=C(()=>O.value==="horizontal"&&g.value?"bottom-start":"right-start"),m=C(()=>O.value==="horizontal"&&g.value||O.value==="vertical"&&!l.props.collapse?Gr:Yn),g=C(()=>{let 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N,W,Y;return(Y=(W=(N=h.value)==null?void 0:N.popperRef)==null?void 0:W.popperInstanceRef)==null?void 0:Y.destroy()},V=N=>{N||F()},q=()=>{l.props.menuTrigger==="hover"&&l.props.mode==="horizontal"||l.props.collapse&&l.props.mode==="vertical"||e.disabled||l.handleSubMenuClick({index:e.index,indexPath:a.value,active:k.value})},R=(N,W=e.showTimeout)=>{var Y;N.type==="focus"&&!N.relatedTarget||l.props.menuTrigger==="click"&&l.props.mode==="horizontal"||!l.props.collapse&&l.props.mode==="vertical"||e.disabled||(s.mouseInChild.value=!0,u==null||u(),{stop:u}=jr(()=>{l.openMenu(e.index,a.value)},W),v.value&&((Y=i.value.vnode.el)==null||Y.dispatchEvent(new MouseEvent("mouseenter"))))},E=(N=!1)=>{var W,Y;l.props.menuTrigger==="click"&&l.props.mode==="horizontal"||!l.props.collapse&&l.props.mode==="vertical"||(u==null||u(),s.mouseInChild.value=!1,{stop:u}=jr(()=>!p.value&&l.closeMenu(e.index,a.value),e.hideTimeout),v.value&&N&&((W=o.parent)==null?void 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gB=Le({mode:{type:String,values:["horizontal","vertical"],default:"vertical"},defaultActive:{type:String,default:""},defaultOpeneds:{type:Te(Array),default:()=>Ft([])},uniqueOpened:Boolean,router:Boolean,menuTrigger:{type:String,values:["hover","click"],default:"hover"},collapse:Boolean,backgroundColor:String,textColor:String,activeTextColor:String,collapseTransition:{type:Boolean,default:!0},ellipsis:{type:Boolean,default:!0}}),Gc=e=>Array.isArray(e)&&e.every(t=>Qe(t)),bB={close:(e,t)=>Qe(e)&&Gc(t),open:(e,t)=>Qe(e)&&Gc(t),select:(e,t,n,o)=>Qe(e)&&Gc(t)&&ut(n)&&(o===void 0||o instanceof Promise)};var vB=ee({name:"ElMenu",props:gB,emits:bB,setup(e,{emit:t,slots:n,expose:o}){const r=nt(),a=r.appContext.config.globalProperties.$router,i=A(),l=A(e.defaultOpeneds&&!e.collapse?e.defaultOpeneds.slice(0):[]),s=A(e.defaultActive),c=A({}),d=A({}),u=C(()=>e.mode==="horizontal"||e.mode==="vertical"&&e.collapse),p=()=>{const x=s.value&&c.value[s.value];if(!x||e.mode==="horizontal"||e.collapse)return;x.indexPath.forEach($=>{const z=d.value[$];z&&f($,z.indexPath)})},f=(x,k)=>{l.value.includes(x)||(e.uniqueOpened&&(l.value=l.value.filter($=>k.includes($))),l.value.push(x),t("open",x,k))},h=(x,k)=>{const $=l.value.indexOf(x);$!==-1&&l.value.splice($,1),t("close",x,k)},b=({index:x,indexPath:k})=>{l.value.includes(x)?h(x,k):f(x,k)},m=x=>{(e.mode==="horizontal"||e.collapse)&&(l.value=[]);const{index:k,indexPath:$}=x;if(!(k===void 0||$===void 0))if(e.router&&a){const z=x.route||k,P=a.push(z).then(O=>(O||(s.value=k),O));t("select",k,$,{index:k,indexPath:$,route:z},P)}else s.value=k,t("select",k,$,{index:k,indexPath:$})},g=x=>{const k=c.value,$=k[x]||s.value&&k[s.value]||k[e.defaultActive];$?(s.value=$.index,p()):s.value=x},v=()=>{je(()=>r.proxy.$forceUpdate())};ge(()=>e.defaultActive,x=>{c.value[x]||(s.value=""),g(x)}),ge(c.value,()=>p()),ge(()=>e.collapse,x=>{x&&(l.value=[])});{const x=P=>{d.value[P.index]=P},k=P=>{delete d.value[P.index]};ot("rootMenu",vt({props:e,openedMenus:l,items:c,subMenus:d,activeIndex:s,isMenuPopup:u,addMenuItem:P=>{c.value[P.index]=P},removeMenuItem:P=>{delete c.value[P.index]},addSubMenu:x,removeSubMenu:k,openMenu:f,closeMenu:h,handleMenuItemClick:m,handleSubMenuClick:b})),ot(`subMenu:${r.uid}`,{addSubMenu:x,removeSubMenu:k,mouseInChild:A(!1)})}et(()=>{p(),e.mode==="horizontal"&&new dB(r.vnode.el)}),o({open:k=>{const{indexPath:$}=d.value[k];$.forEach(z=>f(z,$))},close:h,handleResize:v});const y=x=>{const k=Array.isArray(x)?x:[x],$=[];return k.forEach(z=>{Array.isArray(z.children)?$.push(...y(z.children)):$.push(z)}),$},T=x=>e.mode==="horizontal"?Ue(x,[[kR,v]]):x;return()=>{var x,k,$,z;let P=(k=(x=n.default)==null?void 0:x.call(n))!=null?k:[];const O=[];if(e.mode==="horizontal"&&i.value){const V=Array.from((z=($=i.value)==null?void 0:$.childNodes)!=null?z:[]).filter(Se=>Se.nodeName!=="#text"||Se.nodeValue),q=y(P),R=64,E=Number.parseInt(getComputedStyle(i.value).paddingLeft,10),N=Number.parseInt(getComputedStyle(i.value).paddingRight,10),W=i.value.clientWidth-E-N;let Y=0,J=0;V.forEach((Se,oe)=>{Y+=Se.offsetWidth||0,Y<=W-R&&(J=oe+1)});const Z=q.slice(0,J),se=q.slice(J);(se==null?void 0:se.length)&&e.ellipsis&&(P=Z,O.push(Re(Fp,{index:"sub-menu-more",class:"el-sub-menu__hide-arrow"},{title:()=>Re(qe,{class:["el-sub-menu__icon-more"]},{default:()=>Re(r5)}),default:()=>se})))}const D=b_(e),F=(V=>e.ellipsis?T(V):V)(Re("ul",{key:String(e.collapse),role:"menubar",ref:i,style:D.value,class:{"el-menu":!0,"el-menu--horizontal":e.mode==="horizontal","el-menu--collapse":e.collapse}},[...P,...O]));return e.collapseTransition&&e.mode==="vertical"?Re(fB,()=>F):F}}});const yB=Le({index:{type:Te([String,null]),default:null},route:{type:Te([String,Object])},disabled:Boolean}),_B={click:e=>Qe(e.index)&&Array.isArray(e.indexPath)},Xc="ElMenuItem",wB=ee({name:Xc,components:{ElTooltip:vn},props:yB,emits:_B,setup(e,{emit:t}){const n=nt(),o=Ie("rootMenu");o||Wt(Xc,"can not inject root menu");const{parentMenu:r,paddingStyle:a,indexPath:i}=g_(n,Yt(e,"index")),l=Ie(`subMenu:${r.value.uid}`);l||Wt(Xc,"can not inject sub menu");const s=C(()=>e.index===o.activeIndex),c=vt({index:e.index,indexPath:i,active:s}),d=()=>{e.disabled||(o.handleMenuItemClick({index:e.index,indexPath:i.value,route:e.route}),t("click",c))};return et(()=>{l.addSubMenu(c),o.addMenuItem(c)}),$t(()=>{l.removeSubMenu(c),o.removeMenuItem(c)}),{Effect:BN,parentMenu:r,rootMenu:o,paddingStyle:a,active:s,handleClick:d}}}),xB={class:"el-menu-tooltip__trigger"};function kB(e,t,n,o,r,a){const i=le("el-tooltip");return w(),I("li",{class:S(["el-menu-item",{"is-active":e.active,"is-disabled":e.disabled}]),role:"menuitem",tabindex:"-1",style:Oe(e.paddingStyle),onClick:t[0]||(t[0]=(...l)=>e.handleClick&&e.handleClick(...l))},[e.parentMenu.type.name==="ElMenu"&&e.rootMenu.props.collapse&&e.$slots.title?(w(),Q(i,{key:0,effect:e.Effect.DARK,placement:"right","fallback-placements":["left"],persistent:""},{content:K(()=>[ue(e.$slots,"title")]),default:K(()=>[M("div",xB,[ue(e.$slots,"default")])]),_:3},8,["effect"])):(w(),I(Fe,{key:1},[ue(e.$slots,"default"),ue(e.$slots,"title")],64))],6)}var v_=_e(wB,[["render",kB],["__file","/home/runner/work/element-plus/element-plus/packages/components/menu/src/menu-item.vue"]]);const TB={title:String},ag="ElMenuItemGroup",CB=ee({name:ag,props:TB,setup(){const e=nt(),t=Ie("rootMenu");return t||Wt(ag,"can not inject root menu"),{levelPadding:C(()=>{if(t.props.collapse)return 20;let 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lB=_e(aB,[["render",iB],["__file","/home/runner/work/element-plus/element-plus/packages/components/menu/src/menu-collapse-transition.vue"]]);function g_(e,t){const n=Ie("rootMenu");n||Wt("useMenu","can not inject root menu");const o=C(()=>{let i=e.parent;const l=[t.value];for(;i.type.name!=="ElMenu";)i.props.index&&l.unshift(i.props.index),i=i.parent;return l}),r=C(()=>{let i=e.parent;for(;i&&!["ElMenu","ElSubMenu"].includes(i.type.name);)i=i.parent;return i}),a=C(()=>{let i=e.parent;if(n.props.mode!=="vertical")return{};let l=20;if(n.props.collapse)l=20;else for(;i&&i.type.name!=="ElMenu";)i.type.name==="ElSubMenu"&&(l+=20),i=i.parent;return{paddingLeft:`${l}px`}});return{parentMenu:r,paddingStyle:a,indexPath:o}}function sB(e){return C(()=>{const n=e.backgroundColor;return n?new e0(n).shade(20).toString():""})}const b_=e=>C(()=>({"--el-menu-text-color":e.textColor||"","--el-menu-hover-text-color":e.textColor||"","--el-menu-bg-color":e.backgroundColor||"","--el-menu-hover-bg-color":sB(e).value||"","--el-menu-active-color":e.activeTextColor||""})),cB=Le({index:{type:String,required:!0},showTimeout:{type:Number,default:300},hideTimeout:{type:Number,default:300},popperClass:String,disabled:Boolean,popperAppendToBody:{type:Boolean,default:void 0},popperOffset:{type:Number,default:6}}),Uc="ElSubMenu";var Op=ee({name:Uc,props:cB,setup(e,{slots:t,expose:n}){const o=nt(),{paddingStyle:r,indexPath:a,parentMenu:i}=g_(o,C(()=>e.index)),l=Ie("rootMenu");l||Wt(Uc,"can not inject root menu");const s=Ie(`subMenu:${i.value.uid}`);s||Wt(Uc,"can not inject sub menu");const c=A({}),d=A({});let u;const p=A(!1),f=A(),h=A(null),b=C(()=>O.value==="horizontal"&&g.value?"bottom-start":"right-start"),m=C(()=>O.value==="horizontal"&&g.value||O.value==="vertical"&&!l.props.collapse?Gr:Yn),g=C(()=>{let 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N,W,Y;return(Y=(W=(N=h.value)==null?void 0:N.popperRef)==null?void 0:W.popperInstanceRef)==null?void 0:Y.destroy()},V=N=>{N||F()},q=()=>{l.props.menuTrigger==="hover"&&l.props.mode==="horizontal"||l.props.collapse&&l.props.mode==="vertical"||e.disabled||l.handleSubMenuClick({index:e.index,indexPath:a.value,active:k.value})},M=(N,W=e.showTimeout)=>{var Y;N.type==="focus"&&!N.relatedTarget||l.props.menuTrigger==="click"&&l.props.mode==="horizontal"||!l.props.collapse&&l.props.mode==="vertical"||e.disabled||(s.mouseInChild.value=!0,u==null||u(),{stop:u}=jr(()=>{l.openMenu(e.index,a.value)},W),v.value&&((Y=i.value.vnode.el)==null||Y.dispatchEvent(new MouseEvent("mouseenter"))))},E=(N=!1)=>{var W,Y;l.props.menuTrigger==="click"&&l.props.mode==="horizontal"||!l.props.collapse&&l.props.mode==="vertical"||(u==null||u(),s.mouseInChild.value=!1,{stop:u}=jr(()=>!p.value&&l.closeMenu(e.index,a.value),e.hideTimeout),v.value&&N&&((W=o.parent)==null?void 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dB=Le({mode:{type:String,values:["horizontal","vertical"],default:"vertical"},defaultActive:{type:String,default:""},defaultOpeneds:{type:Te(Array),default:()=>Ft([])},uniqueOpened:Boolean,router:Boolean,menuTrigger:{type:String,values:["hover","click"],default:"hover"},collapse:Boolean,backgroundColor:String,textColor:String,activeTextColor:String,collapseTransition:{type:Boolean,default:!0},ellipsis:{type:Boolean,default:!0}}),Yc=e=>Array.isArray(e)&&e.every(t=>Qe(t)),uB={close:(e,t)=>Qe(e)&&Yc(t),open:(e,t)=>Qe(e)&&Yc(t),select:(e,t,n,o)=>Qe(e)&&Yc(t)&&ut(n)&&(o===void 0||o instanceof Promise)};var pB=ee({name:"ElMenu",props:dB,emits:uB,setup(e,{emit:t,slots:n,expose:o}){const r=nt(),a=r.appContext.config.globalProperties.$router,i=A(),l=A(e.defaultOpeneds&&!e.collapse?e.defaultOpeneds.slice(0):[]),s=A(e.defaultActive),c=A({}),d=A({}),u=C(()=>e.mode==="horizontal"||e.mode==="vertical"&&e.collapse),p=()=>{const x=s.value&&c.value[s.value];if(!x||e.mode==="horizontal"||e.collapse)return;x.indexPath.forEach($=>{const z=d.value[$];z&&f($,z.indexPath)})},f=(x,k)=>{l.value.includes(x)||(e.uniqueOpened&&(l.value=l.value.filter($=>k.includes($))),l.value.push(x),t("open",x,k))},h=(x,k)=>{const $=l.value.indexOf(x);$!==-1&&l.value.splice($,1),t("close",x,k)},b=({index:x,indexPath:k})=>{l.value.includes(x)?h(x,k):f(x,k)},m=x=>{(e.mode==="horizontal"||e.collapse)&&(l.value=[]);const{index:k,indexPath:$}=x;if(!(k===void 0||$===void 0))if(e.router&&a){const z=x.route||k,P=a.push(z).then(O=>(O||(s.value=k),O));t("select",k,$,{index:k,indexPath:$,route:z},P)}else s.value=k,t("select",k,$,{index:k,indexPath:$})},g=x=>{const k=c.value,$=k[x]||s.value&&k[s.value]||k[e.defaultActive];$?(s.value=$.index,p()):s.value=x},v=()=>{je(()=>r.proxy.$forceUpdate())};ge(()=>e.defaultActive,x=>{c.value[x]||(s.value=""),g(x)}),ge(c.value,()=>p()),ge(()=>e.collapse,x=>{x&&(l.value=[])});{const x=P=>{d.value[P.index]=P},k=P=>{delete d.value[P.index]};ot("rootMenu",vt({props:e,openedMenus:l,items:c,subMenus:d,activeIndex:s,isMenuPopup:u,addMenuItem:P=>{c.value[P.index]=P},removeMenuItem:P=>{delete c.value[P.index]},addSubMenu:x,removeSubMenu:k,openMenu:f,closeMenu:h,handleMenuItemClick:m,handleSubMenuClick:b})),ot(`subMenu:${r.uid}`,{addSubMenu:x,removeSubMenu:k,mouseInChild:A(!1)})}et(()=>{p(),e.mode==="horizontal"&&new rB(r.vnode.el)}),o({open:k=>{const{indexPath:$}=d.value[k];$.forEach(z=>f(z,$))},close:h,handleResize:v});const y=x=>{const k=Array.isArray(x)?x:[x],$=[];return k.forEach(z=>{Array.isArray(z.children)?$.push(...y(z.children)):$.push(z)}),$},T=x=>e.mode==="horizontal"?Ue(x,[[bR,v]]):x;return()=>{var x,k,$,z;let P=(k=(x=n.default)==null?void 0:x.call(n))!=null?k:[];const O=[];if(e.mode==="horizontal"&&i.value){const V=Array.from((z=($=i.value)==null?void 0:$.childNodes)!=null?z:[]).filter(Se=>Se.nodeName!=="#text"||Se.nodeValue),q=y(P),M=64,E=Number.parseInt(getComputedStyle(i.value).paddingLeft,10),N=Number.parseInt(getComputedStyle(i.value).paddingRight,10),W=i.value.clientWidth-E-N;let Y=0,J=0;V.forEach((Se,oe)=>{Y+=Se.offsetWidth||0,Y<=W-M&&(J=oe+1)});const Z=q.slice(0,J),se=q.slice(J);(se==null?void 0:se.length)&&e.ellipsis&&(P=Z,O.push(Re(Op,{index:"sub-menu-more",class:"el-sub-menu__hide-arrow"},{title:()=>Re(qe,{class:["el-sub-menu__icon-more"]},{default:()=>Re(JE)}),default:()=>se})))}const D=b_(e),F=(V=>e.ellipsis?T(V):V)(Re("ul",{key:String(e.collapse),role:"menubar",ref:i,style:D.value,class:{"el-menu":!0,"el-menu--horizontal":e.mode==="horizontal","el-menu--collapse":e.collapse}},[...P,...O]));return e.collapseTransition&&e.mode==="vertical"?Re(lB,()=>F):F}}});const fB=Le({index:{type:Te([String,null]),default:null},route:{type:Te([String,Object])},disabled:Boolean}),hB={click:e=>Qe(e.index)&&Array.isArray(e.indexPath)},Gc="ElMenuItem",mB=ee({name:Gc,components:{ElTooltip:vn},props:fB,emits:hB,setup(e,{emit:t}){const n=nt(),o=Ie("rootMenu");o||Wt(Gc,"can not inject root menu");const{parentMenu:r,paddingStyle:a,indexPath:i}=g_(n,Yt(e,"index")),l=Ie(`subMenu:${r.value.uid}`);l||Wt(Gc,"can not inject sub menu");const s=C(()=>e.index===o.activeIndex),c=vt({index:e.index,indexPath:i,active:s}),d=()=>{e.disabled||(o.handleMenuItemClick({index:e.index,indexPath:i.value,route:e.route}),t("click",c))};return et(()=>{l.addSubMenu(c),o.addMenuItem(c)}),$t(()=>{l.removeSubMenu(c),o.removeMenuItem(c)}),{Effect:PN,parentMenu:r,rootMenu:o,paddingStyle:a,active:s,handleClick:d}}}),gB={class:"el-menu-tooltip__trigger"};function bB(e,t,n,o,r,a){const i=le("el-tooltip");return 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d=[];s?(s&&!s.children&&(s.children=[]),s.children.push(l),d=su(c,s)):(c.push(l),d=c),B_(d),i._columns.value=d,l.type==="selection"&&(i.selectable.value=l.selectable,i.reserveSelection.value=l.reserveSelection),e.$ready&&(e.store.updateColumns(),e.store.scheduleLayout())},removeColumn(i,l,s){const c=_(i._columns)||[];if(s)s.children.splice(s.children.findIndex(d=>d.id===l.id),1),s.children.length===0&&delete s.children,i._columns.value=su(c,s);else{const d=c.indexOf(l);d>-1&&(c.splice(d,1),i._columns.value=c)}e.$ready&&(e.store.updateColumns(),e.store.scheduleLayout())},sort(i,l){const{prop:s,order:c,init:d}=l;if(s){const u=_(i.columns).find(p=>p.property===s);u&&(u.order=c,e.store.updateSort(u,s,c),e.store.commit("changeSortCondition",{init:d}))}},changeSortCondition(i,l){const{sortingColumn:s,sortProp:c,sortOrder:d}=i;_(d)===null&&(i.sortingColumn.value=null,i.sortProp.value=null);const u={filter:!0};e.store.execQuery(u),(!l||!(l.silent||l.init))&&e.emit("sort-change",{column:_(s),prop:_(c),order:_(d)}),e.store.updateTableScrollY()},filterChange(i,l){const{column:s,values:c,silent:d}=l,u=e.store.updateFilters(s,c);e.store.execQuery(),d||e.emit("filter-change",u),e.store.updateTableScrollY()},toggleAllSelection(){e.store.toggleAllSelection()},rowSelectedChanged(i,l){e.store.toggleRowSelection(l),e.store.updateAllSelected()},setHoverRow(i,l){i.hoverRow.value=l},setCurrentRow(i,l){e.store.updateCurrentRow(l)}},r=function(i,...l){const s=e.store.mutations;if(s[i])s[i].apply(e,[e.store.states].concat(l));else throw new Error(`Action not found: ${i}`)},a=function(){je(()=>e.layout.updateScrollY.apply(e.layout))};return ze(he({ns:n},t),{mutations:o,commit:r,updateTableScrollY:a})}const Ti={rowKey:"rowKey",defaultExpandAll:"defaultExpandAll",selectOnIndeterminate:"selectOnIndeterminate",indent:"indent",lazy:"lazy",data:"data",["treeProps.hasChildren"]:{key:"lazyColumnIdentifier",default:"hasChildren"},["treeProps.children"]:{key:"childrenColumnName",default:"children"}};function SK(e,t){if(!e)throw new Error("Table is required.");const n=CK();return n.toggleAllSelection=bn(n._toggleAllSelection,10),Object.keys(Ti).forEach(o=>{j_(H_(t,o),o,n)}),$K(n,t),n}function $K(e,t){Object.keys(Ti).forEach(n=>{ge(()=>H_(t,n),o=>{j_(o,n,e)})})}function j_(e,t,n){let o=e,r=Ti[t];typeof Ti[t]=="object"&&(r=r.key,o=o||Ti[t].default),n.states[r].value=o}function H_(e,t){if(t.includes(".")){const n=t.split(".");let o=e;return n.forEach(r=>{o=o[r]}),o}else return e[t]}class AK{constructor(t){this.observers=[],this.table=null,this.store=null,this.columns=[],this.fit=!0,this.showHeader=!0,this.height=A(null),this.scrollX=A(!1),this.scrollY=A(!1),this.bodyWidth=A(null),this.fixedWidth=A(null),this.rightFixedWidth=A(null),this.tableHeight=A(null),this.headerHeight=A(44),this.appendHeight=A(0),this.footerHeight=A(44),this.viewportHeight=A(null),this.bodyHeight=A(null),this.bodyScrollHeight=A(0),this.fixedBodyHeight=A(null),this.gutterWidth=0;for(const n in t)lt(t,n)&&(Vt(this[n])?this[n].value=t[n]:this[n]=t[n]);if(!this.table)throw new Error("Table is required for Table Layout");if(!this.store)throw new Error("Store is required for Table Layout")}updateScrollY(){if(this.height.value===null)return!1;const n=this.table.refs.bodyWrapper;if(this.table.vnode.el&&n){let o=!0;const r=this.scrollY.value;return this.bodyHeight.value===null?o=!1:o=n.scrollHeight>this.bodyHeight.value,this.scrollY.value=o,r!==o}return!1}setHeight(t,n="height"){if(!dt)return;const o=this.table.vnode.el;if(t=lu(t),this.height.value=Number(t),!o&&(t||t===0))return je(()=>this.setHeight(t,n));typeof t=="number"?(o.style[n]=`${t}px`,this.updateElsHeight()):typeof t=="string"&&(o.style[n]=t,this.updateElsHeight())}setMaxHeight(t){this.setHeight(t,"max-height")}getFlattenColumns(){const t=[];return this.table.store.states.columns.value.forEach(o=>{o.isColumnGroup?t.push.apply(t,o.columns):t.push(o)}),t}updateElsHeight(){var t,n;if(!this.table.$ready)return je(()=>this.updateElsHeight());const{tableWrapper:o,headerWrapper:r,appendWrapper:a,footerWrapper:i,tableHeader:l,tableBody:s}=this.table.refs;if(o&&o.style.display==="none")return;const{tableLayout:c}=this.table.props;if(this.appendHeight.value=a?a.offsetHeight:0,this.showHeader&&!r&&c==="fixed")return;const d=l||null,u=this.headerDisplayNone(d),p=(r==null?void 0:r.offsetHeight)||0,f=this.headerHeight.value=this.showHeader?p:0;if(this.showHeader&&!u&&p>0&&(this.table.store.states.columns.value||[]).length>0&&f<2)return je(()=>this.updateElsHeight());const h=this.tableHeight.value=(n=(t=this.table)==null?void 0:t.vnode.el)==null?void 0:n.clientHeight,b=this.footerHeight.value=i?i.offsetHeight:0;this.height.value!==null&&(this.bodyHeight.value===null&&requestAnimationFrame(()=>this.updateElsHeight()),this.bodyHeight.value=h-f-b+(i?1:0),this.bodyScrollHeight.value=s==null?void 0:s.scrollHeight),this.fixedBodyHeight.value=this.scrollX.value?this.bodyHeight.value-this.gutterWidth:this.bodyHeight.value,this.viewportHeight.value=this.scrollX.value?h-this.gutterWidth:h,this.updateScrollY(),this.notifyObservers("scrollable")}headerDisplayNone(t){if(!t)return!0;let n=t;for(;n.tagName!=="DIV";){if(getComputedStyle(n).display==="none")return!0;n=n.parentElement}return!1}updateColumnsWidth(){if(!dt)return;const t=this.fit,n=this.table.vnode.el.clientWidth;let o=0;const r=this.getFlattenColumns(),a=r.filter(s=>typeof s.width!="number");if(r.forEach(s=>{typeof s.width=="number"&&s.realWidth&&(s.realWidth=null)}),a.length>0&&t){if(r.forEach(s=>{o+=Number(s.width||s.minWidth||80)}),o<=n){this.scrollX.value=!1;const s=n-o;if(a.length===1)a[0].realWidth=Number(a[0].minWidth||80)+s;else{const c=a.reduce((p,f)=>p+Number(f.minWidth||80),0),d=s/c;let u=0;a.forEach((p,f)=>{if(f===0)return;const h=Math.floor(Number(p.minWidth||80)*d);u+=h,p.realWidth=Number(p.minWidth||80)+h}),a[0].realWidth=Number(a[0].minWidth||80)+s-u}}else this.scrollX.value=!0,a.forEach(s=>{s.realWidth=Number(s.minWidth)});this.bodyWidth.value=Math.max(o,n),this.table.state.resizeState.value.width=this.bodyWidth.value}else r.forEach(s=>{!s.width&&!s.minWidth?s.realWidth=80:s.realWidth=Number(s.width||s.minWidth),o+=s.realWidth}),this.scrollX.value=o>n,this.bodyWidth.value=o;const i=this.store.states.fixedColumns.value;if(i.length>0){let s=0;i.forEach(c=>{s+=Number(c.realWidth||c.width)}),this.fixedWidth.value=s}const l=this.store.states.rightFixedColumns.value;if(l.length>0){let s=0;l.forEach(c=>{s+=Number(c.realWidth||c.width)}),this.rightFixedWidth.value=s}this.notifyObservers("columns")}addObserver(t){this.observers.push(t)}removeObserver(t){const n=this.observers.indexOf(t);n!==-1&&this.observers.splice(n,1)}notifyObservers(t){this.observers.forEach(o=>{var r,a;switch(t){case"columns":(r=o.state)==null||r.onColumnsChange(this);break;case"scrollable":(a=o.state)==null||a.onScrollableChange(this);break;default:throw new Error(`Table Layout don't have event 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e.column?e.column.filteredValue||[]:[]},set(T){e.column&&e.upDataColumn("filteredValue",T)}}),d=C(()=>e.column?e.column.filterMultiple:!0),u=T=>T.value===s.value,p=()=>{a.value=!1},f=T=>{T.stopPropagation(),a.value=!a.value},h=()=>{a.value=!1},b=()=>{v(c.value),p()},m=()=>{c.value=[],v(c.value),p()},g=T=>{s.value=T,v(typeof T!="undefined"&&T!==null?c.value:[]),p()},v=T=>{e.store.commit("filterChange",{column:e.column,values:T}),e.store.updateAllSelected()};ge(a,T=>{e.column&&e.upDataColumn("filterOpened",T)},{immediate:!0});const y=C(()=>{var T,x;return(x=(T=i.value)==null?void 0:T.popperRef)==null?void 0:x.contentRef});return{tooltipVisible:a,multiple:d,filteredValue:c,filterValue:s,filters:l,handleConfirm:b,handleReset:m,handleSelect:g,isActive:u,t:n,ns:o,showFilterPanel:f,hideFilterPanel:h,popperPaneRef:y,tooltip:i}}}),IK={key:0},NK=["disabled"],MK=["label","onClick"];function RK(e,t,n,o,r,a){const i=le("el-checkbox"),l=le("el-checkbox-group"),s=le("el-scrollbar"),c=le("arrow-up"),d=le("arrow-down"),u=le("el-icon"),p=le("el-tooltip"),f=In("click-outside");return w(),Q(p,{ref:"tooltip",visible:e.tooltipVisible,"onUpdate:visible":t[5]||(t[5]=h=>e.tooltipVisible=h),offset:0,placement:e.placement,"show-arrow":!1,"stop-popper-mouse-event":!1,"append-to-body":"",effect:"light",pure:"","popper-class":e.ns.b(),persistent:""},{content:K(()=>[e.multiple?(w(),I("div",IK,[M("div",{class:S(e.ns.e("content"))},[U(s,{"wrap-class":e.ns.e("wrap")},{default:K(()=>[U(l,{modelValue:e.filteredValue,"onUpdate:modelValue":t[0]||(t[0]=h=>e.filteredValue=h),class:S(e.ns.e("checkbox-group"))},{default:K(()=>[(w(!0),I(Fe,null,ct(e.filters,h=>(w(),Q(i,{key:h.value,label:h.value},{default:K(()=>[it(ve(h.text),1)]),_:2},1032,["label"]))),128))]),_:1},8,["modelValue","class"])]),_:1},8,["wrap-class"])],2),M("div",{class:S(e.ns.e("bottom"))},[M("button",{class:S({[e.ns.is("disabled")]:e.filteredValue.length===0}),disabled:e.filteredValue.length===0,type:"button",onClick:t[1]||(t[1]=(...h)=>e.handleConfirm&&e.handleConfirm(...h))},ve(e.t("el.table.confirmFilter")),11,NK),M("button",{type:"button",onClick:t[2]||(t[2]=(...h)=>e.handleReset&&e.handleReset(...h))},ve(e.t("el.table.resetFilter")),1)],2)])):(w(),I("ul",{key:1,class:S(e.ns.e("list"))},[M("li",{class:S([e.ns.e("list-item"),{[e.ns.is("active")]:e.filterValue===void 0||e.filterValue===null}]),onClick:t[3]||(t[3]=h=>e.handleSelect(null))},ve(e.t("el.table.clearFilter")),3),(w(!0),I(Fe,null,ct(e.filters,h=>(w(),I("li",{key:h.value,class:S([e.ns.e("list-item"),e.ns.is("active",e.isActive(h))]),label:h.value,onClick:b=>e.handleSelect(h.value)},ve(h.text),11,MK))),128))],2))]),default:K(()=>[Ue((w(),I("span",{class:S([`${e.ns.namespace.value}-table__column-filter-trigger`,`${e.ns.namespace.value}-none-outline`]),onClick:t[4]||(t[4]=(...h)=>e.showFilterPanel&&e.showFilterPanel(...h))},[U(u,null,{default:K(()=>[e.column.filterOpened?(w(),Q(c,{key:0})):(w(),Q(d,{key:1}))]),_:1})],2)),[[f,e.hideFilterPanel,e.popperPaneRef]])]),_:1},8,["visible","placement","popper-class"])}var PK=_e(zK,[["render",RK],["__file","/home/runner/work/element-plus/element-plus/packages/components/table/src/filter-panel.vue"]]);function q_(e){const t=nt();Ji(()=>{n.value.addObserver(t)}),et(()=>{o(n.value),r(n.value)}),Un(()=>{o(n.value),r(n.value)}),Qi(()=>{n.value.removeObserver(t)});const n=C(()=>{const a=e.layout;if(!a)throw new Error("Can not find table layout.");return a}),o=a=>{var i;const l=((i=e.vnode.el)==null?void 0:i.querySelectorAll("colgroup > col"))||[];if(!l.length)return;const s=a.getFlattenColumns(),c={};s.forEach(d=>{c[d.id]=d});for(let d=0,u=l.length;d{var i,l;const s=((i=e.vnode.el)==null?void 0:i.querySelectorAll("colgroup > col[name=gutter]"))||[];for(let d=0,u=s.length;d{b.stopPropagation()},a=(b,m)=>{!m.filters&&m.sortable?h(b,m,!1):m.filterable&&!m.sortable&&r(b),o==null||o.emit("header-click",m,b)},i=(b,m)=>{o==null||o.emit("header-contextmenu",m,b)},l=A(null),s=A(!1),c=A({}),d=(b,m)=>{if(!!dt&&!(m.children&&m.children.length>0)&&l.value&&e.border){s.value=!0;const g=o;t("set-drag-visible",!0);const y=(g==null?void 0:g.vnode.el).getBoundingClientRect().left,T=n.vnode.el.querySelector(`th.${m.id}`),x=T.getBoundingClientRect(),k=x.left-y+30;bo(T,"noclick"),c.value={startMouseLeft:b.clientX,startLeft:x.right-y,startColumnLeft:x.left-y,tableLeft:y};const $=g==null?void 0:g.refs.resizeProxy;$.style.left=`${c.value.startLeft}px`,document.onselectstart=function(){return!1},document.ondragstart=function(){return!1};const z=O=>{const D=O.clientX-c.value.startMouseLeft,L=c.value.startLeft+D;$.style.left=`${Math.max(k,L)}px`},P=()=>{if(s.value){const{startColumnLeft:O,startLeft:D}=c.value,F=Number.parseInt($.style.left,10)-O;m.width=m.realWidth=F,g==null||g.emit("header-dragend",m.width,D-O,m,b),requestAnimationFrame(()=>{e.store.scheduleLayout(!1,!0)}),document.body.style.cursor="",s.value=!1,l.value=null,c.value={},t("set-drag-visible",!1)}document.removeEventListener("mousemove",z),document.removeEventListener("mouseup",P),document.onselectstart=null,document.ondragstart=null,setTimeout(()=>{hn(T,"noclick")},0)};document.addEventListener("mousemove",z),document.addEventListener("mouseup",P)}},u=(b,m)=>{if(m.children&&m.children.length>0)return;let g=b.target;for(;g&&g.tagName!=="TH";)g=g.parentNode;if(!(!m||!m.resizable)&&!s.value&&e.border){const v=g.getBoundingClientRect(),y=document.body.style;v.width>12&&v.right-b.pageX<8?(y.cursor="col-resize",oo(g,"is-sortable")&&(g.style.cursor="col-resize"),l.value=m):s.value||(y.cursor="",oo(g,"is-sortable")&&(g.style.cursor="pointer"),l.value=null)}},p=()=>{!dt||(document.body.style.cursor="")},f=({order:b,sortOrders:m})=>{if(b==="")return m[0];const g=m.indexOf(b||null);return m[g>m.length-2?0:g+1]},h=(b,m,g)=>{b.stopPropagation();const v=m.order===g?null:g||f(m);let y=b.target;for(;y&&y.tagName!=="TH";)y=y.parentNode;if(y&&y.tagName==="TH"&&oo(y,"noclick")){hn(y,"noclick");return}if(!m.sortable)return;const T=e.store.states;let x=T.sortProp.value,k;const $=T.sortingColumn.value;($!==m||$===m&&$.order===null)&&($&&($.order=null),T.sortingColumn.value=m,x=m.property),v?k=m.order=v:k=m.order=null,T.sortProp.value=x,T.sortOrder.value=k,o==null||o.store.commit("changeSortCondition")};return{handleHeaderClick:a,handleHeaderContextMenu:i,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:h,handleFilterClick:r}}function FK(e){const t=Ie(xo),n=ke("table");return{getHeaderRowStyle:l=>{const s=t==null?void 0:t.props.headerRowStyle;return typeof s=="function"?s.call(null,{rowIndex:l}):s},getHeaderRowClass:l=>{const s=[],c=t==null?void 0:t.props.headerRowClassName;return typeof c=="string"?s.push(c):typeof c=="function"&&s.push(c.call(null,{rowIndex:l})),s.join(" ")},getHeaderCellStyle:(l,s,c,d)=>{var u;let p=(u=t==null?void 0:t.props.headerCellStyle)!=null?u:{};typeof p=="function"&&(p=p.call(null,{rowIndex:l,columnIndex:s,row:c,column:d}));const f=d.isSubColumn?null:Hp(s,d.fixed,e.store,c);return Ra(f,"left"),Ra(f,"right"),Object.assign({},p,f)},getHeaderCellClass:(l,s,c,d)=>{const u=d.isSubColumn?[]:jp(n.b(),s,d.fixed,e.store,c),p=[d.id,d.order,d.headerAlign,d.className,d.labelClassName,...u];d.children||p.push("is-leaf"),d.sortable&&p.push("is-sortable");const f=t==null?void 0:t.props.headerCellClassName;return typeof f=="string"?p.push(f):typeof f=="function"&&p.push(f.call(null,{rowIndex:l,columnIndex:s,row:c,column:d})),p.push(n.e("cell")),p.filter(h=>Boolean(h)).join(" ")}}}const K_=e=>{const t=[];return e.forEach(n=>{n.children?(t.push(n),t.push.apply(t,K_(n.children))):t.push(n)}),t},DK=e=>{let t=1;const n=(a,i)=>{if(i&&(a.level=i.level+1,t{n(s,a),l+=s.colSpan}),a.colSpan=l}else a.colSpan=1};e.forEach(a=>{a.level=1,n(a,void 0)});const o=[];for(let a=0;a{a.children?(a.rowSpan=1,a.children.forEach(i=>i.isSubColumn=!0)):a.rowSpan=t-a.level+1,o[a.level-1].push(a)}),o};function LK(e){const t=Ie(xo),n=C(()=>DK(e.store.states.originColumns.value));return{isGroup:C(()=>{const a=n.value.length>1;return a&&t&&(t.state.isGroup.value=!0),a}),toggleAllSelection:a=>{a.stopPropagation(),t==null||t.store.commit("toggleAllSelection")},columnRows:n}}var VK=ee({name:"ElTableHeader",components:{ElCheckbox:lo},props:{fixed:{type:String,default:""},store:{required:!0,type:Object},border:Boolean,defaultSort:{type:Object,default:()=>({prop:"",order:""})}},setup(e,{emit:t}){const n=nt(),o=Ie(xo),r=ke("table"),a=A({}),{onColumnsChange:i,onScrollableChange:l}=q_(o);et(async()=>{await je(),await je();const{prop:k,order:$}=e.defaultSort;o==null||o.store.commit("sort",{prop:k,order:$,init:!0})});const{handleHeaderClick:s,handleHeaderContextMenu:c,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:f,handleFilterClick:h}=OK(e,t),{getHeaderRowStyle:b,getHeaderRowClass:m,getHeaderCellStyle:g,getHeaderCellClass:v}=FK(e),{isGroup:y,toggleAllSelection:T,columnRows:x}=LK(e);return n.state={onColumnsChange:i,onScrollableChange:l},n.filterPanels=a,{ns:r,filterPanels:a,onColumnsChange:i,onScrollableChange:l,columnRows:x,getHeaderRowClass:m,getHeaderRowStyle:b,getHeaderCellClass:v,getHeaderCellStyle:g,handleHeaderClick:s,handleHeaderContextMenu:c,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:f,handleFilterClick:h,isGroup:y,toggleAllSelection:T}},render(){const{ns:e,isGroup:t,columnRows:n,getHeaderCellStyle:o,getHeaderCellClass:r,getHeaderRowClass:a,getHeaderRowStyle:i,handleHeaderClick:l,handleHeaderContextMenu:s,handleMouseDown:c,handleMouseMove:d,handleSortClick:u,handleMouseOut:p,store:f,$parent:h}=this;let b=1;return Re("thead",{class:{[e.is("group")]:t}},n.map((m,g)=>Re("tr",{class:a(g),key:g,style:i(g)},m.map((v,y)=>(v.rowSpan>b&&(b=v.rowSpan),Re("th",{class:r(g,y,m,v),colspan:v.colSpan,key:`${v.id}-thead`,rowspan:v.rowSpan,style:o(g,y,m,v),onClick:T=>l(T,v),onContextmenu:T=>s(T,v),onMousedown:T=>c(T,v),onMousemove:T=>d(T,v),onMouseout:p},[Re("div",{class:["cell",v.filteredValue&&v.filteredValue.length>0?"highlight":"",v.labelClassName]},[v.renderHeader?v.renderHeader({column:v,$index:y,store:f,_self:h}):v.label,v.sortable&&Re("span",{onClick:T=>u(T,v),class:"caret-wrapper"},[Re("i",{onClick:T=>u(T,v,"ascending"),class:"sort-caret ascending"}),Re("i",{onClick:T=>u(T,v,"descending"),class:"sort-caret descending"})]),v.filterable&&Re(PK,{store:f,placement:v.filterPlacement||"bottom-start",column:v,upDataColumn:(T,x)=>{v[T]=x}})])]))))))}});function BK(e){const t=Ie(xo),n=A(""),o=A(Re("div")),r=(p,f,h)=>{var b;const m=t,g=Qc(p);let v;const y=(b=m==null?void 0:m.vnode.el)==null?void 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p=="function"&&u.push(p.call(null,{row:c,rowIndex:d})),u},getCellStyle:(c,d,u,p)=>{const f=t==null?void 0:t.props.cellStyle;let h=f!=null?f:{};typeof f=="function"&&(h=f.call(null,{rowIndex:c,columnIndex:d,row:u,column:p}));const b=p.isSubColumn?null:Hp(d,e==null?void 0:e.fixed,e.store);return Ra(b,"left"),Ra(b,"right"),Object.assign({},h,b)},getCellClass:(c,d,u,p)=>{const f=p.isSubColumn?[]:jp(n.b(),d,e==null?void 0:e.fixed,e.store),h=[p.id,p.align,p.className,...f],b=t==null?void 0:t.props.cellClassName;return typeof b=="string"?h.push(b):typeof b=="function"&&h.push(b.call(null,{rowIndex:c,columnIndex:d,row:u,column:p})),h.push(n.e("cell")),h.filter(m=>Boolean(m)).join(" ")},getSpan:(c,d,u,p)=>{let f=1,h=1;const b=t==null?void 0:t.props.spanMethod;if(typeof b=="function"){const m=b({row:c,column:d,rowIndex:u,columnIndex:p});Array.isArray(m)?(f=m[0],h=m[1]):typeof m=="object"&&(f=m.rowspan,h=m.colspan)}return{rowspan:f,colspan:h}},getColspanRealWidth:(c,d,u)=>{if(d<1)return 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YK=ee({name:"ElTableFooter",props:{fixed:{type:String,default:""},store:{required:!0,type:Object},summaryMethod:Function,sumText:String,border:Boolean,defaultSort:{type:Object,default:()=>({prop:"",order:""})}},setup(e){const{getCellClasses:t,getCellStyles:n,columns:o}=UK(e);return{ns:ke("table"),getCellClasses:t,getCellStyles:n,columns:o}},render(){const{columns:e,getCellStyles:t,getCellClasses:n,summaryMethod:o,sumText:r,ns:a}=this,i=this.store.states.data.value;let l=[];return o?l=o({columns:e,data:i}):e.forEach((s,c)=>{if(c===0){l[c]=r;return}const d=i.map(h=>Number(h[s.property])),u=[];let p=!0;d.forEach(h=>{if(!Number.isNaN(+h)){p=!1;const b=`${h}`.split(".")[1];u.push(b?b.length:0)}});const f=Math.max.apply(null,u);p?l[c]="":l[c]=d.reduce((h,b)=>{const m=Number(b);return Number.isNaN(+m)?h:Number.parseFloat((h+b).toFixed(Math.min(f,20)))},0)}),Re("table",{class:a.e("footer"),cellspacing:"0",cellpadding:"0",border:"0"},[qp({columns:e}),Re("tbody",[Re("tr",{},[...e.map((s,c)=>Re("td",{key:c,colspan:s.colSpan,rowspan:s.rowSpan,class:n(e,c),style:t(s,c)},[Re("div",{class:["cell",s.labelClassName]},[l[c]])]))])])])}});function GK(e){return{setCurrentRow:d=>{e.commit("setCurrentRow",d)},getSelectionRows:()=>e.getSelectionRows(),toggleRowSelection:(d,u)=>{e.toggleRowSelection(d,u,!1),e.updateAllSelected()},clearSelection:()=>{e.clearSelection()},clearFilter:d=>{e.clearFilter(d)},toggleAllSelection:()=>{e.commit("toggleAllSelection")},toggleRowExpansion:(d,u)=>{e.toggleRowExpansionAdapter(d,u)},clearSort:()=>{e.clearSort()},sort:(d,u)=>{e.commit("sort",{prop:d,order:u})}}}function XK(e,t,n,o){const r=A(!1),a=A(null),i=A(!1),l=N=>{i.value=N},s=A({width:null,height:null}),c=A(!1),d={display:"inline-block",verticalAlign:"middle"},u=A();Dn(()=>{t.setHeight(e.height)}),Dn(()=>{t.setMaxHeight(e.maxHeight)}),ge(()=>[e.currentRowKey,n.states.rowKey],([N,W])=>{!_(W)||n.setCurrentRowKey(`${N}`)},{immediate:!0}),ge(()=>e.data,N=>{o.store.commit("setData",N)},{immediate:!0,deep:!0}),Dn(()=>{e.expandRowKeys&&n.setExpandRowKeysAdapter(e.expandRowKeys)});const p=()=>{o.store.commit("setHoverRow",null),o.hoverState&&(o.hoverState=null)},f=(N,W)=>{const{pixelX:Y,pixelY:J}=W;Math.abs(Y)>=Math.abs(J)&&(o.refs.bodyWrapper.scrollLeft+=W.pixelX/5)},h=C(()=>e.height||e.maxHeight||n.states.fixedColumns.value.length>0||n.states.rightFixedColumns.value.length>0),b=C(()=>({width:t.bodyWidth.value?`${t.bodyWidth.value}px`:""})),m=()=>{h.value&&t.updateElsHeight(),t.updateColumnsWidth(),requestAnimationFrame(T)};et(async()=>{await je(),n.updateColumns(),x(),requestAnimationFrame(m),s.value={width:u.value=o.vnode.el.offsetWidth,height:o.vnode.el.offsetHeight},n.states.columns.value.forEach(N=>{N.filteredValue&&N.filteredValue.length&&o.store.commit("filterChange",{column:N,values:N.filteredValue,silent:!0})}),o.$ready=!0});const g=(N,W)=>{if(!N)return;const Y=Array.from(N.classList).filter(J=>!J.startsWith("is-scrolling-"));Y.push(t.scrollX.value?W:"is-scrolling-none"),N.className=Y.join(" ")},v=N=>{const{tableWrapper:W}=o.refs;g(W,N)},y=N=>{const{tableWrapper:W}=o.refs;return!!(W&&W.classList.contains(N))},T=function(){if(!o.refs.scrollBarRef)return;if(!t.scrollX.value){const oe="is-scrolling-none";y(oe)||v(oe);return}const N=o.refs.scrollBarRef.wrap$;if(!N)return;const{scrollLeft:W,offsetWidth:Y,scrollWidth:J}=N,{headerWrapper:Z,footerWrapper:se}=o.refs;Z&&(Z.scrollLeft=W),se&&(se.scrollLeft=W);const Se=J-Y-1;W>=Se?v("is-scrolling-right"):v(W===0?"is-scrolling-left":"is-scrolling-middle")},x=()=>{var 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ZK={data:{type:Array,default:()=>[]},size:String,width:[String,Number],height:[String,Number],maxHeight:[String,Number],fit:{type:Boolean,default:!0},stripe:Boolean,border:Boolean,rowKey:[String,Function],showHeader:{type:Boolean,default:!0},showSummary:Boolean,sumText:String,summaryMethod:Function,rowClassName:[String,Function],rowStyle:[Object,Function],cellClassName:[String,Function],cellStyle:[Object,Function],headerRowClassName:[String,Function],headerRowStyle:[Object,Function],headerCellClassName:[String,Function],headerCellStyle:[Object,Function],highlightCurrentRow:Boolean,currentRowKey:[String,Number],emptyText:String,expandRowKeys:Array,defaultExpandAll:Boolean,defaultSort:Object,tooltipEffect:String,spanMethod:Function,selectOnIndeterminate:{type:Boolean,default:!0},indent:{type:Number,default:16},treeProps:{type:Object,default:()=>({hasChildren:"hasChildren",children:"children"})},lazy:Boolean,load:Function,style:{type:Object,default:()=>({})},className:{type:String,default:""},tableLayout:{type:String,default:"fixed"},scrollbarAlwaysOn:{type:Boolean,default:!1}};const 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n=ke("tree"),{broadcastExpanded:o}=e1(e),r=Ie("RootTree"),a=A(!1),i=A(!1),l=A(null),s=A(null),c=A(null),d=Ie(t1),u=nt();ot("NodeInstance",u),e.node.expanded&&(a.value=!0,i.value=!0);const p=r.props.children||"children";ge(()=>{const P=e.node.data[p];return P&&[...P]},()=>{e.node.updateChildren()}),ge(()=>e.node.indeterminate,P=>{b(e.node.checked,P)}),ge(()=>e.node.checked,P=>{b(P,e.node.indeterminate)}),ge(()=>e.node.expanded,P=>{je(()=>a.value=P),P&&(i.value=!0)});const f=P=>Gp(r.props.nodeKey,P.data),h=P=>{const O=e.props.class;if(!O)return{};let D;if(Je(O)){const{data:L}=P;D=O(L,P)}else D=O;return Qe(D)?{[D]:!0}:D},b=(P,O)=>{(l.value!==P||s.value!==O)&&r.ctx.emit("check-change",e.node.data,P,O),l.value=P,s.value=O},m=P=>{const 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i=le("el-icon"),l=le("el-checkbox"),s=le("loading"),c=le("node-content"),d=le("el-tree-node"),u=le("el-collapse-transition");return Ue((w(),I("div",{ref:"node$",class:S([e.ns.b("node"),e.ns.is("expanded",e.expanded),e.ns.is("current",e.node.isCurrent),e.ns.is("hidden",!e.node.visible),e.ns.is("focusable",!e.node.disabled),e.ns.is("checked",!e.node.disabled&&e.node.checked),e.getNodeClass(e.node)]),role:"treeitem",tabindex:"-1","aria-expanded":e.expanded,"aria-disabled":e.node.disabled,"aria-checked":e.node.checked,draggable:e.tree.props.draggable,"data-key":e.getNodeKey(e.node),onClick:t[1]||(t[1]=He((...p)=>e.handleClick&&e.handleClick(...p),["stop"])),onContextmenu:t[2]||(t[2]=(...p)=>e.handleContextMenu&&e.handleContextMenu(...p)),onDragstart:t[3]||(t[3]=He((...p)=>e.handleDragStart&&e.handleDragStart(...p),["stop"])),onDragover:t[4]||(t[4]=He((...p)=>e.handleDragOver&&e.handleDragOver(...p),["stop"])),onDragend:t[5]||(t[5]=He((...p)=>e.handleDragEnd&&e.handleDragEnd(...p),["stop"])),onDrop:t[6]||(t[6]=He((...p)=>e.handleDrop&&e.handleDrop(...p),["stop"]))},[M("div",{class:S(e.ns.be("node","content")),style:Oe({paddingLeft:(e.node.level-1)*e.tree.props.indent+"px"})},[e.tree.props.icon||e.CaretRight?(w(),Q(i,{key:0,class:S([e.ns.be("node","expand-icon"),e.ns.is("leaf",e.node.isLeaf),{expanded:!e.node.isLeaf&&e.expanded}]),onClick:He(e.handleExpandIconClick,["stop"])},{default:K(()=>[(w(),Q(pt(e.tree.props.icon||e.CaretRight)))]),_:1},8,["class","onClick"])):G("v-if",!0),e.showCheckbox?(w(),Q(l,{key:1,"model-value":e.node.checked,indeterminate:e.node.indeterminate,disabled:!!e.node.disabled,onClick:t[0]||(t[0]=He(()=>{},["stop"])),onChange:e.handleCheckChange},null,8,["model-value","indeterminate","disabled","onChange"])):G("v-if",!0),e.node.loading?(w(),Q(i,{key:2,class:S([e.ns.be("node","loading-icon"),e.ns.is("loading")])},{default:K(()=>[U(s)]),_:1},8,["class"])):G("v-if",!0),U(c,{node:e.node,"render-content":e.renderContent},null,8,["node","render-content"])],6),U(u,null,{default:K(()=>[!e.renderAfterExpand||e.childNodeRendered?Ue((w(),I("div",{key:0,class:S(e.ns.be("node","children")),role:"group","aria-expanded":e.expanded},[(w(!0),I(Fe,null,ct(e.node.childNodes,p=>(w(),Q(d,{key:e.getNodeKey(p),"render-content":e.renderContent,"render-after-expand":e.renderAfterExpand,"show-checkbox":e.showCheckbox,node:p,accordion:e.accordion,props:e.props,onNodeExpand:e.handleChildNodeExpand},null,8,["render-content","render-after-expand","show-checkbox","node","accordion","props","onNodeExpand"]))),128))],10,MU)),[[ft,e.expanded]]):G("v-if",!0)]),_:1})],42,NU)),[[ft,e.node.visible]])}var PU=_e(IU,[["render",RU],["__file","/home/runner/work/element-plus/element-plus/packages/components/tree/src/tree-node.vue"]]);function OU({el$:e},t){const n=ke("tree"),o=Kt([]),r=Kt([]);et(()=>{i(),At(e.value,"keydown",a)}),$t(()=>{Bt(e.value,"keydown",a)}),Un(()=>{o.value=Array.from(e.value.querySelectorAll("[role=treeitem]")),r.value=Array.from(e.value.querySelectorAll("input[type=checkbox]"))}),ge(r,l=>{l.forEach(s=>{s.setAttribute("tabindex","-1")})});const a=l=>{const s=l.target;if(!s.className.includes(n.b("node")))return;const c=l.code;o.value=Array.from(e.value.querySelectorAll(`.${n.is("focusable")}[role=treeitem]`));const d=o.value.indexOf(s);let u;if([Pe.up,Pe.down].includes(c)){if(l.preventDefault(),c===Pe.up){u=d===-1?0:d!==0?d-1:o.value.length-1;const 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FU=ee({name:"ElTree",components:{ElTreeNode:PU},props:{data:{type:Array,default:()=>[]},emptyText:{type:String},renderAfterExpand:{type:Boolean,default:!0},nodeKey:String,checkStrictly:Boolean,defaultExpandAll:Boolean,expandOnClickNode:{type:Boolean,default:!0},checkOnClickNode:Boolean,checkDescendants:{type:Boolean,default:!1},autoExpandParent:{type:Boolean,default:!0},defaultCheckedKeys:Array,defaultExpandedKeys:Array,currentNodeKey:[String,Number],renderContent:Function,showCheckbox:{type:Boolean,default:!1},draggable:{type:Boolean,default:!1},allowDrag:Function,allowDrop:Function,props:{type:Object,default:()=>({children:"children",label:"label",disabled:"disabled"})},lazy:{type:Boolean,default:!1},highlightCurrent:Boolean,load:Function,filterNodeMethod:Function,accordion:Boolean,indent:{type:Number,default:18},icon:[String,Object]},emits:["check-change","current-change","node-click","node-contextmenu","node-collapse","node-expand","check","node-drag-start","node-drag-end","node-drop","node-drag-leave","node-drag-enter","node-drag-over"],setup(e,t){const{t:n}=Tt(),o=ke("tree"),r=A(new $U({key:e.nodeKey,data:e.data,lazy:e.lazy,props:e.props,load:e.load,currentNodeKey:e.currentNodeKey,checkStrictly:e.checkStrictly,checkDescendants:e.checkDescendants,defaultCheckedKeys:e.defaultCheckedKeys,defaultExpandedKeys:e.defaultExpandedKeys,autoExpandParent:e.autoExpandParent,defaultExpandAll:e.defaultExpandAll,filterNodeMethod:e.filterNodeMethod}));r.value.initialize();const a=A(r.value.root),i=A(null),l=A(null),s=A(null),{broadcastExpanded:c}=e1(e),{dragState:d}=zU({props:e,ctx:t,el$:l,dropIndicator$:s,store:r});OU({el$:l},r);const u=C(()=>{const{childNodes:E}=a.value;return!E||E.length===0||E.every(({visible:N})=>!N)});ge(()=>e.defaultCheckedKeys,E=>{r.value.setDefaultCheckedKey(E)}),ge(()=>e.defaultExpandedKeys,E=>{r.value.setDefaultExpandedKeys(E)}),ge(()=>e.data,E=>{r.value.setData(E)},{deep:!0}),ge(()=>e.checkStrictly,E=>{r.value.checkStrictly=E});const p=E=>{if(!e.filterNodeMethod)throw new Error("[Tree] filterNodeMethod is required when 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DU(e,t,n,o,r,a){var i;const l=le("el-tree-node");return w(),I("div",{ref:"el$",class:S([e.ns.b(),e.ns.is("dragging",!!e.dragState.draggingNode),e.ns.is("drop-not-allow",!e.dragState.allowDrop),e.ns.is("drop-inner",e.dragState.dropType==="inner"),{[e.ns.m("highlight-current")]:e.highlightCurrent}]),role:"tree"},[(w(!0),I(Fe,null,ct(e.root.childNodes,s=>(w(),Q(l,{key:e.getNodeKey(s),node:s,props:e.props,accordion:e.accordion,"render-after-expand":e.renderAfterExpand,"show-checkbox":e.showCheckbox,"render-content":e.renderContent,onNodeExpand:e.handleNodeExpand},null,8,["node","props","accordion","render-after-expand","show-checkbox","render-content","onNodeExpand"]))),128)),e.isEmpty?(w(),I("div",{key:0,class:S(e.ns.e("empty-block"))},[M("span",{class:S(e.ns.e("empty-text"))},ve((i=e.emptyText)!=null?i:e.t("el.tree.emptyText")),3)],2)):G("v-if",!0),Ue(M("div",{ref:"dropIndicator$",class:S(e.ns.e("drop-indicator"))},null,2),[[ft,e.dragState.showDropIndicator]])],2)}var cs=_e(FU,[["render",DU],["__file","/home/runner/work/element-plus/element-plus/packages/components/tree/src/tree.vue"]]);cs.install=e=>{e.component(cs.name,cs)};const Ps=cs,LU=Ps,VU=(e,{attrs:t},{tree:n,key:o})=>{const r=ke("tree-select"),a=ze(he(he({},rr(Gt(e),Object.keys(qr.props))),t),{valueKey:o,popperClass:C(()=>{const i=[r.e("popper")];return e.popperClass&&i.push(e.popperClass),i.join(" ")}),filterMethod:(i="")=>{e.filterMethod&&e.filterMethod(i),je(()=>{var l;(l=n.value)==null||l.filter(i)})},onVisibleChange:i=>{var l;(l=t.onVisibleChange)==null||l.call(t,i),e.filterable&&i&&a.filterMethod()}});return a},BU=ee({extends:zs,setup(e,t){const n=zs.setup(e,t);return delete n.selectOptionClick,n},methods:{selectOptionClick(){this.$el.parentElement.click()}}}),jU=(e,{attrs:t,slots:n,emit:o},{select:r,tree:a,key:i})=>{ge(()=>e.modelValue,()=>{e.showCheckbox&&je(()=>{const 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0:r.offsetHeight)||0,f=this.headerHeight.value=this.showHeader?p:0;if(this.showHeader&&!u&&p>0&&(this.table.store.states.columns.value||[]).length>0&&f<2)return je(()=>this.updateElsHeight());const h=this.tableHeight.value=(n=(t=this.table)==null?void 0:t.vnode.el)==null?void 0:n.clientHeight,b=this.footerHeight.value=i?i.offsetHeight:0;this.height.value!==null&&(this.bodyHeight.value===null&&requestAnimationFrame(()=>this.updateElsHeight()),this.bodyHeight.value=h-f-b+(i?1:0),this.bodyScrollHeight.value=s==null?void 0:s.scrollHeight),this.fixedBodyHeight.value=this.scrollX.value?this.bodyHeight.value-this.gutterWidth:this.bodyHeight.value,this.viewportHeight.value=this.scrollX.value?h-this.gutterWidth:h,this.updateScrollY(),this.notifyObservers("scrollable")}headerDisplayNone(t){if(!t)return!0;let n=t;for(;n.tagName!=="DIV";){if(getComputedStyle(n).display==="none")return!0;n=n.parentElement}return!1}updateColumnsWidth(){if(!dt)return;const t=this.fit,n=this.table.vnode.el.clientWidth;let o=0;const r=this.getFlattenColumns(),a=r.filter(s=>typeof s.width!="number");if(r.forEach(s=>{typeof s.width=="number"&&s.realWidth&&(s.realWidth=null)}),a.length>0&&t){if(r.forEach(s=>{o+=Number(s.width||s.minWidth||80)}),o<=n){this.scrollX.value=!1;const s=n-o;if(a.length===1)a[0].realWidth=Number(a[0].minWidth||80)+s;else{const c=a.reduce((p,f)=>p+Number(f.minWidth||80),0),d=s/c;let u=0;a.forEach((p,f)=>{if(f===0)return;const h=Math.floor(Number(p.minWidth||80)*d);u+=h,p.realWidth=Number(p.minWidth||80)+h}),a[0].realWidth=Number(a[0].minWidth||80)+s-u}}else this.scrollX.value=!0,a.forEach(s=>{s.realWidth=Number(s.minWidth)});this.bodyWidth.value=Math.max(o,n),this.table.state.resizeState.value.width=this.bodyWidth.value}else r.forEach(s=>{!s.width&&!s.minWidth?s.realWidth=80:s.realWidth=Number(s.width||s.minWidth),o+=s.realWidth}),this.scrollX.value=o>n,this.bodyWidth.value=o;const i=this.store.states.fixedColumns.value;if(i.length>0){let s=0;i.forEach(c=>{s+=Number(c.realWidth||c.width)}),this.fixedWidth.value=s}const l=this.store.states.rightFixedColumns.value;if(l.length>0){let s=0;l.forEach(c=>{s+=Number(c.realWidth||c.width)}),this.rightFixedWidth.value=s}this.notifyObservers("columns")}addObserver(t){this.observers.push(t)}removeObserver(t){const n=this.observers.indexOf(t);n!==-1&&this.observers.splice(n,1)}notifyObservers(t){this.observers.forEach(o=>{var r,a;switch(t){case"columns":(r=o.state)==null||r.onColumnsChange(this);break;case"scrollable":(a=o.state)==null||a.onScrollableChange(this);break;default:throw new Error(`Table Layout don't have event ${t}.`)}})}}const{CheckboxGroup:kK}=lo,TK=ee({name:"ElTableFilterPanel",components:{ElCheckbox:lo,ElCheckboxGroup:kK,ElScrollbar:qo,ElTooltip:vn,ElIcon:qe,ArrowDown:Gr,ArrowUp:rl},directives:{ClickOutside:Jr},props:{placement:{type:String,default:"bottom-start"},store:{type:Object},column:{type:Object},upDataColumn:{type:Function}},setup(e){const t=nt(),{t:n}=Tt(),o=ke("table-filter"),r=t==null?void 0:t.parent;r.filterPanels.value[e.column.id]||(r.filterPanels.value[e.column.id]=t);const a=A(!1),i=A(null),l=C(()=>e.column&&e.column.filters),s=C({get:()=>{var T;return(((T=e.column)==null?void 0:T.filteredValue)||[])[0]},set:T=>{c.value&&(typeof T!="undefined"&&T!==null?c.value.splice(0,1,T):c.value.splice(0,1))}}),c=C({get(){return e.column?e.column.filteredValue||[]:[]},set(T){e.column&&e.upDataColumn("filteredValue",T)}}),d=C(()=>e.column?e.column.filterMultiple:!0),u=T=>T.value===s.value,p=()=>{a.value=!1},f=T=>{T.stopPropagation(),a.value=!a.value},h=()=>{a.value=!1},b=()=>{v(c.value),p()},m=()=>{c.value=[],v(c.value),p()},g=T=>{s.value=T,v(typeof T!="undefined"&&T!==null?c.value:[]),p()},v=T=>{e.store.commit("filterChange",{column:e.column,values:T}),e.store.updateAllSelected()};ge(a,T=>{e.column&&e.upDataColumn("filterOpened",T)},{immediate:!0});const y=C(()=>{var T,x;return(x=(T=i.value)==null?void 0:T.popperRef)==null?void 0:x.contentRef});return{tooltipVisible:a,multiple:d,filteredValue:c,filterValue:s,filters:l,handleConfirm:b,handleReset:m,handleSelect:g,isActive:u,t:n,ns:o,showFilterPanel:f,hideFilterPanel:h,popperPaneRef:y,tooltip:i}}}),CK={key:0},SK=["disabled"],$K=["label","onClick"];function AK(e,t,n,o,r,a){const i=le("el-checkbox"),l=le("el-checkbox-group"),s=le("el-scrollbar"),c=le("arrow-up"),d=le("arrow-down"),u=le("el-icon"),p=le("el-tooltip"),f=In("click-outside");return w(),Q(p,{ref:"tooltip",visible:e.tooltipVisible,"onUpdate:visible":t[5]||(t[5]=h=>e.tooltipVisible=h),offset:0,placement:e.placement,"show-arrow":!1,"stop-popper-mouse-event":!1,"append-to-body":"",effect:"light",pure:"","popper-class":e.ns.b(),persistent:""},{content:K(()=>[e.multiple?(w(),I("div",CK,[R("div",{class:S(e.ns.e("content"))},[U(s,{"wrap-class":e.ns.e("wrap")},{default:K(()=>[U(l,{modelValue:e.filteredValue,"onUpdate:modelValue":t[0]||(t[0]=h=>e.filteredValue=h),class:S(e.ns.e("checkbox-group"))},{default:K(()=>[(w(!0),I(Fe,null,st(e.filters,h=>(w(),Q(i,{key:h.value,label:h.value},{default:K(()=>[ct(ve(h.text),1)]),_:2},1032,["label"]))),128))]),_:1},8,["modelValue","class"])]),_:1},8,["wrap-class"])],2),R("div",{class:S(e.ns.e("bottom"))},[R("button",{class:S({[e.ns.is("disabled")]:e.filteredValue.length===0}),disabled:e.filteredValue.length===0,type:"button",onClick:t[1]||(t[1]=(...h)=>e.handleConfirm&&e.handleConfirm(...h))},ve(e.t("el.table.confirmFilter")),11,SK),R("button",{type:"button",onClick:t[2]||(t[2]=(...h)=>e.handleReset&&e.handleReset(...h))},ve(e.t("el.table.resetFilter")),1)],2)])):(w(),I("ul",{key:1,class:S(e.ns.e("list"))},[R("li",{class:S([e.ns.e("list-item"),{[e.ns.is("active")]:e.filterValue===void 0||e.filterValue===null}]),onClick:t[3]||(t[3]=h=>e.handleSelect(null))},ve(e.t("el.table.clearFilter")),3),(w(!0),I(Fe,null,st(e.filters,h=>(w(),I("li",{key:h.value,class:S([e.ns.e("list-item"),e.ns.is("active",e.isActive(h))]),label:h.value,onClick:b=>e.handleSelect(h.value)},ve(h.text),11,$K))),128))],2))]),default:K(()=>[Ue((w(),I("span",{class:S([`${e.ns.namespace.value}-table__column-filter-trigger`,`${e.ns.namespace.value}-none-outline`]),onClick:t[4]||(t[4]=(...h)=>e.showFilterPanel&&e.showFilterPanel(...h))},[U(u,null,{default:K(()=>[e.column.filterOpened?(w(),Q(c,{key:0})):(w(),Q(d,{key:1}))]),_:1})],2)),[[f,e.hideFilterPanel,e.popperPaneRef]])]),_:1},8,["visible","placement","popper-class"])}var EK=_e(TK,[["render",AK],["__file","/home/runner/work/element-plus/element-plus/packages/components/table/src/filter-panel.vue"]]);function q_(e){const t=nt();Ji(()=>{n.value.addObserver(t)}),et(()=>{o(n.value),r(n.value)}),Un(()=>{o(n.value),r(n.value)}),Qi(()=>{n.value.removeObserver(t)});const n=C(()=>{const a=e.layout;if(!a)throw new Error("Can not find table layout.");return a}),o=a=>{var i;const l=((i=e.vnode.el)==null?void 0:i.querySelectorAll("colgroup > col"))||[];if(!l.length)return;const s=a.getFlattenColumns(),c={};s.forEach(d=>{c[d.id]=d});for(let d=0,u=l.length;d{var i,l;const s=((i=e.vnode.el)==null?void 0:i.querySelectorAll("colgroup > col[name=gutter]"))||[];for(let d=0,u=s.length;d{b.stopPropagation()},a=(b,m)=>{!m.filters&&m.sortable?h(b,m,!1):m.filterable&&!m.sortable&&r(b),o==null||o.emit("header-click",m,b)},i=(b,m)=>{o==null||o.emit("header-contextmenu",m,b)},l=A(null),s=A(!1),c=A({}),d=(b,m)=>{if(!!dt&&!(m.children&&m.children.length>0)&&l.value&&e.border){s.value=!0;const g=o;t("set-drag-visible",!0);const y=(g==null?void 0:g.vnode.el).getBoundingClientRect().left,T=n.vnode.el.querySelector(`th.${m.id}`),x=T.getBoundingClientRect(),k=x.left-y+30;bo(T,"noclick"),c.value={startMouseLeft:b.clientX,startLeft:x.right-y,startColumnLeft:x.left-y,tableLeft:y};const $=g==null?void 0:g.refs.resizeProxy;$.style.left=`${c.value.startLeft}px`,document.onselectstart=function(){return!1},document.ondragstart=function(){return!1};const z=O=>{const D=O.clientX-c.value.startMouseLeft,L=c.value.startLeft+D;$.style.left=`${Math.max(k,L)}px`},P=()=>{if(s.value){const{startColumnLeft:O,startLeft:D}=c.value,F=Number.parseInt($.style.left,10)-O;m.width=m.realWidth=F,g==null||g.emit("header-dragend",m.width,D-O,m,b),requestAnimationFrame(()=>{e.store.scheduleLayout(!1,!0)}),document.body.style.cursor="",s.value=!1,l.value=null,c.value={},t("set-drag-visible",!1)}document.removeEventListener("mousemove",z),document.removeEventListener("mouseup",P),document.onselectstart=null,document.ondragstart=null,setTimeout(()=>{hn(T,"noclick")},0)};document.addEventListener("mousemove",z),document.addEventListener("mouseup",P)}},u=(b,m)=>{if(m.children&&m.children.length>0)return;let g=b.target;for(;g&&g.tagName!=="TH";)g=g.parentNode;if(!(!m||!m.resizable)&&!s.value&&e.border){const v=g.getBoundingClientRect(),y=document.body.style;v.width>12&&v.right-b.pageX<8?(y.cursor="col-resize",oo(g,"is-sortable")&&(g.style.cursor="col-resize"),l.value=m):s.value||(y.cursor="",oo(g,"is-sortable")&&(g.style.cursor="pointer"),l.value=null)}},p=()=>{!dt||(document.body.style.cursor="")},f=({order:b,sortOrders:m})=>{if(b==="")return m[0];const g=m.indexOf(b||null);return m[g>m.length-2?0:g+1]},h=(b,m,g)=>{b.stopPropagation();const v=m.order===g?null:g||f(m);let y=b.target;for(;y&&y.tagName!=="TH";)y=y.parentNode;if(y&&y.tagName==="TH"&&oo(y,"noclick")){hn(y,"noclick");return}if(!m.sortable)return;const T=e.store.states;let x=T.sortProp.value,k;const $=T.sortingColumn.value;($!==m||$===m&&$.order===null)&&($&&($.order=null),T.sortingColumn.value=m,x=m.property),v?k=m.order=v:k=m.order=null,T.sortProp.value=x,T.sortOrder.value=k,o==null||o.store.commit("changeSortCondition")};return{handleHeaderClick:a,handleHeaderContextMenu:i,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:h,handleFilterClick:r}}function IK(e){const t=Ie(xo),n=ke("table");return{getHeaderRowStyle:l=>{const s=t==null?void 0:t.props.headerRowStyle;return typeof s=="function"?s.call(null,{rowIndex:l}):s},getHeaderRowClass:l=>{const s=[],c=t==null?void 0:t.props.headerRowClassName;return typeof c=="string"?s.push(c):typeof c=="function"&&s.push(c.call(null,{rowIndex:l})),s.join(" ")},getHeaderCellStyle:(l,s,c,d)=>{var u;let p=(u=t==null?void 0:t.props.headerCellStyle)!=null?u:{};typeof p=="function"&&(p=p.call(null,{rowIndex:l,columnIndex:s,row:c,column:d}));const f=d.isSubColumn?null:jp(s,d.fixed,e.store,c);return Ra(f,"left"),Ra(f,"right"),Object.assign({},p,f)},getHeaderCellClass:(l,s,c,d)=>{const u=d.isSubColumn?[]:Bp(n.b(),s,d.fixed,e.store,c),p=[d.id,d.order,d.headerAlign,d.className,d.labelClassName,...u];d.children||p.push("is-leaf"),d.sortable&&p.push("is-sortable");const f=t==null?void 0:t.props.headerCellClassName;return typeof f=="string"?p.push(f):typeof f=="function"&&p.push(f.call(null,{rowIndex:l,columnIndex:s,row:c,column:d})),p.push(n.e("cell")),p.filter(h=>Boolean(h)).join(" ")}}}const K_=e=>{const t=[];return e.forEach(n=>{n.children?(t.push(n),t.push.apply(t,K_(n.children))):t.push(n)}),t},NK=e=>{let t=1;const n=(a,i)=>{if(i&&(a.level=i.level+1,t{n(s,a),l+=s.colSpan}),a.colSpan=l}else a.colSpan=1};e.forEach(a=>{a.level=1,n(a,void 0)});const o=[];for(let a=0;a{a.children?(a.rowSpan=1,a.children.forEach(i=>i.isSubColumn=!0)):a.rowSpan=t-a.level+1,o[a.level-1].push(a)}),o};function MK(e){const t=Ie(xo),n=C(()=>NK(e.store.states.originColumns.value));return{isGroup:C(()=>{const a=n.value.length>1;return a&&t&&(t.state.isGroup.value=!0),a}),toggleAllSelection:a=>{a.stopPropagation(),t==null||t.store.commit("toggleAllSelection")},columnRows:n}}var RK=ee({name:"ElTableHeader",components:{ElCheckbox:lo},props:{fixed:{type:String,default:""},store:{required:!0,type:Object},border:Boolean,defaultSort:{type:Object,default:()=>({prop:"",order:""})}},setup(e,{emit:t}){const n=nt(),o=Ie(xo),r=ke("table"),a=A({}),{onColumnsChange:i,onScrollableChange:l}=q_(o);et(async()=>{await je(),await je();const{prop:k,order:$}=e.defaultSort;o==null||o.store.commit("sort",{prop:k,order:$,init:!0})});const{handleHeaderClick:s,handleHeaderContextMenu:c,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:f,handleFilterClick:h}=zK(e,t),{getHeaderRowStyle:b,getHeaderRowClass:m,getHeaderCellStyle:g,getHeaderCellClass:v}=IK(e),{isGroup:y,toggleAllSelection:T,columnRows:x}=MK(e);return n.state={onColumnsChange:i,onScrollableChange:l},n.filterPanels=a,{ns:r,filterPanels:a,onColumnsChange:i,onScrollableChange:l,columnRows:x,getHeaderRowClass:m,getHeaderRowStyle:b,getHeaderCellClass:v,getHeaderCellStyle:g,handleHeaderClick:s,handleHeaderContextMenu:c,handleMouseDown:d,handleMouseMove:u,handleMouseOut:p,handleSortClick:f,handleFilterClick:h,isGroup:y,toggleAllSelection:T}},render(){const{ns:e,isGroup:t,columnRows:n,getHeaderCellStyle:o,getHeaderCellClass:r,getHeaderRowClass:a,getHeaderRowStyle:i,handleHeaderClick:l,handleHeaderContextMenu:s,handleMouseDown:c,handleMouseMove:d,handleSortClick:u,handleMouseOut:p,store:f,$parent:h}=this;let b=1;return Re("thead",{class:{[e.is("group")]:t}},n.map((m,g)=>Re("tr",{class:a(g),key:g,style:i(g)},m.map((v,y)=>(v.rowSpan>b&&(b=v.rowSpan),Re("th",{class:r(g,y,m,v),colspan:v.colSpan,key:`${v.id}-thead`,rowspan:v.rowSpan,style:o(g,y,m,v),onClick:T=>l(T,v),onContextmenu:T=>s(T,v),onMousedown:T=>c(T,v),onMousemove:T=>d(T,v),onMouseout:p},[Re("div",{class:["cell",v.filteredValue&&v.filteredValue.length>0?"highlight":"",v.labelClassName]},[v.renderHeader?v.renderHeader({column:v,$index:y,store:f,_self:h}):v.label,v.sortable&&Re("span",{onClick:T=>u(T,v),class:"caret-wrapper"},[Re("i",{onClick:T=>u(T,v,"ascending"),class:"sort-caret ascending"}),Re("i",{onClick:T=>u(T,v,"descending"),class:"sort-caret descending"})]),v.filterable&&Re(EK,{store:f,placement:v.filterPlacement||"bottom-start",column:v,upDataColumn:(T,x)=>{v[T]=x}})])]))))))}});function PK(e){const t=Ie(xo),n=A(""),o=A(Re("div")),r=(p,f,h)=>{var b;const m=t,g=Jc(p);let v;const y=(b=m==null?void 0:m.vnode.el)==null?void 0:b.dataset.prefix;g&&(v=xg({columns:e.store.states.columns.value},g,y),v&&(m==null||m.emit(`cell-${h}`,f,v,g,p))),m==null||m.emit(`row-${h}`,f,v,p)},a=(p,f)=>{r(p,f,"dblclick")},i=(p,f)=>{e.store.commit("setCurrentRow",f),r(p,f,"click")},l=(p,f)=>{r(p,f,"contextmenu")},s=bn(p=>{e.store.commit("setHoverRow",p)},30),c=bn(()=>{e.store.commit("setHoverRow",null)},30);return{handleDoubleClick:a,handleClick:i,handleContextMenu:l,handleMouseEnter:s,handleMouseLeave:c,handleCellMouseEnter:(p,f)=>{var h;const b=t,m=Jc(p),g=(h=b==null?void 0:b.vnode.el)==null?void 0:h.dataset.prefix;if(m){const k=xg({columns:e.store.states.columns.value},m,g),$=b.hoverState={cell:m,column:k,row:f};b==null||b.emit("cell-mouse-enter",$.row,$.column,$.cell,p)}const v=p.target.querySelector(".cell");if(!(oo(v,`${g}-tooltip`)&&v.childNodes.length))return;const y=document.createRange();y.setStart(v,0),y.setEnd(v,v.childNodes.length);const T=y.getBoundingClientRect().width,x=(Number.parseInt(go(v,"paddingLeft"),10)||0)+(Number.parseInt(go(v,"paddingRight"),10)||0);(T+x>v.offsetWidth||v.scrollWidth>v.offsetWidth)&&fK(m,m.innerText||m.textContent,{placement:"top",strategy:"fixed"},f.tooltipEffect)},handleCellMouseLeave:p=>{if(!Jc(p))return;const h=t==null?void 0:t.hoverState;t==null||t.emit("cell-mouse-leave",h==null?void 0:h.row,h==null?void 0:h.column,h==null?void 0:h.cell,p)},tooltipContent:n,tooltipTrigger:o}}function OK(e){const t=Ie(xo),n=ke("table");return{getRowStyle:(c,d)=>{const u=t==null?void 0:t.props.rowStyle;return typeof u=="function"?u.call(null,{row:c,rowIndex:d}):u||null},getRowClass:(c,d)=>{const u=[n.e("row")];(t==null?void 0:t.props.highlightCurrentRow)&&c===e.store.states.currentRow.value&&u.push("current-row"),e.stripe&&d%2===1&&u.push(n.em("row","striped"));const p=t==null?void 0:t.props.rowClassName;return typeof p=="string"?u.push(p):typeof p=="function"&&u.push(p.call(null,{row:c,rowIndex:d})),u},getCellStyle:(c,d,u,p)=>{const f=t==null?void 0:t.props.cellStyle;let h=f!=null?f:{};typeof f=="function"&&(h=f.call(null,{rowIndex:c,columnIndex:d,row:u,column:p}));const b=p.isSubColumn?null:jp(d,e==null?void 0:e.fixed,e.store);return Ra(b,"left"),Ra(b,"right"),Object.assign({},h,b)},getCellClass:(c,d,u,p)=>{const f=p.isSubColumn?[]:Bp(n.b(),d,e==null?void 0:e.fixed,e.store),h=[p.id,p.align,p.className,...f],b=t==null?void 0:t.props.cellClassName;return typeof b=="string"?h.push(b):typeof b=="function"&&h.push(b.call(null,{rowIndex:c,columnIndex:d,row:u,column:p})),h.push(n.e("cell")),h.filter(m=>Boolean(m)).join(" ")},getSpan:(c,d,u,p)=>{let f=1,h=1;const b=t==null?void 0:t.props.spanMethod;if(typeof b=="function"){const m=b({row:c,column:d,rowIndex:u,columnIndex:p});Array.isArray(m)?(f=m[0],h=m[1]):typeof m=="object"&&(f=m.rowspan,h=m.colspan)}return{rowspan:f,colspan:h}},getColspanRealWidth:(c,d,u)=>{if(d<1)return 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c;(c=Rs)==null||c()}),{ns:o,onColumnsChange:l,onScrollableChange:s,wrappedRowRender:r,tooltipContent:a,tooltipTrigger:i}},render(){const{wrappedRowRender:e,store:t}=this,n=t.states.data.value||[];return Re("tbody",{},[n.reduce((o,r)=>o.concat(e(r,o.length)),[])])}});function Hp(e){const t=e.tableLayout==="auto";let n=e.columns||[];t&&n.every(r=>r.width===void 0)&&(n=[]);const o=r=>{const a={key:`${e.tableLayout}_${r.id}`,style:{},name:void 0};return t?a.style={width:`${r.width}px`}:a.name=r.id,a};return Re("colgroup",{},n.map(r=>Re("col",o(r))))}Hp.props=["columns","tableLayout"];function VK(){const e=Ie(xo),t=e==null?void 0:e.store,n=C(()=>t.states.fixedLeafColumnsLength.value),o=C(()=>t.states.rightFixedColumns.value.length),r=C(()=>t.states.columns.value.length),a=C(()=>t.states.fixedColumns.value.length),i=C(()=>t.states.rightFixedColumns.value.length);return{leftFixedLeafCount:n,rightFixedLeafCount:o,columnsCount:r,leftFixedCount:a,rightFixedCount:i,columns:t.states.columns}}function BK(e){const{columns:t}=VK(),n=ke("table");return{getCellClasses:(a,i)=>{const l=a[i],s=[n.e("cell"),l.id,l.align,l.labelClassName,...Bp(n.b(),i,l.fixed,e.store)];return l.className&&s.push(l.className),l.children||s.push(n.is("leaf")),s},getCellStyles:(a,i)=>{const l=jp(i,a.fixed,e.store);return Ra(l,"left"),Ra(l,"right"),l},columns:t}}var jK=ee({name:"ElTableFooter",props:{fixed:{type:String,default:""},store:{required:!0,type:Object},summaryMethod:Function,sumText:String,border:Boolean,defaultSort:{type:Object,default:()=>({prop:"",order:""})}},setup(e){const{getCellClasses:t,getCellStyles:n,columns:o}=BK(e);return{ns:ke("table"),getCellClasses:t,getCellStyles:n,columns:o}},render(){const{columns:e,getCellStyles:t,getCellClasses:n,summaryMethod:o,sumText:r,ns:a}=this,i=this.store.states.data.value;let l=[];return o?l=o({columns:e,data:i}):e.forEach((s,c)=>{if(c===0){l[c]=r;return}const d=i.map(h=>Number(h[s.property])),u=[];let p=!0;d.forEach(h=>{if(!Number.isNaN(+h)){p=!1;const b=`${h}`.split(".")[1];u.push(b?b.length:0)}});const f=Math.max.apply(null,u);p?l[c]="":l[c]=d.reduce((h,b)=>{const m=Number(b);return 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r=A(!1),a=A(null),i=A(!1),l=N=>{i.value=N},s=A({width:null,height:null}),c=A(!1),d={display:"inline-block",verticalAlign:"middle"},u=A();Dn(()=>{t.setHeight(e.height)}),Dn(()=>{t.setMaxHeight(e.maxHeight)}),ge(()=>[e.currentRowKey,n.states.rowKey],([N,W])=>{!_(W)||n.setCurrentRowKey(`${N}`)},{immediate:!0}),ge(()=>e.data,N=>{o.store.commit("setData",N)},{immediate:!0,deep:!0}),Dn(()=>{e.expandRowKeys&&n.setExpandRowKeysAdapter(e.expandRowKeys)});const p=()=>{o.store.commit("setHoverRow",null),o.hoverState&&(o.hoverState=null)},f=(N,W)=>{const{pixelX:Y,pixelY:J}=W;Math.abs(Y)>=Math.abs(J)&&(o.refs.bodyWrapper.scrollLeft+=W.pixelX/5)},h=C(()=>e.height||e.maxHeight||n.states.fixedColumns.value.length>0||n.states.rightFixedColumns.value.length>0),b=C(()=>({width:t.bodyWidth.value?`${t.bodyWidth.value}px`:""})),m=()=>{h.value&&t.updateElsHeight(),t.updateColumnsWidth(),requestAnimationFrame(T)};et(async()=>{await je(),n.updateColumns(),x(),requestAnimationFrame(m),s.value={width:u.value=o.vnode.el.offsetWidth,height:o.vnode.el.offsetHeight},n.states.columns.value.forEach(N=>{N.filteredValue&&N.filteredValue.length&&o.store.commit("filterChange",{column:N,values:N.filteredValue,silent:!0})}),o.$ready=!0});const g=(N,W)=>{if(!N)return;const Y=Array.from(N.classList).filter(J=>!J.startsWith("is-scrolling-"));Y.push(t.scrollX.value?W:"is-scrolling-none"),N.className=Y.join(" ")},v=N=>{const{tableWrapper:W}=o.refs;g(W,N)},y=N=>{const{tableWrapper:W}=o.refs;return!!(W&&W.classList.contains(N))},T=function(){if(!o.refs.scrollBarRef)return;if(!t.scrollX.value){const oe="is-scrolling-none";y(oe)||v(oe);return}const N=o.refs.scrollBarRef.wrap$;if(!N)return;const{scrollLeft:W,offsetWidth:Y,scrollWidth:J}=N,{headerWrapper:Z,footerWrapper:se}=o.refs;Z&&(Z.scrollLeft=W),se&&(se.scrollLeft=W);const Se=J-Y-1;W>=Se?v("is-scrolling-right"):v(W===0?"is-scrolling-left":"is-scrolling-middle")},x=()=>{var 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Y.scrollLeft+=Math.ceil(W.pixelX/5)},M=C(()=>e.maxHeight?e.showSummary?{bottom:0}:{bottom:t.scrollX.value&&e.data.length?`${t.gutterWidth}px`:""}:e.showSummary?{height:t.tableHeight.value?`${t.tableHeight.value}px`:""}:{height:t.viewportHeight.value?`${t.viewportHeight.value}px`:""}),E=C(()=>{if(e.height)return{height:t.fixedBodyHeight.value?`${t.fixedBodyHeight.value}px`:""};if(e.maxHeight){let N=iu(e.maxHeight);if(typeof N=="number")return N=t.scrollX.value?N-t.gutterWidth:N,e.showHeader&&(N-=t.headerHeight.value),N-=t.footerHeight.value,{"max-height":`${N}px`}}return{}});return{isHidden:r,renderExpanded:a,setDragVisible:l,isGroup:c,handleMouseLeave:p,handleHeaderFooterMousewheel:f,tableSize:z,bodyHeight:F,height:L,emptyBlockStyle:V,handleFixedMousewheel:q,fixedHeight:M,fixedBodyHeight:E,resizeProxyVisible:i,bodyWidth:P,resizeState:s,doLayout:m,tableBodyStyles:b,tableLayout:O,scrollbarViewStyle:d}}var KK={data:{type:Array,default:()=>[]},size:String,width:[String,Number],height:[String,Number],maxHeight:[String,Number],fit:{type:Boolean,default:!0},stripe:Boolean,border:Boolean,rowKey:[String,Function],showHeader:{type:Boolean,default:!0},showSummary:Boolean,sumText:String,summaryMethod:Function,rowClassName:[String,Function],rowStyle:[Object,Function],cellClassName:[String,Function],cellStyle:[Object,Function],headerRowClassName:[String,Function],headerRowStyle:[Object,Function],headerCellClassName:[String,Function],headerCellStyle:[Object,Function],highlightCurrentRow:Boolean,currentRowKey:[String,Number],emptyText:String,expandRowKeys:Array,defaultExpandAll:Boolean,defaultSort:Object,tooltipEffect:String,spanMethod:Function,selectOnIndeterminate:{type:Boolean,default:!0},indent:{type:Number,default:16},treeProps:{type:Object,default:()=>({hasChildren:"hasChildren",children:"children"})},lazy:Boolean,load:Function,style:{type:Object,default:()=>({})},className:{type:String,default:""},tableLayout:{type:String,default:"fixed"},scrollbarAlwaysOn:{type:Boolean,default:!1}};const WK=()=>{const e=A(),t=(a,i)=>{const l=e.value;l&&l.scrollTo(a,i)},n=(a,i)=>{const l=e.value;l&&mt(i)&&["Top","Left"].includes(a)&&l[`setScroll${a}`](i)};return{scrollBarRef:e,scrollTo:t,setScrollTop:a=>n("Top",a),setScrollLeft:a=>n("Left",a)}};let UK=1;const YK=ee({name:"ElTable",directives:{Mousewheel:gR},components:{TableHeader:RK,TableBody:LK,TableFooter:jK,ElScrollbar:qo,hColgroup:Hp},props:KK,emits:["select","select-all","selection-change","cell-mouse-enter","cell-mouse-leave","cell-contextmenu","cell-click","cell-dblclick","row-click","row-contextmenu","row-dblclick","header-click","header-contextmenu","sort-change","filter-change","current-change","header-dragend","expand-change"],setup(e){const{t}=Tt(),n=ke("table"),o=nt();ot(xo,o);const r=_K(o,e);o.store=r;const a=new xK({store:o.store,table:o,fit:e.fit,showHeader:e.showHeader});o.layout=a;const 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i=le("el-icon"),l=le("el-checkbox"),s=le("loading"),c=le("node-content"),d=le("el-tree-node"),u=le("el-collapse-transition");return 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EU=_e(CU,[["render",AU],["__file","/home/runner/work/element-plus/element-plus/packages/components/tree/src/tree-node.vue"]]);function zU({el$:e},t){const n=ke("tree"),o=Kt([]),r=Kt([]);et(()=>{i(),At(e.value,"keydown",a)}),$t(()=>{Bt(e.value,"keydown",a)}),Un(()=>{o.value=Array.from(e.value.querySelectorAll("[role=treeitem]")),r.value=Array.from(e.value.querySelectorAll("input[type=checkbox]"))}),ge(r,l=>{l.forEach(s=>{s.setAttribute("tabindex","-1")})});const a=l=>{const s=l.target;if(!s.className.includes(n.b("node")))return;const c=l.code;o.value=Array.from(e.value.querySelectorAll(`.${n.is("focusable")}[role=treeitem]`));const d=o.value.indexOf(s);let u;if([Pe.up,Pe.down].includes(c)){if(l.preventDefault(),c===Pe.up){u=d===-1?0:d!==0?d-1:o.value.length-1;const 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IU=ee({name:"ElTree",components:{ElTreeNode:EU},props:{data:{type:Array,default:()=>[]},emptyText:{type:String},renderAfterExpand:{type:Boolean,default:!0},nodeKey:String,checkStrictly:Boolean,defaultExpandAll:Boolean,expandOnClickNode:{type:Boolean,default:!0},checkOnClickNode:Boolean,checkDescendants:{type:Boolean,default:!1},autoExpandParent:{type:Boolean,default:!0},defaultCheckedKeys:Array,defaultExpandedKeys:Array,currentNodeKey:[String,Number],renderContent:Function,showCheckbox:{type:Boolean,default:!1},draggable:{type:Boolean,default:!1},allowDrag:Function,allowDrop:Function,props:{type:Object,default:()=>({children:"children",label:"label",disabled:"disabled"})},lazy:{type:Boolean,default:!1},highlightCurrent:Boolean,load:Function,filterNodeMethod:Function,accordion:Boolean,indent:{type:Number,default:18},icon:[String,Object]},emits:["check-change","current-change","node-click","node-contextmenu","node-collapse","node-expand","check","node-drag-start","node-drag-end","node-drop","node-drag-leave","node-drag-enter","node-drag-over"],setup(e,t){const{t:n}=Tt(),o=ke("tree"),r=A(new 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cs=_e(IU,[["render",NU],["__file","/home/runner/work/element-plus/element-plus/packages/components/tree/src/tree.vue"]]);cs.install=e=>{e.component(cs.name,cs)};const Ps=cs,MU=Ps,RU=(e,{attrs:t},{tree:n,key:o})=>{const r=ke("tree-select"),a=ze(he(he({},rr(Gt(e),Object.keys(qr.props))),t),{valueKey:o,popperClass:C(()=>{const i=[r.e("popper")];return e.popperClass&&i.push(e.popperClass),i.join(" ")}),filterMethod:(i="")=>{e.filterMethod&&e.filterMethod(i),je(()=>{var l;(l=n.value)==null||l.filter(i)})},onVisibleChange:i=>{var l;(l=t.onVisibleChange)==null||l.call(t,i),e.filterable&&i&&a.filterMethod()}});return a},PU=ee({extends:zs,setup(e,t){const n=zs.setup(e,t);return delete n.selectOptionClick,n},methods:{selectOptionClick(){this.$el.parentElement.click()}}}),OU=(e,{attrs:t,slots:n,emit:o},{select:r,tree:a,key:i})=>{ge(()=>e.modelValue,()=>{e.showCheckbox&&je(()=>{const 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s=document.body;Hr(e.appendTo)?s=e.appendTo:Qe(e.appendTo)&&(s=document.querySelector(e.appendTo)),Hr(s)||(s=document.body);const c=document.createElement("div"),d=U(PG,l,jt(l.message)?{default:()=>l.message}:null);return d.appContext=t!=null?t:Oa._context,d.props.onDestroy=()=>{wa(null,c)},wa(d,c),Os[n].push({vm:d}),s.appendChild(c.firstElementChild),{close:()=>{d.component.proxy.visible=!1}}};h1.forEach(e=>{Oa[e]=(t={})=>((typeof t=="string"||jt(t))&&(t={message:t}),Oa(ze(he({},t),{type:e})))});function FG(e,t,n){const o=Os[t],r=o.findIndex(({vm:c})=>{var d;return((d=c.component)==null?void 0:d.props.id)===e});if(r===-1)return;const{vm:a}=o[r];if(!a)return;n==null||n(a);const i=a.el.offsetHeight,l=t.split("-")[0];o.splice(r,1);const s=o.length;if(!(s<1))for(let c=r;c{t.component.proxy.visible=!1})}Oa.closeAll=DG;Oa._context=null;const LG=ey(Oa,"$notify");var VG=[qY,GY,iG,SG,LG,jj],BG=x8([...FY,...VG]);const jG=window.jQuery;Ub(Qx).use(nk).use(BG).mount("#app")});export default HG(); 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.el-button--info:not(:first-child):not(:last-child){border-left-color:var(--el-button-divide-border-color);border-right-color:var(--el-button-divide-border-color)}.el-button{--el-button-font-weight:var(--el-font-weight-primary);--el-button-border-color:var(--el-border-color);--el-button-bg-color:var(--el-fill-color-blank);--el-button-text-color:var(--el-text-color-regular);--el-button-disabled-text-color:var(--el-disabled-text-color);--el-button-disabled-bg-color:var(--el-fill-color-blank);--el-button-disabled-border-color:var(--el-border-color-light);--el-button-divide-border-color:rgba(255, 255, 255, .5);--el-button-hover-text-color:var(--el-color-primary);--el-button-hover-bg-color:var(--el-color-primary-light-9);--el-button-hover-border-color:var(--el-color-primary-light-7);--el-button-active-text-color:var(--el-button-hover-text-color);--el-button-active-border-color:var(--el-color-primary);--el-button-active-bg-color:var(--el-button-hover-bg-color)}.el-button{display:inline-flex;justify-content:center;align-items:center;line-height:1;height:32px;white-space:nowrap;cursor:pointer;background-color:var(--el-button-bg-color);border:var(--el-border);border-color:var(--el-button-border-color);color:var(--el-button-text-color);-webkit-appearance:none;text-align:center;box-sizing:border-box;outline:0;transition:.1s;font-weight:var(--el-button-font-weight);-webkit-user-select:none;user-select:none;vertical-align:middle;padding:8px 15px;font-size:var(--el-font-size-base);border-radius:var(--el-border-radius-base)}.el-button>span{display:inline-flex;align-items:center}.el-button+.el-button{margin-left:12px}.el-button.is-round{padding:8px 15px}.el-button:focus,.el-button:hover{color:var(--el-button-hover-text-color);border-color:var(--el-button-hover-border-color);background-color:var(--el-button-hover-bg-color);outline:0}.el-button:active{color:var(--el-button-active-text-color);border-color:var(--el-button-active-border-color);background-color:var(--el-button-active-bg-color);outline:0}.el-button::-moz-focus-inner{border:0}.el-button [class*=el-icon]+span{margin-left:6px}.el-button [class*=el-icon] svg{vertical-align:bottom}.el-button.is-plain{--el-button-hover-text-color:var(--el-color-primary);--el-button-hover-bg-color:var(--el-fill-color-blank);--el-button-hover-border-color:var(--el-color-primary)}.el-button.is-active{color:var(--el-button-active-text-color);border-color:var(--el-button-active-border-color);background-color:var(--el-button-active-bg-color);outline:0}.el-button.is-disabled,.el-button.is-disabled:focus,.el-button.is-disabled:hover{color:var(--el-button-disabled-text-color);cursor:not-allowed;background-image:none;background-color:var(--el-button-disabled-bg-color);border-color:var(--el-button-disabled-border-color)}.el-button.is-loading{position:relative;pointer-events:none}.el-button.is-loading:before{z-index:1;pointer-events:none;content:"";position:absolute;left:-1px;top:-1px;right:-1px;bottom:-1px;border-radius:inherit;background-color:var(--el-mask-color-extra-light)}.el-button.is-round{border-radius:var(--el-border-radius-round)}.el-button.is-circle{border-radius:50%;padding:8px}.el-button__text--expand{letter-spacing:.3em;margin-right:-.3em}.el-button--primary{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-primary);--el-button-border-color:var(--el-color-primary);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-primary-light-3);--el-button-hover-border-color:var(--el-color-primary-light-3);--el-button-active-bg-color:var(--el-color-primary-dark-2);--el-button-active-border-color:var(--el-color-primary-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-primary-light-5);--el-button-disabled-border-color:var(--el-color-primary-light-5)}.el-button--primary.is-plain{--el-button-text-color:var(--el-color-primary);--el-button-bg-color:var(--el-color-primary-light-9);--el-button-border-color:var(--el-color-primary-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-primary);--el-button-hover-border-color:var(--el-color-primary);--el-button-active-text-color:var(--el-color-white)}.el-button--primary.is-plain.is-disabled,.el-button--primary.is-plain.is-disabled:active,.el-button--primary.is-plain.is-disabled:focus,.el-button--primary.is-plain.is-disabled:hover{color:var(--el-color-primary-light-5);background-color:var(--el-color-primary-light-9);border-color:var(--el-color-primary-light-8)}.el-button--success{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-success);--el-button-border-color:var(--el-color-success);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-success-light-3);--el-button-hover-border-color:var(--el-color-success-light-3);--el-button-active-bg-color:var(--el-color-success-dark-2);--el-button-active-border-color:var(--el-color-success-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-success-light-5);--el-button-disabled-border-color:var(--el-color-success-light-5)}.el-button--success.is-plain{--el-button-text-color:var(--el-color-success);--el-button-bg-color:var(--el-color-success-light-9);--el-button-border-color:var(--el-color-success-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-success);--el-button-hover-border-color:var(--el-color-success);--el-button-active-text-color:var(--el-color-white)}.el-button--success.is-plain.is-disabled,.el-button--success.is-plain.is-disabled:active,.el-button--success.is-plain.is-disabled:focus,.el-button--success.is-plain.is-disabled:hover{color:var(--el-color-success-light-5);background-color:var(--el-color-success-light-9);border-color:var(--el-color-success-light-8)}.el-button--warning{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-warning);--el-button-border-color:var(--el-color-warning);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-warning-light-3);--el-button-hover-border-color:var(--el-color-warning-light-3);--el-button-active-bg-color:var(--el-color-warning-dark-2);--el-button-active-border-color:var(--el-color-warning-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-warning-light-5);--el-button-disabled-border-color:var(--el-color-warning-light-5)}.el-button--warning.is-plain{--el-button-text-color:var(--el-color-warning);--el-button-bg-color:var(--el-color-warning-light-9);--el-button-border-color:var(--el-color-warning-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-warning);--el-button-hover-border-color:var(--el-color-warning);--el-button-active-text-color:var(--el-color-white)}.el-button--warning.is-plain.is-disabled,.el-button--warning.is-plain.is-disabled:active,.el-button--warning.is-plain.is-disabled:focus,.el-button--warning.is-plain.is-disabled:hover{color:var(--el-color-warning-light-5);background-color:var(--el-color-warning-light-9);border-color:var(--el-color-warning-light-8)}.el-button--danger{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-danger);--el-button-border-color:var(--el-color-danger);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-danger-light-3);--el-button-hover-border-color:var(--el-color-danger-light-3);--el-button-active-bg-color:var(--el-color-danger-dark-2);--el-button-active-border-color:var(--el-color-danger-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-danger-light-5);--el-button-disabled-border-color:var(--el-color-danger-light-5)}.el-button--danger.is-plain{--el-button-text-color:var(--el-color-danger);--el-button-bg-color:var(--el-color-danger-light-9);--el-button-border-color:var(--el-color-danger-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-danger);--el-button-hover-border-color:var(--el-color-danger);--el-button-active-text-color:var(--el-color-white)}.el-button--danger.is-plain.is-disabled,.el-button--danger.is-plain.is-disabled:active,.el-button--danger.is-plain.is-disabled:focus,.el-button--danger.is-plain.is-disabled:hover{color:var(--el-color-danger-light-5);background-color:var(--el-color-danger-light-9);border-color:var(--el-color-danger-light-8)}.el-button--info{--el-button-text-color:var(--el-color-white);--el-button-bg-color:var(--el-color-info);--el-button-border-color:var(--el-color-info);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-info-light-3);--el-button-hover-border-color:var(--el-color-info-light-3);--el-button-active-bg-color:var(--el-color-info-dark-2);--el-button-active-border-color:var(--el-color-info-dark-2);--el-button-disabled-text-color:var(--el-color-white);--el-button-disabled-bg-color:var(--el-color-info-light-5);--el-button-disabled-border-color:var(--el-color-info-light-5)}.el-button--info.is-plain{--el-button-text-color:var(--el-color-info);--el-button-bg-color:var(--el-color-info-light-9);--el-button-border-color:var(--el-color-info-light-5);--el-button-hover-text-color:var(--el-color-white);--el-button-hover-bg-color:var(--el-color-info);--el-button-hover-border-color:var(--el-color-info);--el-button-active-text-color:var(--el-color-white)}.el-button--info.is-plain.is-disabled,.el-button--info.is-plain.is-disabled:active,.el-button--info.is-plain.is-disabled:focus,.el-button--info.is-plain.is-disabled:hover{color:var(--el-color-info-light-5);background-color:var(--el-color-info-light-9);border-color:var(--el-color-info-light-8)}.el-button--large{--el-button-size:40px;height:var(--el-button-size);padding:12px 19px;font-size:var(--el-font-size-base);border-radius:var(--el-border-radius-base)}.el-button--large [class*=el-icon]+span{margin-left:8px}.el-button--large.is-round{padding:12px 19px}.el-button--large.is-circle{width:var(--el-button-size);padding:12px}.el-button--small{--el-button-size:24px;height:var(--el-button-size);padding:5px 11px;font-size:12px;border-radius:calc(var(--el-border-radius-base) - 1px)}.el-button--small [class*=el-icon]+span{margin-left:4px}.el-button--small.is-round{padding:5px 11px}.el-button--small.is-circle{width:var(--el-button-size);padding:5px}.el-button--text{border-color:transparent;color:var(--el-color-primary);background:0 0;padding-left:0;padding-right:0}.el-button--text:focus,.el-button--text:hover{color:var(--el-color-primary-light-3);border-color:transparent;background-color:transparent}.el-button--text:active{color:var(--el-color-primary-dark-2);border-color:transparent;background-color:transparent}.el-button--text.is-disabled,.el-button--text.is-disabled:focus,.el-button--text.is-disabled:hover{border-color:transparent}.el-calendar{--el-calendar-border:var(--el-table-border, 1px solid var(--el-border-color-lighter));--el-calendar-header-border-bottom:var(--el-calendar-border);--el-calendar-selected-bg-color:var(--el-color-primary-light-9);--el-calendar-cell-width:85px;background-color:var(--el-fill-color-blank)}.el-calendar__header{display:flex;justify-content:space-between;padding:12px 20px;border-bottom:var(--el-calendar-header-border-bottom)}.el-calendar__title{color:var(--el-text-color);align-self:center}.el-calendar__body{padding:12px 20px 35px}.el-calendar-table{table-layout:fixed;width:100%}.el-calendar-table thead th{padding:12px 0;color:var(--el-text-color-regular);font-weight:400}.el-calendar-table:not(.is-range) td.next,.el-calendar-table:not(.is-range) td.prev{color:var(--el-text-color-placeholder)}.el-calendar-table td{border-bottom:var(--el-calendar-border);border-right:var(--el-calendar-border);vertical-align:top;transition:background-color var(--el-transition-duration-fast) ease}.el-calendar-table td.is-selected{background-color:var(--el-calendar-selected-bg-color)}.el-calendar-table td.is-today{color:var(--el-color-primary)}.el-calendar-table tr:first-child td{border-top:var(--el-calendar-border)}.el-calendar-table tr td:first-child{border-left:var(--el-calendar-border)}.el-calendar-table tr.el-calendar-table__row--hide-border td{border-top:none}.el-calendar-table .el-calendar-day{box-sizing:border-box;padding:8px;height:var(--el-calendar-cell-width)}.el-calendar-table .el-calendar-day:hover{cursor:pointer;background-color:var(--el-calendar-selected-bg-color)}.el-card{--el-card-border-color:var(--el-border-color-light);--el-card-border-radius:4px;--el-card-padding:20px;--el-card-bg-color:var(--el-fill-color-blank)}.el-card{border-radius:var(--el-card-border-radius);border:1px solid var(--el-card-border-color);background-color:var(--el-card-bg-color);overflow:hidden;color:var(--el-text-color-primary);transition:var(--el-transition-duration)}.el-card.is-always-shadow{box-shadow:var(--el-box-shadow-light)}.el-card.is-hover-shadow:focus,.el-card.is-hover-shadow:hover{box-shadow:var(--el-box-shadow-light)}.el-card__header{padding:calc(var(--el-card-padding) - 2px) var(--el-card-padding);border-bottom:1px solid var(--el-card-border-color);box-sizing:border-box}.el-card__body{padding:var(--el-card-padding)}.el-carousel__item{position:absolute;top:0;left:0;width:100%;height:100%;display:inline-block;overflow:hidden;z-index:calc(var(--el-index-normal) - 1)}.el-carousel__item.is-active{z-index:calc(var(--el-index-normal) - 1)}.el-carousel__item.is-animating{transition:transform .4s ease-in-out}.el-carousel__item--card{width:50%;transition:transform .4s ease-in-out}.el-carousel__item--card.is-in-stage{cursor:pointer;z-index:var(--el-index-normal)}.el-carousel__item--card.is-in-stage.is-hover .el-carousel__mask,.el-carousel__item--card.is-in-stage:hover .el-carousel__mask{opacity:.12}.el-carousel__item--card.is-active{z-index:calc(var(--el-index-normal) + 1)}.el-carousel__mask{position:absolute;width:100%;height:100%;top:0;left:0;background-color:#fff;opacity:.24;transition:var(--el-transition-duration-fast)}.el-carousel{--el-carousel-arrow-font-size:12px;--el-carousel-arrow-size:36px;--el-carousel-arrow-background:rgba(31, 45, 61, .11);--el-carousel-arrow-hover-background:rgba(31, 45, 61, .23);--el-carousel-indicator-width:30px;--el-carousel-indicator-height:2px;--el-carousel-indicator-padding-horizontal:4px;--el-carousel-indicator-padding-vertical:12px;--el-carousel-indicator-out-color:var(--el-border-color-hover);position:relative}.el-carousel--horizontal{overflow-x:hidden}.el-carousel--vertical{overflow-y:hidden}.el-carousel__container{position:relative;height:300px}.el-carousel__arrow{border:none;outline:0;padding:0;margin:0;height:var(--el-carousel-arrow-size);width:var(--el-carousel-arrow-size);cursor:pointer;transition:var(--el-transition-duration);border-radius:50%;background-color:var(--el-carousel-arrow-background);color:#fff;position:absolute;top:50%;z-index:10;transform:translateY(-50%);text-align:center;font-size:var(--el-carousel-arrow-font-size);display:inline-flex;justify-content:center;align-items:center}.el-carousel__arrow--left{left:16px}.el-carousel__arrow--right{right:16px}.el-carousel__arrow:hover{background-color:var(--el-carousel-arrow-hover-background)}.el-carousel__arrow i{cursor:pointer}.el-carousel__indicators{position:absolute;list-style:none;margin:0;padding:0;z-index:calc(var(--el-index-normal) + 1)}.el-carousel__indicators--horizontal{bottom:0;left:50%;transform:translate(-50%)}.el-carousel__indicators--vertical{right:0;top:50%;transform:translateY(-50%)}.el-carousel__indicators--outside{bottom:calc(var(--el-carousel-indicator-height) + var(--el-carousel-indicator-padding-vertical) * 2);text-align:center;position:static;transform:none}.el-carousel__indicators--outside .el-carousel__indicator:hover button{opacity:.64}.el-carousel__indicators--outside button{background-color:var(--el-carousel-indicator-out-color);opacity:.24}.el-carousel__indicators--labels{left:0;right:0;transform:none;text-align:center}.el-carousel__indicators--labels .el-carousel__button{height:auto;width:auto;padding:2px 18px;font-size:12px}.el-carousel__indicators--labels .el-carousel__indicator{padding:6px 4px}.el-carousel__indicator{background-color:transparent;cursor:pointer}.el-carousel__indicator:hover button{opacity:.72}.el-carousel__indicator--horizontal{display:inline-block;padding:var(--el-carousel-indicator-padding-vertical) var(--el-carousel-indicator-padding-horizontal)}.el-carousel__indicator--vertical{padding:var(--el-carousel-indicator-padding-horizontal) var(--el-carousel-indicator-padding-vertical)}.el-carousel__indicator--vertical .el-carousel__button{width:var(--el-carousel-indicator-height);height:calc(var(--el-carousel-indicator-width)/ 2)}.el-carousel__indicator.is-active button{opacity:1}.el-carousel__button{display:block;opacity:.48;width:var(--el-carousel-indicator-width);height:var(--el-carousel-indicator-height);background-color:#fff;border:none;outline:0;padding:0;margin:0;cursor:pointer;transition:var(--el-transition-duration)}.carousel-arrow-left-enter-from,.carousel-arrow-left-leave-active{transform:translateY(-50%) translate(-10px);opacity:0}.carousel-arrow-right-enter-from,.carousel-arrow-right-leave-active{transform:translateY(-50%) translate(10px);opacity:0}.el-cascader-panel{--el-cascader-menu-text-color:var(--el-text-color-regular);--el-cascader-menu-selected-text-color:var(--el-color-primary);--el-cascader-menu-fill:var(--el-bg-color-overlay);--el-cascader-menu-font-size:var(--el-font-size-base);--el-cascader-menu-radius:var(--el-border-radius-base);--el-cascader-menu-border:solid 1px var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color)}.el-cascader-panel{display:flex;border-radius:var(--el-cascader-menu-radius);font-size:var(--el-cascader-menu-font-size)}.el-cascader-panel.is-bordered{border:var(--el-cascader-menu-border);border-radius:var(--el-cascader-menu-radius)}.el-cascader-menu{min-width:180px;box-sizing:border-box;color:var(--el-cascader-menu-text-color);border-right:var(--el-cascader-menu-border)}.el-cascader-menu:last-child{border-right:none}.el-cascader-menu:last-child .el-cascader-node{padding-right:20px}.el-cascader-menu__wrap.el-scrollbar__wrap{height:204px}.el-cascader-menu__list{position:relative;min-height:100%;margin:0;padding:6px 0;list-style:none;box-sizing:border-box}.el-cascader-menu__hover-zone{position:absolute;top:0;left:0;width:100%;height:100%;pointer-events:none}.el-cascader-menu__empty-text{position:absolute;top:50%;left:50%;transform:translate(-50%,-50%);display:flex;align-items:center;color:var(--el-cascader-color-empty)}.el-cascader-menu__empty-text .is-loading{margin-right:2px}.el-cascader-node{position:relative;display:flex;align-items:center;padding:0 30px 0 20px;height:34px;line-height:34px;outline:0}.el-cascader-node.is-selectable.in-active-path{color:var(--el-cascader-menu-text-color)}.el-cascader-node.in-active-path,.el-cascader-node.is-active,.el-cascader-node.is-selectable.in-checked-path{color:var(--el-cascader-menu-selected-text-color);font-weight:700}.el-cascader-node:not(.is-disabled){cursor:pointer}.el-cascader-node:not(.is-disabled):focus,.el-cascader-node:not(.is-disabled):hover{background:var(--el-cascader-node-background-hover)}.el-cascader-node.is-disabled{color:var(--el-cascader-node-color-disabled);cursor:not-allowed}.el-cascader-node__prefix{position:absolute;left:10px}.el-cascader-node__postfix{position:absolute;right:10px}.el-cascader-node__label{flex:1;text-align:left;padding:0 8px;white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.el-cascader-node>.el-radio{margin-right:0}.el-cascader-node>.el-radio .el-radio__label{padding-left:0}.el-cascader{--el-cascader-menu-text-color:var(--el-text-color-regular);--el-cascader-menu-selected-text-color:var(--el-color-primary);--el-cascader-menu-fill:var(--el-bg-color-overlay);--el-cascader-menu-font-size:var(--el-font-size-base);--el-cascader-menu-radius:var(--el-border-radius-base);--el-cascader-menu-border:solid 1px var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color);display:inline-block;position:relative;font-size:var(--el-font-size-base);line-height:32px;outline:0}.el-cascader:not(.is-disabled):hover .el-input__inner{cursor:pointer;box-shadow:0 0 0 1px var(--el-input-hover-border-color) inset}.el-cascader .el-input{cursor:pointer}.el-cascader .el-input .el-input__inner{text-overflow:ellipsis}.el-cascader .el-input .el-input__inner:focus{box-shadow:0 0 0 1px var(--el-input-focus-border-color,var(--el-color-primary)) inset}.el-cascader .el-input .el-input__suffix-inner .el-icon{height:calc(100% - 2px)}.el-cascader .el-input .el-input__suffix-inner .el-icon svg{vertical-align:middle}.el-cascader .el-input .icon-arrow-down{transition:transform var(--el-transition-duration);font-size:14px}.el-cascader .el-input .icon-arrow-down.is-reverse{transform:rotate(180deg)}.el-cascader .el-input .icon-circle-close:hover{color:var(--el-input-clear-hover-color,var(--el-text-color-secondary))}.el-cascader .el-input.is-focus .el-input__inner{box-shadow:0 0 0 1px var(--el-input-focus-border-color,var(--el-color-primary)) inset}.el-cascader--large{font-size:14px;line-height:40px}.el-cascader--small{font-size:12px;line-height:24px}.el-cascader.is-disabled .el-cascader__label{z-index:calc(var(--el-index-normal) + 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var(--el-border-color-light);--el-cascader-menu-shadow:var(--el-box-shadow-light);--el-cascader-node-background-hover:var(--el-fill-color-light);--el-cascader-node-color-disabled:var(--el-text-color-placeholder);--el-cascader-color-empty:var(--el-text-color-placeholder);--el-cascader-tag-background:var(--el-fill-color)}.el-cascader__dropdown{font-size:var(--el-cascader-menu-font-size);border-radius:var(--el-cascader-menu-radius)}.el-cascader__dropdown.el-popper[role=tooltip]{background:var(--el-cascader-menu-fill);border:var(--el-cascader-menu-border);box-shadow:var(--el-cascader-menu-shadow)}.el-cascader__dropdown.el-popper[role=tooltip] .el-popper__arrow:before{border:var(--el-cascader-menu-border)}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent;border-left-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent;border-right-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent;border-bottom-color:transparent}.el-cascader__dropdown.el-popper[role=tooltip][data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent;border-top-color:transparent}.el-cascader__dropdown.el-popper{box-shadow:var(--el-cascader-menu-shadow)}.el-cascader__tags{position:absolute;left:0;right:30px;top:50%;transform:translateY(-50%);display:flex;flex-wrap:wrap;line-height:normal;text-align:left;box-sizing:border-box}.el-cascader__tags .el-tag{display:inline-flex;align-items:center;max-width:100%;margin:2px 0 2px 6px;text-overflow:ellipsis;background:var(--el-cascader-tag-background)}.el-cascader__tags .el-tag:not(.is-hit){border-color:transparent}.el-cascader__tags .el-tag>span{flex:1;overflow:hidden;text-overflow:ellipsis}.el-cascader__tags 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.el-color-alpha-slider__thumb{left:0;top:0;width:100%;height:4px}.el-color-dropdown{width:300px}.el-color-dropdown__main-wrapper{margin-bottom:6px}.el-color-dropdown__main-wrapper:after{content:"";display:table;clear:both}.el-color-dropdown__btns{margin-top:12px;text-align:right}.el-color-dropdown__value{float:left;line-height:26px;font-size:12px;color:#000;width:160px}.el-color-picker{display:inline-block;position:relative;line-height:normal}.el-color-picker.is-disabled .el-color-picker__trigger{cursor:not-allowed}.el-color-picker--large{height:40px}.el-color-picker--large .el-color-picker__trigger{height:40px;width:40px}.el-color-picker--large .el-color-picker__mask{height:38px;width:38px}.el-color-picker--small{height:24px}.el-color-picker--small .el-color-picker__trigger{height:24px;width:24px}.el-color-picker--small .el-color-picker__mask{height:22px;width:22px}.el-color-picker--small .el-color-picker__empty,.el-color-picker--small .el-color-picker__icon{transform:scale(.8)}.el-color-picker__mask{height:38px;width:38px;border-radius:4px;position:absolute;top:1px;left:1px;z-index:1;cursor:not-allowed;background-color:#ffffffb3}.el-color-picker__trigger{display:inline-flex;justify-content:center;align-items:center;box-sizing:border-box;height:32px;width:32px;padding:4px;border:1px solid var(--el-border-color);border-radius:4px;font-size:0;position:relative;cursor:pointer}.el-color-picker__color{position:relative;display:block;box-sizing:border-box;border:1px solid var(--el-text-color-secondary);border-radius:var(--el-border-radius-small);width:100%;height:100%;text-align:center}.el-color-picker__color.is-alpha{background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAIAAADZF8uwAAAAGUlEQVQYV2M4gwH+YwCGIasIUwhT25BVBADtzYNYrHvv4gAAAABJRU5ErkJggg==)}.el-color-picker__color-inner{display:inline-flex;justify-content:center;align-items:center;width:100%;height:100%}.el-color-picker .el-color-picker__empty{font-size:12px;color:var(--el-text-color-secondary)}.el-color-picker .el-color-picker__icon{display:inline-flex;justify-content:center;align-items:center;color:#fff;font-size:12px}.el-color-picker__panel{position:absolute;z-index:10;padding:6px;box-sizing:content-box;background-color:#fff;border-radius:var(--el-border-radius-base);box-shadow:var(--el-box-shadow-light)}.el-color-picker__panel.el-popper{border:1px solid var(--el-border-color-lighter)}.el-container{display:flex;flex-direction:row;flex:1;flex-basis:auto;box-sizing:border-box;min-width:0}.el-container.is-vertical{flex-direction:column}.el-date-table{font-size:12px;-webkit-user-select:none;user-select:none}.el-date-table.is-week-mode .el-date-table__row:hover .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table.is-week-mode .el-date-table__row:hover td.available:hover{color:var(--el-datepicker-text-color)}.el-date-table.is-week-mode .el-date-table__row:hover td:first-child .el-date-table-cell{margin-left:5px;border-top-left-radius:15px;border-bottom-left-radius:15px}.el-date-table.is-week-mode .el-date-table__row:hover td:last-child .el-date-table-cell{margin-right:5px;border-top-right-radius:15px;border-bottom-right-radius:15px}.el-date-table.is-week-mode .el-date-table__row.current .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table td{width:32px;height:30px;padding:4px 0;box-sizing:border-box;text-align:center;cursor:pointer;position:relative}.el-date-table td .el-date-table-cell{height:30px;padding:3px 0;box-sizing:border-box}.el-date-table td .el-date-table-cell .el-date-table-cell__text{width:24px;height:24px;display:block;margin:0 auto;line-height:24px;position:absolute;left:50%;transform:translate(-50%);border-radius:50%}.el-date-table td.next-month,.el-date-table td.prev-month{color:var(--el-datepicker-off-text-color)}.el-date-table td.today{position:relative}.el-date-table td.today .el-date-table-cell__text{color:var(--el-color-primary);font-weight:700}.el-date-table td.today.end-date .el-date-table-cell__text,.el-date-table td.today.start-date .el-date-table-cell__text{color:#fff}.el-date-table td.available:hover{color:var(--el-datepicker-hover-text-color)}.el-date-table td.in-range .el-date-table-cell{background-color:var(--el-datepicker-inrange-bg-color)}.el-date-table td.in-range .el-date-table-cell:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-date-table td.current:not(.disabled) .el-date-table-cell__text{color:#fff;background-color:var(--el-datepicker-active-color)}.el-date-table td.end-date .el-date-table-cell,.el-date-table td.start-date .el-date-table-cell{color:#fff}.el-date-table td.end-date .el-date-table-cell__text,.el-date-table td.start-date .el-date-table-cell__text{background-color:var(--el-datepicker-active-color)}.el-date-table td.start-date .el-date-table-cell{margin-left:5px;border-top-left-radius:15px;border-bottom-left-radius:15px}.el-date-table td.end-date .el-date-table-cell{margin-right:5px;border-top-right-radius:15px;border-bottom-right-radius:15px}.el-date-table td.disabled .el-date-table-cell{background-color:var(--el-fill-color-light);opacity:1;cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-date-table td.selected .el-date-table-cell{margin-left:5px;margin-right:5px;background-color:var(--el-datepicker-inrange-bg-color);border-radius:15px}.el-date-table td.selected .el-date-table-cell:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-date-table td.selected .el-date-table-cell__text{background-color:var(--el-datepicker-active-color);color:#fff;border-radius:15px}.el-date-table td.week{font-size:80%;color:var(--el-datepicker-header-text-color)}.el-date-table th{padding:5px;color:var(--el-datepicker-header-text-color);font-weight:400;border-bottom:solid 1px var(--el-border-color-lighter)}.el-month-table{font-size:12px;margin:-1px;border-collapse:collapse}.el-month-table td{text-align:center;padding:8px 0;cursor:pointer}.el-month-table td div{height:48px;padding:6px 0;box-sizing:border-box}.el-month-table td.today .cell{color:var(--el-color-primary);font-weight:700}.el-month-table td.today.end-date .cell,.el-month-table td.today.start-date .cell{color:#fff}.el-month-table td.disabled .cell{background-color:var(--el-fill-color-light);cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-month-table td.disabled .cell:hover{color:var(--el-text-color-placeholder)}.el-month-table td .cell{width:60px;height:36px;display:block;line-height:36px;color:var(--el-datepicker-text-color);margin:0 auto;border-radius:18px}.el-month-table td .cell:hover{color:var(--el-datepicker-hover-text-color)}.el-month-table td.in-range div{background-color:var(--el-datepicker-inrange-bg-color)}.el-month-table td.in-range div:hover{background-color:var(--el-datepicker-inrange-hover-bg-color)}.el-month-table td.end-date div,.el-month-table td.start-date div{color:#fff}.el-month-table td.end-date .cell,.el-month-table td.start-date .cell{color:#fff;background-color:var(--el-datepicker-active-color)}.el-month-table td.start-date div{border-top-left-radius:24px;border-bottom-left-radius:24px}.el-month-table td.end-date div{border-top-right-radius:24px;border-bottom-right-radius:24px}.el-month-table td.current:not(.disabled) .cell{color:var(--el-datepicker-active-color)}.el-year-table{font-size:12px;margin:-1px;border-collapse:collapse}.el-year-table .el-icon{color:var(--el-datepicker-icon-color)}.el-year-table td{text-align:center;padding:20px 3px;cursor:pointer}.el-year-table td.today .cell{color:var(--el-color-primary);font-weight:700}.el-year-table td.disabled .cell{background-color:var(--el-fill-color-light);cursor:not-allowed;color:var(--el-text-color-placeholder)}.el-year-table td.disabled .cell:hover{color:var(--el-text-color-placeholder)}.el-year-table td .cell{width:48px;height:32px;display:block;line-height:32px;color:var(--el-datepicker-text-color);margin:0 auto}.el-year-table td .cell:hover{color:var(--el-datepicker-hover-text-color)}.el-year-table td.current:not(.disabled) .cell{color:var(--el-datepicker-active-color)}.el-time-spinner.has-seconds .el-time-spinner__wrapper{width:33.3%}.el-time-spinner__wrapper{max-height:192px;overflow:auto;display:inline-block;width:50%;vertical-align:top;position:relative}.el-time-spinner__wrapper.el-scrollbar__wrap:not(.el-scrollbar__wrap--hidden-default){padding-bottom:15px}.el-time-spinner__wrapper.is-arrow{box-sizing:border-box;text-align:center;overflow:hidden}.el-time-spinner__wrapper.is-arrow .el-time-spinner__list{transform:translateY(-32px)}.el-time-spinner__wrapper.is-arrow .el-time-spinner__item:hover:not(.is-disabled):not(.is-active){background:#fff;cursor:default}.el-time-spinner__arrow{font-size:12px;color:var(--el-text-color-secondary);position:absolute;left:0;width:100%;z-index:var(--el-index-normal);text-align:center;height:30px;line-height:30px;cursor:pointer}.el-time-spinner__arrow:hover{color:var(--el-color-primary)}.el-time-spinner__arrow.arrow-up{top:10px}.el-time-spinner__arrow.arrow-down{bottom:10px}.el-time-spinner__input.el-input{width:70%}.el-time-spinner__input.el-input .el-input__inner{padding:0;text-align:center}.el-time-spinner__list{padding:0;margin:0;list-style:none;text-align:center}.el-time-spinner__list:after,.el-time-spinner__list:before{content:"";display:block;width:100%;height:80px}.el-time-spinner__item{height:32px;line-height:32px;font-size:12px;color:var(--el-text-color-regular)}.el-time-spinner__item:hover:not(.is-disabled):not(.is-active){background:var(--el-fill-color-light);cursor:pointer}.el-time-spinner__item.is-active:not(.is-disabled){color:var(--el-text-color-primary);font-weight:700}.el-time-spinner__item.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-picker__popper{--el-datepicker-border-color:var(--el-disabled-border-color)}.el-picker__popper.el-popper[role=tooltip]{background:var(--el-bg-color-overlay);border:1px solid var(--el-datepicker-border-color);box-shadow:var(--el-box-shadow-light)}.el-picker__popper.el-popper[role=tooltip] .el-popper__arrow:before{border:1px solid var(--el-datepicker-border-color)}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent;border-left-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent;border-right-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent;border-bottom-color:transparent}.el-picker__popper.el-popper[role=tooltip][data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent;border-top-color:transparent}.el-date-editor{--el-date-editor-width:220px;--el-date-editor-monthrange-width:300px;--el-date-editor-daterange-width:350px;--el-date-editor-datetimerange-width:400px;--el-input-text-color:var(--el-text-color-regular);--el-input-border:var(--el-border);--el-input-hover-border:var(--el-border-color-hover);--el-input-focus-border:var(--el-color-primary);--el-input-transparent-border:0 0 0 1px transparent inset;--el-input-border-color:var(--el-border-color);--el-input-border-radius:var(--el-border-radius-base);--el-input-bg-color:var(--el-fill-color-blank);--el-input-icon-color:var(--el-text-color-placeholder);--el-input-placeholder-color:var(--el-text-color-placeholder);--el-input-hover-border-color:var(--el-border-color-hover);--el-input-clear-hover-color:var(--el-text-color-secondary);--el-input-focus-border-color:var(--el-color-primary);position:relative;display:inline-block;text-align:left}.el-date-editor.el-input,.el-date-editor.el-input__inner{width:var(--el-date-editor-width)}.el-date-editor--monthrange.el-input,.el-date-editor--monthrange.el-input__inner{width:var(--el-date-editor-monthrange-width)}.el-date-editor--daterange.el-input,.el-date-editor--daterange.el-input__inner,.el-date-editor--timerange.el-input,.el-date-editor--timerange.el-input__inner{width:var(--el-date-editor-daterange-width)}.el-date-editor--datetimerange.el-input,.el-date-editor--datetimerange.el-input__inner{width:var(--el-date-editor-datetimerange-width)}.el-date-editor--dates .el-input__inner{text-overflow:ellipsis;white-space:nowrap}.el-date-editor .close-icon,.el-date-editor .clear-icon{cursor:pointer}.el-date-editor .clear-icon:hover{color:var(--el-text-color-secondary)}.el-date-editor .el-range__icon{height:inherit;font-size:14px;color:var(--el-text-color-placeholder);float:left}.el-date-editor .el-range__icon svg{vertical-align:middle}.el-date-editor .el-range-input{-webkit-appearance:none;appearance:none;border:none;outline:0;display:inline-block;height:100%;margin:0;padding:0;width:39%;text-align:center;font-size:var(--el-font-size-base);color:var(--el-text-color-regular);background-color:transparent}.el-date-editor .el-range-input::placeholder{color:var(--el-text-color-placeholder)}.el-date-editor .el-range-separator{flex:1;display:inline-flex;justify-content:center;align-items:center;height:100%;padding:0 5px;margin:0;font-size:14px;word-break:keep-all;color:var(--el-text-color-primary)}.el-date-editor .el-range__close-icon{font-size:14px;color:var(--el-text-color-placeholder);height:inherit;width:unset;cursor:pointer}.el-date-editor .el-range__close-icon:hover{color:var(--el-text-color-secondary)}.el-date-editor .el-range__close-icon svg{vertical-align:middle}.el-date-editor .el-range__close-icon--hidden{opacity:0;visibility:hidden}.el-range-editor.el-input__inner{display:inline-flex;align-items:center;padding:3px 10px}.el-range-editor .el-range-input{line-height:1}.el-range-editor.is-active,.el-range-editor.is-active:hover{box-shadow:0 0 0 1px var(--el-input-focus-border-color) inset}.el-range-editor--large{line-height:40px}.el-range-editor--large.el-input__inner{height:40px}.el-range-editor--large .el-range-separator{line-height:40px;font-size:14px}.el-range-editor--large .el-range-input{font-size:14px}.el-range-editor--small{line-height:24px}.el-range-editor--small.el-input__inner{height:24px}.el-range-editor--small .el-range-separator{line-height:24px;font-size:12px}.el-range-editor--small .el-range-input{font-size:12px}.el-range-editor.is-disabled{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color);color:var(--el-disabled-text-color);cursor:not-allowed}.el-range-editor.is-disabled:focus,.el-range-editor.is-disabled:hover{border-color:var(--el-disabled-border-color)}.el-range-editor.is-disabled input{background-color:var(--el-disabled-bg-color);color:var(--el-disabled-text-color);cursor:not-allowed}.el-range-editor.is-disabled input::placeholder{color:var(--el-text-color-placeholder)}.el-range-editor.is-disabled .el-range-separator{color:var(--el-disabled-text-color)}.el-picker-panel{color:var(--el-text-color-regular);background:var(--el-bg-color-overlay);border-radius:var(--el-border-radius-base);line-height:30px}.el-picker-panel .el-time-panel{margin:5px 0;border:solid 1px var(--el-datepicker-border-color);background-color:var(--el-color-white);box-shadow:var(--el-box-shadow-light)}.el-picker-panel__body-wrapper:after,.el-picker-panel__body:after{content:"";display:table;clear:both}.el-picker-panel__content{position:relative;margin:15px}.el-picker-panel__footer{border-top:1px solid var(--el-datepicker-inner-border-color);padding:4px 12px;text-align:right;background-color:var(--el-bg-color-overlay);position:relative;font-size:0}.el-picker-panel__shortcut{display:block;width:100%;border:0;background-color:transparent;line-height:28px;font-size:14px;color:var(--el-datepicker-text-color);padding-left:12px;text-align:left;outline:0;cursor:pointer}.el-picker-panel__shortcut:hover{color:var(--el-datepicker-hover-text-color)}.el-picker-panel__shortcut.active{background-color:#e6f1fe;color:var(--el-datepicker-active-color)}.el-picker-panel__btn{border:1px solid var(--el-fill-color-darker);color:var(--el-text-color-primary);line-height:24px;border-radius:2px;padding:0 20px;cursor:pointer;background-color:transparent;outline:0;font-size:12px}.el-picker-panel__btn[disabled]{color:var(--el-text-color-disabled);cursor:not-allowed}.el-picker-panel__icon-btn{font-size:12px;color:var(--el-datepicker-icon-color);border:0;background:0 0;cursor:pointer;outline:0;margin-top:8px}.el-picker-panel__icon-btn:hover{color:var(--el-datepicker-hover-text-color)}.el-picker-panel__icon-btn.is-disabled{color:var(--el-text-color-disabled)}.el-picker-panel__icon-btn.is-disabled:hover{cursor:not-allowed}.el-picker-panel__icon-btn .el-icon{cursor:pointer;font-size:inherit}.el-picker-panel__link-btn{vertical-align:middle}.el-picker-panel [slot=sidebar],.el-picker-panel__sidebar{position:absolute;top:0;bottom:0;width:110px;border-right:1px solid var(--el-datepicker-inner-border-color);box-sizing:border-box;padding-top:6px;background-color:var(--el-bg-color-overlay);overflow:auto}.el-picker-panel [slot=sidebar]+.el-picker-panel__body,.el-picker-panel__sidebar+.el-picker-panel__body{margin-left:110px}.el-date-picker{--el-datepicker-text-color:var(--el-text-color-regular);--el-datepicker-off-text-color:var(--el-text-color-placeholder);--el-datepicker-header-text-color:var(--el-text-color-regular);--el-datepicker-icon-color:var(--el-text-color-primary);--el-datepicker-border-color:var(--el-disabled-border-color);--el-datepicker-inner-border-color:var(--el-border-color-light);--el-datepicker-inrange-bg-color:var(--el-border-color-extra-light);--el-datepicker-inrange-hover-bg-color:var(--el-border-color-extra-light);--el-datepicker-active-color:var(--el-color-primary);--el-datepicker-hover-text-color:var(--el-color-primary)}.el-date-picker{width:322px}.el-date-picker.has-sidebar.has-time{width:434px}.el-date-picker.has-sidebar{width:438px}.el-date-picker.has-time .el-picker-panel__body-wrapper{position:relative}.el-date-picker .el-picker-panel__content{width:292px}.el-date-picker table{table-layout:fixed;width:100%}.el-date-picker__editor-wrap{position:relative;display:table-cell;padding:0 5px}.el-date-picker__time-header{position:relative;border-bottom:1px solid var(--el-datepicker-inner-border-color);font-size:12px;padding:8px 5px 5px;display:table;width:100%;box-sizing:border-box}.el-date-picker__header{margin:12px;text-align:center}.el-date-picker__header--bordered{margin-bottom:0;padding-bottom:12px;border-bottom:solid 1px var(--el-border-color-lighter)}.el-date-picker__header--bordered+.el-picker-panel__content{margin-top:0}.el-date-picker__header-label{font-size:16px;font-weight:500;padding:0 5px;line-height:22px;text-align:center;cursor:pointer;color:var(--el-text-color-regular)}.el-date-picker__header-label:hover{color:var(--el-datepicker-hover-text-color)}.el-date-picker__header-label.active{color:var(--el-datepicker-active-color)}.el-date-picker__prev-btn{float:left}.el-date-picker__next-btn{float:right}.el-date-picker__time-wrap{padding:10px;text-align:center}.el-date-picker__time-label{float:left;cursor:pointer;line-height:30px;margin-left:10px}.el-date-picker .el-time-panel{position:absolute}.el-date-range-picker{--el-datepicker-text-color:var(--el-text-color-regular);--el-datepicker-off-text-color:var(--el-text-color-placeholder);--el-datepicker-header-text-color:var(--el-text-color-regular);--el-datepicker-icon-color:var(--el-text-color-primary);--el-datepicker-border-color:var(--el-disabled-border-color);--el-datepicker-inner-border-color:var(--el-border-color-light);--el-datepicker-inrange-bg-color:var(--el-border-color-extra-light);--el-datepicker-inrange-hover-bg-color:var(--el-border-color-extra-light);--el-datepicker-active-color:var(--el-color-primary);--el-datepicker-hover-text-color:var(--el-color-primary)}.el-date-range-picker{width:646px}.el-date-range-picker.has-sidebar{width:756px}.el-date-range-picker table{table-layout:fixed;width:100%}.el-date-range-picker .el-picker-panel__body{min-width:513px}.el-date-range-picker .el-picker-panel__content{margin:0}.el-date-range-picker__header{position:relative;text-align:center;height:28px}.el-date-range-picker__header [class*=arrow-left]{float:left}.el-date-range-picker__header [class*=arrow-right]{float:right}.el-date-range-picker__header div{font-size:16px;font-weight:500;margin-right:50px}.el-date-range-picker__content{float:left;width:50%;box-sizing:border-box;margin:0;padding:16px}.el-date-range-picker__content.is-left{border-right:1px solid var(--el-datepicker-inner-border-color)}.el-date-range-picker__content .el-date-range-picker__header div{margin-left:50px;margin-right:50px}.el-date-range-picker__editors-wrap{box-sizing:border-box;display:table-cell}.el-date-range-picker__editors-wrap.is-right{text-align:right}.el-date-range-picker__time-header{position:relative;border-bottom:1px solid var(--el-datepicker-inner-border-color);font-size:12px;padding:8px 5px 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var(--el-datepicker-border-color)}.el-time-panel{border-radius:2px;position:relative;width:180px;left:0;z-index:var(--el-index-top);-webkit-user-select:none;user-select:none;box-sizing:content-box}.el-time-panel__content{font-size:0;position:relative;overflow:hidden}.el-time-panel__content:after,.el-time-panel__content:before{content:"";top:50%;position:absolute;margin-top:-16px;height:32px;z-index:-1;left:0;right:0;box-sizing:border-box;padding-top:6px;text-align:left;border-top:1px solid var(--el-border-color-light);border-bottom:1px solid var(--el-border-color-light)}.el-time-panel__content:after{left:50%;margin-left:12%;margin-right:12%}.el-time-panel__content:before{padding-left:50%;margin-right:12%;margin-left:12%}.el-time-panel__content.has-seconds:after{left:66.6666666667%}.el-time-panel__content.has-seconds:before{padding-left:33.3333333333%}.el-time-panel__footer{border-top:1px solid 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.el-icon{font-size:inherit;vertical-align:text-bottom}.el-drawer__body{flex:1;padding:var(--el-drawer-padding-primary);overflow:auto}.el-drawer__body>*{box-sizing:border-box}.el-drawer.ltr,.el-drawer.rtl{height:100%;top:0;bottom:0}.el-drawer.btt,.el-drawer.ttb{width:100%;left:0;right:0}.el-drawer.ltr{left:0}.el-drawer.rtl{right:0}.el-drawer.ttb{top:0}.el-drawer.btt{bottom:0}.el-drawer-fade-enter-active,.el-drawer-fade-leave-active{transition:all var(--el-transition-duration)}.el-drawer-fade-enter-active,.el-drawer-fade-enter-from,.el-drawer-fade-enter-to,.el-drawer-fade-leave-active,.el-drawer-fade-leave-from,.el-drawer-fade-leave-to{overflow:hidden!important}.el-drawer-fade-enter-from,.el-drawer-fade-leave-to{opacity:0}.el-drawer-fade-enter-to,.el-drawer-fade-leave-from{opacity:1}.el-drawer-fade-enter-from .rtl,.el-drawer-fade-leave-to .rtl{transform:translate(100%)}.el-drawer-fade-enter-from .ltr,.el-drawer-fade-leave-to .ltr{transform:translate(-100%)}.el-drawer-fade-enter-from 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var(--el-transition-duration);overflow-wrap:anywhere;overflow:hidden;z-index:9999}.el-notification.right{right:16px}.el-notification.left{left:16px}.el-notification__group{margin-left:var(--el-notification-group-margin-left);margin-right:var(--el-notification-group-margin-right)}.el-notification__title{font-weight:700;font-size:var(--el-notification-title-font-size);line-height:var(--el-notification-icon-size);color:var(--el-notification-title-color);margin:0}.el-notification__content{font-size:var(--el-notification-content-font-size);line-height:24px;margin:6px 0 0;color:var(--el-notification-content-color);text-align:justify}.el-notification__content p{margin:0}.el-notification .el-notification__icon{height:var(--el-notification-icon-size);width:var(--el-notification-icon-size);font-size:var(--el-notification-icon-size)}.el-notification .el-notification__closeBtn{position:absolute;top:18px;right:15px;cursor:pointer;color:var(--el-notification-close-color);font-size:var(--el-notification-close-font-size)}.el-notification .el-notification__closeBtn:hover{color:var(--el-notification-close-hover-color)}.el-notification .el-notification--success{--el-notification-icon-color:var(--el-color-success);color:var(--el-notification-icon-color)}.el-notification .el-notification--info{--el-notification-icon-color:var(--el-color-info);color:var(--el-notification-icon-color)}.el-notification .el-notification--warning{--el-notification-icon-color:var(--el-color-warning);color:var(--el-notification-icon-color)}.el-notification .el-notification--error{--el-notification-icon-color:var(--el-color-error);color:var(--el-notification-icon-color)}.el-notification-fade-enter-from.right{right:0;transform:translate(100%)}.el-notification-fade-enter-from.left{left:0;transform:translate(-100%)}.el-notification-fade-leave-to{opacity:0}.el-overlay{position:fixed;top:0;right:0;bottom:0;left:0;z-index:2000;height:100%;background-color:var(--el-overlay-color-lighter);overflow:auto}.el-overlay .el-overlay-root{height:0}.el-page-header{display:flex;line-height:24px}.el-page-header__left{display:flex;cursor:pointer;margin-right:40px;position:relative}.el-page-header__left:after{content:"";position:absolute;width:1px;height:16px;right:-20px;top:50%;transform:translateY(-50%);background-color:var(--el-border-color)}.el-page-header__icon{font-size:18px;margin-right:6px;display:flex;align-items:center}.el-page-header__icon .el-icon{font-size:inherit}.el-page-header__title{font-size:14px;font-weight:500}.el-page-header__content{font-size:18px;color:var(--el-text-color-primary)}.el-pagination{--el-pagination-font-size:14px;--el-pagination-bg-color:var(--el-fill-color-blank);--el-pagination-text-color:var(--el-text-color-primary);--el-pagination-border-radius:3px;--el-pagination-button-color:var(--el-text-color-primary);--el-pagination-button-width:32px;--el-pagination-button-height:32px;--el-pagination-button-disabled-color:var(--el-text-color-placeholder);--el-pagination-button-disabled-bg-color:var(--el-fill-color-blank);--el-pagination-button-bg-color:var(--el-fill-color);--el-pagination-hover-color:var(--el-color-primary);--el-pagination-height-extra-small:24px;--el-pagination-line-height-extra-small:var(--el-pagination-height-extra-small);white-space:nowrap;padding:2px 5px;color:var(--el-pagination-text-color);font-weight:400;display:flex;align-items:center}.el-pagination:after,.el-pagination:before{display:table;content:""}.el-pagination:after{clear:both}.el-pagination button,.el-pagination span:not([class*=suffix]){display:flex;justify-content:center;align-items:center;font-size:var(--el-pagination-font-size);min-width:var(--el-pagination-button-width);height:var(--el-pagination-button-height);line-height:var(--el-pagination-button-height);box-sizing:border-box}.el-pagination .el-input__inner{text-align:center;-moz-appearance:textfield;line-height:normal}.el-pagination .el-select .el-input{width:128px}.el-pagination button{border:none;padding:0 6px;background:0 0}.el-pagination button:focus{outline:0}.el-pagination button:hover{color:var(--el-pagination-hover-color)}.el-pagination button:disabled{color:var(--el-pagination-button-disabled-color);background-color:var(--el-pagination-button-disabled-bg-color);cursor:not-allowed}.el-pagination .btn-next,.el-pagination .btn-prev{background:center center no-repeat;background-size:16px;background-color:var(--el-pagination-bg-color);cursor:pointer;margin:0;color:var(--el-pagination-button-color)}.el-pagination .btn-next .el-icon,.el-pagination .btn-prev .el-icon{display:block;font-size:12px;font-weight:700;width:inherit}.el-pagination .el-pager li.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-pagination--small .btn-next,.el-pagination--small .btn-prev,.el-pagination--small .el-pager li,.el-pagination--small .el-pager li.btn-quicknext,.el-pagination--small .el-pager li.btn-quickprev,.el-pagination--small .el-pager li:last-child{border-color:transparent;font-size:var(--el-font-size-extra-small);line-height:var(--el-pagination-line-height-extra-small);height:var(--el-pagination-height-extra-small);min-width:24px}.el-pagination--small .arrow.is-disabled{visibility:hidden}.el-pagination--small .more:before,.el-pagination--small li.more:before{line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small button,.el-pagination--small span:not([class*=suffix]){height:var(--el-pagination-height-extra-small);line-height:var(--el-pagination-line-height-extra-small);font-size:var(--el-font-size-extra-small)}.el-pagination--small .el-pagination__editor{height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-pagination__editor.el-input .el-input__inner{height:var(--el-pagination-height-extra-small)}.el-pagination--small .el-input--small,.el-pagination--small .el-input__inner{height:var(--el-pagination-height-extra-small)!important;line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-input__suffix,.el-pagination--small .el-input__suffix .el-input__suffix-inner,.el-pagination--small .el-input__suffix .el-input__suffix-inner i.el-select__caret{line-height:var(--el-pagination-line-height-extra-small)}.el-pagination--small .el-select .el-input{width:100px}.el-pagination__sizes{margin:0 16px 0 0;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__sizes+button.btn-prev[type=button]{margin-left:0}.el-pagination__sizes+.el-pager .number:first-child{margin-left:0}.el-pagination__sizes+.el-pager .number:last-child{margin-right:0}.el-pagination__total{margin-right:16px;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__total+button.btn-prev[type=button]{margin-left:0}.el-pagination__total+.el-pager .number:first-child{margin-left:0}.el-pagination__total+.el-pager .number:last-child{margin-right:0}.el-pagination__total[disabled=true]{color:var(--el-text-color-placeholder)}.el-pagination__jump{margin-left:16px;font-weight:400;color:var(--el-text-color-regular)}.el-pagination__jump .el-input__inner{padding:0 3px}.el-pagination__jump[disabled=true]{color:var(--el-text-color-placeholder)}.el-pagination__rightwrapper{flex:1;display:flex;align-items:center;justify-content:flex-end}.el-pagination__editor{line-height:18px;margin:0 8px;height:var(--el-pagination-button-height);min-width:56px;text-align:center;box-sizing:border-box;border-radius:var(--el-pagination-border-radius)}.el-pagination__editor.el-input{width:50px}.el-pagination__editor.el-input .el-input__inner{height:var(--el-pagination-button-height)}.el-pagination__editor .el-input__inner::-webkit-inner-spin-button,.el-pagination__editor .el-input__inner::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.el-pagination.is-background .btn-next,.el-pagination.is-background .btn-prev,.el-pagination.is-background .el-pager li{margin:0 4px;background-color:var(--el-pagination-button-bg-color);color:var(--el-text-color-regular);min-width:32px;border-radius:2px}.el-pagination.is-background .btn-next.is-disabled,.el-pagination.is-background .btn-prev.is-disabled,.el-pagination.is-background .el-pager li.is-disabled{color:var(--el-text-color-placeholder);background-color:var(--el-disabled-bg-color)}.el-pagination.is-background .btn-next.is-first,.el-pagination.is-background .btn-prev.is-first,.el-pagination.is-background .el-pager li.is-first{margin-left:0}.el-pagination.is-background .btn-next.is-last,.el-pagination.is-background .btn-prev.is-last,.el-pagination.is-background .el-pager li.is-last{margin-right:0}.el-pagination.is-background .btn-next,.el-pagination.is-background .btn-prev{padding:0}.el-pagination.is-background .btn-next:disabled,.el-pagination.is-background .btn-prev:disabled{color:var(--el-text-color-placeholder);background-color:var(--el-disabled-bg-color)}.el-pagination.is-background .btn-next:hover:not([disabled]),.el-pagination.is-background .btn-prev:hover:not([disabled]){color:var(--el-pagination-hover-color)}.el-pagination.is-background .el-pager li:not(.is-disabled):hover{color:var(--el-pagination-hover-color)}.el-pagination.is-background .el-pager li:not(.is-disabled).is-active{background-color:var(--el-color-primary);color:var(--el-color-white);font-weight:700}.el-pagination.is-background.el-pagination--small .btn-next,.el-pagination.is-background.el-pagination--small .btn-prev,.el-pagination.is-background.el-pagination--small .el-pager li{min-width:24px}.el-pager{-webkit-user-select:none;user-select:none;list-style:none;font-size:0;padding:0;margin:0;display:flex;align-items:center}.el-pager li{padding:0 4px;background:var(--el-pagination-bg-color);display:flex;justify-content:center;align-items:center;font-size:var(--el-pagination-font-size);min-width:var(--el-pagination-button-width);height:var(--el-pagination-button-height);line-height:var(--el-pagination-button-height);box-sizing:border-box;cursor:pointer;text-align:center}.el-pager li.btn-quickprev:hover,.el-pager li.btn-quicknext:hover{cursor:pointer}.el-pager li.btn-quicknext,.el-pager li.btn-quickprev{line-height:32px;color:var(--el-pagination-button-color)}.el-pager li.btn-quicknext.is-disabled,.el-pager li.btn-quickprev.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-pager li.btn-quicknext svg,.el-pager li.btn-quickprev svg{pointer-events:none}.el-pager li.is-active+li{border-left:0}.el-pager li:focus-visible{outline:1px solid var(--el-pagination-hover-color)}.el-pager li:hover{color:var(--el-pagination-hover-color)}.el-pager li.is-active{color:var(--el-pagination-hover-color);cursor:default}.el-pager+button.btn-next[type=button]{margin-right:0}.el-popconfirm__main{display:flex;align-items:center}.el-popconfirm__icon{margin-right:5px}.el-popconfirm__action{text-align:right;margin-top:8px}.el-popover{--el-popover-bg-color:var(--el-color-white);--el-popover-font-size:var(--el-font-size-base);--el-popover-border-color:var(--el-border-color-lighter);--el-popover-padding:12px;--el-popover-padding-large:18px 20px;--el-popover-title-font-size:16px;--el-popover-title-text-color:var(--el-text-color-primary);--el-popover-border-radius:4px}.el-popover.el-popper{background:var(--el-popover-bg-color);min-width:150px;border-radius:var(--el-popover-border-radius);border:1px solid var(--el-popover-border-color);padding:var(--el-popover-padding);z-index:var(--el-index-popper);color:var(--el-text-color-regular);line-height:1.4;text-align:justify;font-size:var(--el-popover-font-size);box-shadow:var(--el-box-shadow-light);word-break:break-all}.el-popover.el-popper--plain{padding:var(--el-popover-padding-large)}.el-popover__title{color:var(--el-popover-title-text-color);font-size:var(--el-popover-title-font-size);line-height:1;margin-bottom:12px}.el-popover__reference:focus:hover,.el-popover__reference:focus:not(.focusing){outline-width:0}.el-popover.el-popper:focus,.el-popover.el-popper:focus:active{outline-width:0}.el-progress{position:relative;line-height:1;display:flex;align-items:center}.el-progress__text{font-size:14px;color:var(--el-text-color-regular);margin-left:5px;min-width:50px;line-height:1}.el-progress__text i{vertical-align:middle;display:block}.el-progress--circle,.el-progress--dashboard{display:inline-block}.el-progress--circle .el-progress__text,.el-progress--dashboard .el-progress__text{position:absolute;top:50%;left:0;width:100%;text-align:center;margin:0;transform:translateY(-50%)}.el-progress--circle .el-progress__text i,.el-progress--dashboard .el-progress__text i{vertical-align:middle;display:inline-block}.el-progress--without-text .el-progress__text{display:none}.el-progress--without-text .el-progress-bar{padding-right:0;margin-right:0;display:block}.el-progress--text-inside .el-progress-bar{padding-right:0;margin-right:0}.el-progress.is-success .el-progress-bar__inner{background-color:var(--el-color-success)}.el-progress.is-success .el-progress__text{color:var(--el-color-success)}.el-progress.is-warning .el-progress-bar__inner{background-color:var(--el-color-warning)}.el-progress.is-warning .el-progress__text{color:var(--el-color-warning)}.el-progress.is-exception .el-progress-bar__inner{background-color:var(--el-color-danger)}.el-progress.is-exception .el-progress__text{color:var(--el-color-danger)}.el-progress-bar{flex-grow:1;box-sizing:border-box}.el-progress-bar__outer{height:6px;border-radius:100px;background-color:var(--el-border-color-lighter);overflow:hidden;position:relative;vertical-align:middle}.el-progress-bar__inner{position:absolute;left:0;top:0;height:100%;background-color:var(--el-color-primary);text-align:right;border-radius:100px;line-height:1;white-space:nowrap;transition:width .6s ease}.el-progress-bar__inner:after{display:inline-block;content:"";height:100%;vertical-align:middle}.el-progress-bar__inner--indeterminate{transform:translateZ(0);animation:indeterminate 3s infinite}.el-progress-bar__innerText{display:inline-block;vertical-align:middle;color:#fff;font-size:12px;margin:0 5px}@keyframes progress{0%{background-position:0 0}to{background-position:32px 0}}@keyframes indeterminate{0%{left:-100%}to{left:100%}}.el-radio-button{--el-radio-button-checked-bg-color:var(--el-color-primary);--el-radio-button-checked-text-color:var(--el-color-white);--el-radio-button-checked-border-color:var(--el-color-primary);--el-radio-button-disabled-checked-fill:var(--el-border-color-extra-light)}.el-radio-button{position:relative;display:inline-block;outline:0}.el-radio-button__inner{display:inline-block;line-height:1;white-space:nowrap;vertical-align:middle;background:var(--el-button-bg-color,var(--el-fill-color-blank));border:var(--el-border);font-weight:var(--el-button-font-weight,var(--el-font-weight-primary));border-left:0;color:var(--el-button-text-color,var(--el-text-color-regular));-webkit-appearance:none;text-align:center;box-sizing:border-box;outline:0;margin:0;position:relative;cursor:pointer;transition:var(--el-transition-all);-webkit-user-select:none;user-select:none;padding:8px 15px;font-size:var(--el-font-size-base);border-radius:0}.el-radio-button__inner.is-round{padding:8px 15px}.el-radio-button__inner:hover{color:var(--el-color-primary)}.el-radio-button__inner [class*=el-icon-]{line-height:.9}.el-radio-button__inner [class*=el-icon-]+span{margin-left:5px}.el-radio-button:first-child .el-radio-button__inner{border-left:var(--el-border);border-radius:var(--el-border-radius-base) 0 0 var(--el-border-radius-base);box-shadow:none!important}.el-radio-button__original-radio{opacity:0;outline:0;position:absolute;z-index:-1}.el-radio-button__original-radio:checked+.el-radio-button__inner{color:var(--el-radio-button-checked-text-color,var(--el-color-white));background-color:var(--el-radio-button-checked-bg-color,var(--el-color-primary));border-color:var(--el-radio-button-checked-border-color,var(--el-color-primary));box-shadow:-1px 0 0 0 var(--el-radio-button-checked-border-color,var(--el-color-primary))}.el-radio-button__original-radio:disabled+.el-radio-button__inner{color:var(--el-disabled-text-color);cursor:not-allowed;background-image:none;background-color:var(--el-button-disabled-bg-color,var(--el-fill-color-blank));border-color:var(--el-button-disabled-border-color,var(--el-border-color-light));box-shadow:none}.el-radio-button__original-radio:disabled:checked+.el-radio-button__inner{background-color:var(--el-radio-button-disabled-checked-fill)}.el-radio-button:last-child .el-radio-button__inner{border-radius:0 var(--el-border-radius-base) var(--el-border-radius-base) 0}.el-radio-button:first-child:last-child .el-radio-button__inner{border-radius:var(--el-border-radius-base)}.el-radio-button--large .el-radio-button__inner{padding:12px 19px;font-size:var(--el-font-size-base);border-radius:0}.el-radio-button--large .el-radio-button__inner.is-round{padding:12px 19px}.el-radio-button--small .el-radio-button__inner{padding:5px 11px;font-size:12px;border-radius:0}.el-radio-button--small .el-radio-button__inner.is-round{padding:5px 11px}.el-radio-button:focus:not(.is-focus):not(:active):not(.is-disabled){box-shadow:0 0 2px 2px var(--el-radio-button-checked-border-color)}.el-radio-group{display:inline-flex;align-items:center;flex-wrap:wrap;font-size:0}.el-radio{--el-radio-font-size:var(--el-font-size-base);--el-radio-text-color:var(--el-text-color-regular);--el-radio-font-weight:var(--el-font-weight-primary);--el-radio-input-height:14px;--el-radio-input-width:14px;--el-radio-input-border-radius:var(--el-border-radius-circle);--el-radio-input-bg-color:var(--el-fill-color-blank);--el-radio-input-border:var(--el-border);--el-radio-input-border-color:var(--el-border-color);--el-radio-input-border-color-hover:var(--el-color-primary)}.el-radio{color:var(--el-radio-text-color);font-weight:var(--el-radio-font-weight);position:relative;cursor:pointer;display:inline-flex;align-items:center;white-space:nowrap;outline:0;font-size:var(--el-font-size-base);-webkit-user-select:none;user-select:none;margin-right:32px;height:32px}.el-radio.el-radio--large{height:40px}.el-radio.el-radio--small{height:24px}.el-radio.is-bordered{padding:0 15px 0 9px;border-radius:var(--el-border-radius-base);border:var(--el-border);box-sizing:border-box}.el-radio.is-bordered.is-checked{border-color:var(--el-color-primary)}.el-radio.is-bordered.is-disabled{cursor:not-allowed;border-color:var(--el-border-color-lighter)}.el-radio.is-bordered.el-radio--large{padding:0 19px 0 11px;border-radius:var(--el-border-radius-base)}.el-radio.is-bordered.el-radio--large .el-radio__label{font-size:var(--el-font-size-base)}.el-radio.is-bordered.el-radio--large .el-radio__inner{height:14px;width:14px}.el-radio.is-bordered.el-radio--small{padding:0 11px 0 7px;border-radius:var(--el-border-radius-base)}.el-radio.is-bordered.el-radio--small .el-radio__label{font-size:12px}.el-radio.is-bordered.el-radio--small .el-radio__inner{height:12px;width:12px}.el-radio:last-child{margin-right:0}.el-radio__input{white-space:nowrap;cursor:pointer;outline:0;display:inline-flex;position:relative;vertical-align:middle}.el-radio__input.is-disabled .el-radio__inner{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color);cursor:not-allowed}.el-radio__input.is-disabled .el-radio__inner:after{cursor:not-allowed;background-color:var(--el-disabled-bg-color)}.el-radio__input.is-disabled .el-radio__inner+.el-radio__label{cursor:not-allowed}.el-radio__input.is-disabled.is-checked .el-radio__inner{background-color:var(--el-disabled-bg-color);border-color:var(--el-disabled-border-color)}.el-radio__input.is-disabled.is-checked .el-radio__inner:after{background-color:var(--el-text-color-placeholder)}.el-radio__input.is-disabled+span.el-radio__label{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-radio__input.is-checked .el-radio__inner{border-color:var(--el-color-primary);background:var(--el-color-primary)}.el-radio__input.is-checked .el-radio__inner:after{transform:translate(-50%,-50%) scale(1)}.el-radio__input.is-checked+.el-radio__label{color:var(--el-color-primary)}.el-radio__input.is-focus .el-radio__inner{border-color:var(--el-radio-input-border-color-hover)}.el-radio__inner{border:var(--el-radio-input-border);border-radius:var(--el-radio-input-border-radius);width:var(--el-radio-input-width);height:var(--el-radio-input-height);background-color:var(--el-radio-input-bg-color);position:relative;cursor:pointer;display:inline-block;box-sizing:border-box}.el-radio__inner:hover{border-color:var(--el-radio-input-border-color-hover)}.el-radio__inner:after{width:4px;height:4px;border-radius:var(--el-radio-input-border-radius);background-color:var(--el-color-white);content:"";position:absolute;left:50%;top:50%;transform:translate(-50%,-50%) scale(0);transition:transform .15s ease-in}.el-radio__original{opacity:0;outline:0;position:absolute;z-index:-1;top:0;left:0;right:0;bottom:0;margin:0}.el-radio:focus:not(.is-focus):not(:active):not(.is-disabled) .el-radio__inner{box-shadow:0 0 2px 2px var(--el-radio-input-border-color-hover)}.el-radio__label{font-size:var(--el-radio-font-size);padding-left:8px}.el-radio.el-radio--large .el-radio__label{font-size:14px}.el-radio.el-radio--large .el-radio__inner{width:14px;height:14px}.el-radio.el-radio--small .el-radio__label{font-size:12px}.el-radio.el-radio--small .el-radio__inner{width:12px;height:12px}.el-rate{--el-rate-height:20px;--el-rate-font-size:var(--el-font-size-base);--el-rate-icon-size:18px;--el-rate-icon-margin:6px;--el-rate-void-color:var(--el-border-color-darker);--el-rate-fill-color:#f7ba2a;--el-rate-disabled-void-color:var(--el-fill-color);--el-rate-text-color:var(--el-text-color-primary)}.el-rate{display:inline-flex;align-items:center;height:32px}.el-rate:active,.el-rate:focus{outline-width:0}.el-rate__item{cursor:pointer;display:inline-block;position:relative;font-size:0;vertical-align:middle;color:var(--el-rate-void-color)}.el-rate .el-rate__icon{position:relative;display:inline-block;font-size:var(--el-rate-icon-size);margin-right:var(--el-rate-icon-margin);transition:var(--el-transition-duration)}.el-rate .el-rate__icon.hover{transform:scale(1.15)}.el-rate .el-rate__icon .path2{position:absolute;left:0;top:0}.el-rate .el-rate__icon.is-active{color:var(--el-rate-fill-color)}.el-rate__decimal{position:absolute;top:0;left:0;display:inline-block;overflow:hidden;color:var(--el-rate-fill-color)}.el-rate__text{font-size:var(--el-rate-font-size);vertical-align:middle;color:var(--el-rate-text-color)}.el-rate--large{height:40px}.el-rate--small{height:24px}.el-rate.is-disabled .el-rate__item{cursor:auto;color:var(--el-rate-disabled-void-color)}.el-result{--el-result-padding:40px 30px;--el-result-icon-font-size:64px;--el-result-title-font-size:20px;--el-result-title-margin-top:20px;--el-result-subtitle-margin-top:10px;--el-result-extra-margin-top:30px}.el-result{display:flex;justify-content:center;align-items:center;flex-direction:column;text-align:center;box-sizing:border-box;padding:var(--el-result-padding)}.el-result__icon svg{width:var(--el-result-icon-font-size);height:var(--el-result-icon-font-size)}.el-result__title{margin-top:var(--el-result-title-margin-top)}.el-result__title p{margin:0;font-size:var(--el-result-title-font-size);color:var(--el-text-color-primary);line-height:1.3}.el-result__subtitle{margin-top:var(--el-result-subtitle-margin-top)}.el-result__subtitle p{margin:0;font-size:var(--el-font-size-base);color:var(--el-text-color-regular);line-height:1.3}.el-result__extra{margin-top:var(--el-result-extra-margin-top)}.el-result .icon-success{--el-result-color:var(--el-color-success);color:var(--el-result-color)}.el-result .icon-warning{--el-result-color:var(--el-color-warning);color:var(--el-result-color)}.el-result .icon-danger{--el-result-color:var(--el-color-danger);color:var(--el-result-color)}.el-result .icon-info{--el-result-color:var(--el-color-info);color:var(--el-result-color)}.el-result 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background-color;opacity:var(--el-scrollbar-opacity,.3)}.el-scrollbar__thumb:hover{background-color:var(--el-scrollbar-hover-bg-color,var(--el-text-color-secondary));opacity:var(--el-scrollbar-hover-opacity,.5)}.el-scrollbar__bar{position:absolute;right:2px;bottom:2px;z-index:1;border-radius:4px}.el-scrollbar__bar.is-vertical{width:6px;top:2px}.el-scrollbar__bar.is-vertical>div{width:100%}.el-scrollbar__bar.is-horizontal{height:6px;left:2px}.el-scrollbar__bar.is-horizontal>div{height:100%}.el-scrollbar-fade-enter-active{transition:opacity .34s ease-out}.el-scrollbar-fade-leave-active{transition:opacity .12s ease-out}.el-scrollbar-fade-enter-from,.el-scrollbar-fade-leave-active{opacity:0}.el-select-dropdown{z-index:calc(var(--el-index-top) + 1);border-radius:var(--el-border-radius-base);box-sizing:border-box}.el-select-dropdown .el-scrollbar.is-empty .el-select-dropdown__list{padding:0}.el-select-dropdown__option-item:hover:not(.hover){background-color:transparent}.el-select-dropdown__empty{padding:10px 0;margin:0;text-align:center;color:var(--el-text-color-secondary);font-size:var(--el-select-font-size)}.el-select-dropdown__wrap{max-height:274px}.el-select-dropdown__list{list-style:none;margin:6px 0!important;padding:0!important;box-sizing:border-box}.el-select-dropdown__option-item{font-size:var(--el-select-font-size);padding:0 32px 0 20px;position:relative;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;color:var(--el-text-color-regular);height:34px;line-height:34px;box-sizing:border-box;cursor:pointer}.el-select-dropdown__option-item.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-select-dropdown__option-item.is-disabled:hover{background-color:var(--el-color-white)}.el-select-dropdown__option-item.is-selected{background-color:var(--el-fill-color-light);font-weight:700}.el-select-dropdown__option-item.is-selected:not(.is-multiple){color:var(--el-color-primary)}.el-select-dropdown__option-item.hover{background-color:var(--el-fill-color-light)!important}.el-select-dropdown__option-item:hover{background-color:var(--el-fill-color-light)}.el-select-dropdown.is-multiple .el-select-dropdown__option-item.is-selected{color:var(--el-color-primary);background-color:var(--el-bg-color-overlay)}.el-select-dropdown.is-multiple .el-select-dropdown__option-item.is-selected 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.el-select-dropdown__item{padding-left:20px}.el-select-v2{--el-select-border-color-hover:var(--el-border-color-hover);--el-select-disabled-border:var(--el-disabled-border-color);--el-select-font-size:var(--el-font-size-base);--el-select-close-hover-color:var(--el-text-color-secondary);--el-select-input-color:var(--el-text-color-placeholder);--el-select-multiple-input-color:var(--el-text-color-regular);--el-select-input-focus-border-color:var(--el-color-primary);--el-select-input-font-size:14px}.el-select-v2{display:inline-block;position:relative;vertical-align:middle;font-size:14px}.el-select-v2__wrapper{display:flex;align-items:center;flex-wrap:wrap;box-sizing:border-box;cursor:pointer;padding:1px 30px 1px 0;border:1px solid var(--el-border-color);border-radius:var(--el-border-radius-base);transition:border-color var(--el-transition-duration-fast) var(--el-ease-in-out-bezier-function)}.el-select-v2__wrapper:hover{border-color:var(--el-text-color-placeholder)}.el-select-v2__wrapper.is-filterable{cursor:text}.el-select-v2__wrapper.is-focused{border-color:var(--el-color-primary)}.el-select-v2__wrapper.is-hovering:not(.is-focused){border-color:var(--el-text-color-placeholder)}.el-select-v2__wrapper.is-disabled{cursor:not-allowed;background-color:var(--el-fill-color-light);color:var(--el-text-color-placeholder);border-color:var(--el-select-disabled-border)}.el-select-v2__wrapper.is-disabled:hover{border-color:var(--el-select-disabled-border)}.el-select-v2__wrapper.is-disabled.is-focus{border-color:var(--el-input-focus-border-color)}.el-select-v2__wrapper.is-disabled .is-transparent{opacity:1;-webkit-user-select:none;user-select:none}.el-select-v2__wrapper.is-disabled .el-select-v2__caret,.el-select-v2__wrapper.is-disabled .el-select-v2__combobox-input{cursor:not-allowed}.el-select-v2__wrapper 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var(--el-border-color-light)}.el-select-v2__popper.el-popper[role=tooltip][data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent;border-left-color:transparent}.el-select-v2__popper.el-popper[role=tooltip][data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent;border-right-color:transparent}.el-select-v2__popper.el-popper[role=tooltip][data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent;border-bottom-color:transparent}.el-select-v2__popper.el-popper[role=tooltip][data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent;border-top-color:transparent}.el-select-v2--large .el-select-v2__wrapper .el-select-v2__combobox-input{height:32px}.el-select-v2--large .el-select-v2__caret,.el-select-v2--large .el-select-v2__suffix{height:40px}.el-select-v2--large .el-select-v2__placeholder{font-size:14px;line-height:40px}.el-select-v2--small .el-select-v2__wrapper 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100%;transform:translateY(-50%);width:12px;height:12px}.el-select-dropdown .el-select-dropdown__option-item.is-selected:after{content:"";position:absolute;top:50%;right:20px;border-top:none;border-right:none;background-repeat:no-repeat;background-position:center;background-color:var(--el-color-primary);-webkit-mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask-size:100% 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0;margin:0;box-sizing:border-box}.el-select{--el-select-border-color-hover:var(--el-border-color-hover);--el-select-disabled-border:var(--el-disabled-border-color);--el-select-font-size:var(--el-font-size-base);--el-select-close-hover-color:var(--el-text-color-secondary);--el-select-input-color:var(--el-text-color-placeholder);--el-select-multiple-input-color:var(--el-text-color-regular);--el-select-input-focus-border-color:var(--el-color-primary);--el-select-input-font-size:14px}.el-select{display:inline-block;position:relative;line-height:32px}.el-select__popper.el-popper[role=tooltip]{background:var(--el-bg-color-overlay);border:1px solid var(--el-border-color-light);box-shadow:var(--el-box-shadow-light)}.el-select__popper.el-popper[role=tooltip] .el-popper__arrow:before{border:1px solid var(--el-border-color-light)}.el-select__popper.el-popper[role=tooltip][data-popper-placement^=top] 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";transform:rotate(45deg);background:var(--el-text-color-primary);box-sizing:border-box}.el-popper[data-popper-placement^=top]>.el-popper__arrow{bottom:-5px}.el-popper[data-popper-placement^=top]>.el-popper__arrow:before{border-bottom-right-radius:2px}.el-popper[data-popper-placement^=bottom]>.el-popper__arrow{top:-5px}.el-popper[data-popper-placement^=bottom]>.el-popper__arrow:before{border-top-left-radius:2px}.el-popper[data-popper-placement^=left]>.el-popper__arrow{right:-5px}.el-popper[data-popper-placement^=left]>.el-popper__arrow:before{border-top-right-radius:2px}.el-popper[data-popper-placement^=right]>.el-popper__arrow{left:-5px}.el-popper[data-popper-placement^=right]>.el-popper__arrow:before{border-bottom-left-radius:2px}.el-popper[data-popper-placement^=top] .el-popper__arrow:before{border-top-color:transparent!important;border-left-color:transparent!important}.el-popper[data-popper-placement^=bottom] .el-popper__arrow:before{border-bottom-color:transparent!important;border-right-color:transparent!important}.el-popper[data-popper-placement^=left] .el-popper__arrow:before{border-left-color:transparent!important;border-bottom-color:transparent!important}.el-popper[data-popper-placement^=right] .el-popper__arrow:before{border-right-color:transparent!important;border-top-color:transparent!important}.el-select-dropdown__item{font-size:var(--el-font-size-base);padding:0 32px 0 20px;position:relative;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;color:var(--el-text-color-regular);height:34px;line-height:34px;box-sizing:border-box;cursor:pointer}.el-select-dropdown__item.is-disabled{color:var(--el-text-color-placeholder);cursor:not-allowed}.el-select-dropdown__item.is-disabled:hover{background-color:var(--el-color-white)}.el-select-dropdown__item.hover,.el-select-dropdown__item:hover{background-color:var(--el-fill-color-light)}.el-select-dropdown__item.selected{color:var(--el-color-primary);font-weight:700} diff --git a/index.html b/index.html index 00f22e1..0d9e06f 100644 --- a/index.html +++ b/index.html @@ -8,8 +8,8 @@ - - + +