From ea5740463a0ffc01dc76e51cda281ff3619620bd Mon Sep 17 00:00:00 2001
From: Joshua Bell <jsbell@chromium.org>
Date: Thu, 14 Mar 2024 04:59:49 +0000
Subject: [PATCH] Content: Define output for gru() (#600)

SHA: 09b471d8d6fcd68267d77408e0897c9ceae96969
Reason: push, by fdwr

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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 1 file changed, 299 insertions(+), 261 deletions(-)

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   <meta content="Bikeshed version 82ce88815, updated Thu Sep 7 16:33:55 2023 -0700" name="generator">
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-  <meta content="36288f5c406af7da7675f6461a2f99cc35d4bf66" name="document-revision">
+  <meta content="09b471d8d6fcd68267d77408e0897c9ceae96969" name="document-revision">
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@@ -905,7 +905,7 @@
   <div class="head">
    <p data-fill-with="logo"><a class="logo" href="https://www.w3.org/"> <img alt="W3C" height="48" src="https://www.w3.org/StyleSheets/TR/2021/logos/W3C" width="72"> </a> </p>
    <h1 class="p-name no-ref" id="title">Web Neural Network API</h1>
-   <p id="w3c-state"><a href="https://www.w3.org/standards/types#ED">Editor’s Draft</a>, <time class="dt-updated" datetime="2024-03-13">13 March 2024</time></p>
+   <p id="w3c-state"><a href="https://www.w3.org/standards/types#ED">Editor’s Draft</a>, <time class="dt-updated" datetime="2024-03-14">14 March 2024</time></p>
    <details open>
     <summary>More details about this document</summary>
     <div data-fill-with="spec-metadata">
@@ -4462,14 +4462,51 @@ <h4 class="heading settled" data-level="7.8.19" id="api-mlgraphbuilder-gru"><spa
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-activations" id="ref-for-dom-mlgruoptions-activations①">activations</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists②⑧">exists</a> and its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②⓪">size</a> is not 2, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①②⑥">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror⑦⑤">TypeError</a></code>.</p>
      <li data-md>
       <p>If <var>steps</var> is not equal to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape③②">shape</a>[0], then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①②⑦">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror⑦⑥">TypeError</a></code>.</p>
+     <li data-md>
+      <p><em>Calculate the output shape:</em></p>
+      <ol>
+       <li data-md>
+        <p>Let <var>desc0</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑦">MLOperandDescriptor</a></code>.</p>
+       <li data-md>
+        <p>Set <var>desc0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions①⑧">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list①⑨">list</a> « <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
+       <li data-md>
+        <p>Set <var>desc0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑤">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②④">dataType</a>.</p>
+       <li data-md>
+        <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-returnsequence" id="ref-for-dom-mlgruoptions-returnsequence②">returnSequence</a></code> is true:</p>
+        <ol>
+         <li data-md>
+          <p>Let <var>desc1</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑧">MLOperandDescriptor</a></code>.</p>
+         <li data-md>
+          <p>Set <var>desc1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑥">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑤">dataType</a>.</p>
+         <li data-md>
+          <p>Set <var>desc1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions①⑨">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⓪">list</a> « <var>steps</var>, <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
+        </ol>
+      </ol>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators②⑤">operator</a> for "gru", given <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var> and <var>options</var> as parameters.</p>
        <li data-md>
-        <p>Let <var>output</var> be an empty sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⓪⑨">MLOperand</a></code> objects.</p>
-        <p class="issue" id="issue-0952c806"><a class="self-link" href="#issue-0952c806"></a> Populate <var>output</var></p>
+        <p>Let <var>output0</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①④">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑤">this</a> and <var>desc0</var>.</p>
+       <li data-md>
+        <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-returnsequence" id="ref-for-dom-mlgruoptions-returnsequence③">returnSequence</a></code> is true:</p>
+        <ol>
+         <li data-md>
+          <p>Let <var>output1</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑤">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑥">this</a> and <var>desc1</var>.</p>
+         <li data-md>
+          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②①">list</a> « <var>output0</var>, <var>output1</var> ».</p>
+         <li data-md>
+          <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑤">[[operator]]</a></code> and <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑥">[[operator]]</a></code> to <var>operator</var>.</p>
+        </ol>
+       <li data-md>
+        <p>Otherwise:</p>
+        <ol>
+         <li data-md>
+          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②②">list</a> « <var>output0</var> ».</p>
+         <li data-md>
+          <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑦">[[operator]]</a></code> to <var>operator</var>.</p>
+        </ol>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input①⑨">inputs</a> to <var>input</var>, <var>weight</var>, and <var>recurrentWeight</var>.</p>
        <li data-md>
@@ -4553,28 +4590,28 @@ <h4 class="heading settled" data-level="7.8.19" id="api-mlgraphbuilder-gru"><spa
    <h4 class="heading settled" data-level="7.8.20" id="api-mlgraphbuilder-grucell"><span class="secno">7.8.20. </span><span class="content">gruCell</span><a class="self-link" href="#api-mlgraphbuilder-grucell"></a></h4>
     A single time step of the Gated Recurrent Unit <a data-link-type="biblio" href="#biblio-gru" title="Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation">[GRU]</a> recurrent network using an update gate and a reset gate to compute the hidden state that rolls into the output across the temporal sequence of a recurrent network. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlgrucelloptions"><code><c- g>MLGruCellOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgrucelloptions-bias" id="ref-for-dom-mlgrucelloptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgrucelloptions-recurrentbias" id="ref-for-dom-mlgrucelloptions-recurrentbias"><c- g>recurrentBias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⓪⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgrucelloptions-bias" id="ref-for-dom-mlgrucelloptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgrucelloptions-recurrentbias" id="ref-for-dom-mlgrucelloptions-recurrentbias"><c- g>recurrentBias</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①④"><c- b>boolean</c-></a> <a class="idl-code" data-default="true" data-link-type="dict-member" data-type="boolean " href="#dom-mlgrucelloptions-resetafter" id="ref-for-dom-mlgrucelloptions-resetafter"><c- g>resetAfter</c-></a> = <c- b>true</c->;
   <a data-link-type="idl-name" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout②"><c- n>MLGruWeightLayout</c-></a> <a class="idl-code" data-default="&quot;zrn&quot;" data-link-type="dict-member" data-type="MLGruWeightLayout " href="#dom-mlgrucelloptions-layout" id="ref-for-dom-mlgrucelloptions-layout"><c- g>layout</c-></a> = "zrn";
   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑤"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②④"><c- n>MLActivation</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<MLActivation> " href="#dom-mlgrucelloptions-activations" id="ref-for-dom-mlgrucelloptions-activations"><c- g>activations</c-></a>;
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③③"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-grucell" id="ref-for-dom-mlgraphbuilder-grucell"><c- g>gruCell</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-input"></a></dfn>,
-                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-weight"></a></dfn>,
-                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-recurrentweight"></a></dfn>,
-                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddenstate"><code><c- g>hiddenState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddenstate"></a></dfn>,
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-grucell" id="ref-for-dom-mlgraphbuilder-grucell"><c- g>gruCell</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-input"></a></dfn>,
+                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-weight"></a></dfn>,
+                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-recurrentweight"></a></dfn>,
+                    <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddenstate"><code><c- g>hiddenState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddenstate"></a></dfn>,
                     [<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange①⑧"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②⑧"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddensize"><code><c- g>hiddenSize</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-hiddensize"></a></dfn>,
                     <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgrucelloptions" id="ref-for-dictdef-mlgrucelloptions"><c- n>MLGruCellOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options), MLGraphBuilder/gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-grucell-input-weight-recurrentweight-hiddenstate-hiddensize-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlgrucelloptions" id="ref-for-dictdef-mlgrucelloptions①">MLGruCellOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑦">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑥">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D input bias tensor of shape [3 * hiddenSize]. The ordering of the bias vectors in the second dimension of the tensor shape is specified according to the <code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout⑤">layout</a></code> argument.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑧">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑦">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D recurrent bias tensor of shape [3 * hiddenSize]. The ordering of the bias vectors in the second dimension of the tensor shape is specified according to the <code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout⑥">layout</a></code> argument.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-resetafter"><code>resetAfter</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①⑤">boolean</a>, defaulting to <code>true</code></span>
@@ -4591,19 +4628,19 @@ <h4 class="heading settled" data-level="7.8.20" id="api-mlgraphbuilder-grucell">
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑨">MLOperand</a></code>. The input 2-D tensor of shape [batchSize, inputSize].</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑧">MLOperand</a></code>. The input 2-D tensor of shape [batchSize, inputSize].</p>
      <li data-md>
-      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⓪">MLOperand</a></code>. The 2-D input weight tensor of shape [3 * hiddenSize, inputSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
+      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑨">MLOperand</a></code>. The 2-D input weight tensor of shape [3 * hiddenSize, inputSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
      <li data-md>
-      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②①">MLOperand</a></code>. The 2-D recurrent weight tensor of shape [3 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
+      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⓪">MLOperand</a></code>. The 2-D recurrent weight tensor of shape [3 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
      <li data-md>
-      <p><em>hiddenState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②②">MLOperand</a></code>. The 2-D input hidden state tensor of shape [batchSize, hiddenSize].</p>
+      <p><em>hiddenState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②①">MLOperand</a></code>. The 2-D input hidden state tensor of shape [batchSize, hiddenSize].</p>
      <li data-md>
       <p><em>hiddenSize</em>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②⑨">unsigned long</a></code> scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlgrucelloptions" id="ref-for-dictdef-mlgrucelloptions②">MLGruCellOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②③">MLOperand</a></code>. The 2-D tensor of shape [batchSize, hiddenSize], the cell output hidden state of a single time step of the recurrent network.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②②">MLOperand</a></code>. The 2-D tensor of shape [batchSize, hiddenSize], the cell output hidden state of a single time step of the recurrent network.</p>
    </div>
    <details class="algorithm" data-algorithm="gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize, options)|gruCell(input, weight, recurrentWeight, hiddenState, hiddenSize)" id="dom-mlgraphbuilder-grucell"><code>gruCell(<var>input</var>, <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, <var>hiddenSize</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -4629,20 +4666,20 @@ <h4 class="heading settled" data-level="7.8.20" id="api-mlgraphbuilder-grucell">
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-activations" id="ref-for-dom-mlgruoptions-activations②">activations</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists③④">exists</a> and its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②①">size</a> is not 2, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①③③">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror⑧②">TypeError</a></code>.</p>
      <li data-md>
-      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑦">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑨">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions①⑧">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list①⑨">list</a> « <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape③⑤">shape</a>[0], <var>hiddenSize</var> ».</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⓪">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②③">list</a> « <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape③⑤">shape</a>[0], <var>hiddenSize</var> ».</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑤">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②④">dataType</a>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑦">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑥">dataType</a>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①④">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑤">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑥">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑦">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators②⑥">operator</a> for "gruCell", given <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, <var>hiddenSize</var> and <var>options</var> as parameters.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑤">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑧">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input②③">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -4757,7 +4794,7 @@ <h4 class="heading settled" data-level="7.8.21" id="api-mlgraphbuilder-hard-sigm
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③④"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-hardsigmoid" id="ref-for-dom-mlgraphbuilder-hardsigmoid"><c- g>hardSigmoid</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSigmoid(input, options), MLGraphBuilder/hardSigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardsigmoid-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardsigmoid-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlhardsigmoidoptions" id="ref-for-dictdef-mlhardsigmoidoptions"><c- n>MLHardSigmoidOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSigmoid(input, options), MLGraphBuilder/hardSigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardsigmoid-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardsigmoid-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-hardsigmoid" id="ref-for-dom-mlgraphbuilder-hardsigmoid"><c- g>hardSigmoid</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSigmoid(input, options), MLGraphBuilder/hardSigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardsigmoid-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardsigmoid-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlhardsigmoidoptions" id="ref-for-dictdef-mlhardsigmoidoptions"><c- n>MLHardSigmoidOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSigmoid(input, options), MLGraphBuilder/hardSigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardsigmoid-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardsigmoid-input-options-options"></a></dfn> = {});
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑥"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-hardsigmoid-options" id="ref-for-dom-mlgraphbuilder-hardsigmoid-options"><c- g>hardSigmoid</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlhardsigmoidoptions" id="ref-for-dictdef-mlhardsigmoidoptions①"><c- n>MLHardSigmoidOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSigmoid(options), MLGraphBuilder/hardSigmoid()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardsigmoid-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardsigmoid-options-options"></a></dfn> = {});
 };
 </pre>
@@ -4791,14 +4828,14 @@ <h5 class="heading settled" data-level="7.8.21.1" id="api-mlgraphbuilder-hardsig
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑥">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑤">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlhardsigmoidoptions" id="ref-for-dictdef-mlhardsigmoidoptions③">MLHardSigmoidOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑦">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑥">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="hardSigmoid(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -4812,7 +4849,7 @@ <h5 class="heading settled" data-level="7.8.21.1" id="api-mlgraphbuilder-hardsig
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators②⑦">operator</a> for the hard sigmoid operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑥">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑨">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input②④">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -4839,7 +4876,7 @@ <h5 class="heading settled" data-level="7.8.21.2" id="api-mlgraphbuilder-hardsig
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="hardSigmoid(options)|hardSigmoid()" id="dom-mlgraphbuilder-hardsigmoid-options"><code>hardSigmoid(<var>options</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation②">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑥">this</a>, "hardSigmoid" and <var>options</var>.</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation②">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑧">this</a>, "hardSigmoid" and <var>options</var>.</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①③④">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①③⑤">throw</a> the error.</p>
@@ -4851,7 +4888,7 @@ <h5 class="heading settled" data-level="7.8.21.2" id="api-mlgraphbuilder-hardsig
    <h4 class="heading settled" data-level="7.8.22" id="api-mlgraphbuilder-hard-swish"><span class="secno">7.8.22. </span><span class="content">hardSwish</span><a class="self-link" href="#api-mlgraphbuilder-hard-swish"></a></h4>
     Computes the nonlinear function <code>y = x * max(0, min(6, (x + 3))) / 6</code> that is introduced by <a data-link-type="biblio" href="#biblio-mobilenetv3" title="Searching for MobileNetV3">[MobileNetV3]</a> on the input tensor element-wise. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⑤"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-hardswish" id="ref-for-dom-mlgraphbuilder-hardswish"><c- g>hardSwish</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSwish(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardswish-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardswish-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-hardswish" id="ref-for-dom-mlgraphbuilder-hardswish"><c- g>hardSwish</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/hardSwish(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-hardswish-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-hardswish-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑧"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#hardswish-noargs" id="ref-for-hardswish-noargs"><c- g>hardSwish</c-></a>();
 };
 </pre>
@@ -4878,12 +4915,12 @@ <h5 class="heading settled" data-level="7.8.22.1" id="api-mlgraphbuilder-hardswi
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⓪">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑨">MLOperand</a></code>. The input tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③①">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⓪">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="hardSwish(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -4897,7 +4934,7 @@ <h5 class="heading settled" data-level="7.8.22.1" id="api-mlgraphbuilder-hardswi
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators②⑧">operator</a> for the hard-swish operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑦">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⓪">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input②⑤">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -4924,7 +4961,7 @@ <h5 class="heading settled" data-level="7.8.22.2" id="api-mlgraphbuilder-hardswi
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="hardswish-noargs"><code>hardSwish()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation③">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑦">this</a> and "hardSwish".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation③">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑨">this</a> and "hardSwish".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①③⑥">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①③⑦">throw</a> the error.</p>
@@ -4936,23 +4973,23 @@ <h5 class="heading settled" data-level="7.8.22.2" id="api-mlgraphbuilder-hardswi
    <h4 class="heading settled" data-level="7.8.23" id="api-mlgraphbuilder-instancenorm"><span class="secno">7.8.23. </span><span class="content">instanceNormalization</span><a class="self-link" href="#api-mlgraphbuilder-instancenorm"></a></h4>
     Normalize the input using <a data-link-type="biblio" href="#biblio-instance-normalization" title="Instance Normalization: The Missing Ingredient for Fast Stylization">[Instance-Normalization]</a>. Unlike <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-batchnormalization" id="ref-for-dom-mlgraphbuilder-batchnormalization②">batchNormalization()</a></code> where the mean and variance values used in the normalization are computed across all the samples in the batch dimension while the model is trained, the mean and variance values used in the instance normalization are computed on the fly for each input feature of each individual sample in the batch. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlinstancenormalizationoptions"><code><c- g>MLInstanceNormalizationOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlinstancenormalizationoptions-scale" id="ref-for-dom-mlinstancenormalizationoptions-scale"><c- g>scale</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlinstancenormalizationoptions-bias" id="ref-for-dom-mlinstancenormalizationoptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlinstancenormalizationoptions-scale" id="ref-for-dom-mlinstancenormalizationoptions-scale"><c- g>scale</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlinstancenormalizationoptions-bias" id="ref-for-dom-mlinstancenormalizationoptions-bias"><c- g>bias</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float②③"><c- b>float</c-></a> <a class="idl-code" data-default="1e-5" data-link-type="dict-member" data-type="float " href="#dom-mlinstancenormalizationoptions-epsilon" id="ref-for-dom-mlinstancenormalizationoptions-epsilon"><c- g>epsilon</c-></a> = 1e-5;
   <a data-link-type="idl-name" href="#enumdef-mlinputoperandlayout" id="ref-for-enumdef-mlinputoperandlayout④"><c- n>MLInputOperandLayout</c-></a> <a class="idl-code" data-default="&quot;nchw&quot;" data-link-type="dict-member" data-type="MLInputOperandLayout " href="#dom-mlinstancenormalizationoptions-layout" id="ref-for-dom-mlinstancenormalizationoptions-layout"><c- g>layout</c-></a> = "nchw";
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⑥"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-instancenormalization" id="ref-for-dom-mlgraphbuilder-instancenormalization"><c- g>instanceNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/instanceNormalization(input, options), MLGraphBuilder/instanceNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-instancenormalization-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-instancenormalization-input-options-input"></a></dfn>,
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-instancenormalization" id="ref-for-dom-mlgraphbuilder-instancenormalization"><c- g>instanceNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/instanceNormalization(input, options), MLGraphBuilder/instanceNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-instancenormalization-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-instancenormalization-input-options-input"></a></dfn>,
                                 <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlinstancenormalizationoptions" id="ref-for-dictdef-mlinstancenormalizationoptions"><c- n>MLInstanceNormalizationOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/instanceNormalization(input, options), MLGraphBuilder/instanceNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-instancenormalization-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-instancenormalization-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlinstancenormalizationoptions" id="ref-for-dictdef-mlinstancenormalizationoptions①">MLInstanceNormalizationOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLInstanceNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlinstancenormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑥">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLInstanceNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlinstancenormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑤">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D tensor of the scaling values whose <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②②">size</a> is equal to the number of channels, i.e. the size of the feature dimension of the input. For example, for an <var>input</var> tensor with <code>nchw</code> layout, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②③">size</a> is equal to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape③⑥">shape</a>[1].</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLInstanceNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlinstancenormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑦">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLInstanceNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlinstancenormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑥">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D tensor of the bias values whose <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②④">size</a> is equal to the size of the feature dimension of the input. For example, for an <var>input</var> tensor with <code>nchw</code> layout, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②⑤">size</a> is equal to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape③⑦">shape</a>[1].</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLInstanceNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlinstancenormalizationoptions-epsilon"><code>epsilon</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float②④">float</a>, defaulting to <code>1e-5</code></span>
@@ -4966,11 +5003,11 @@ <h4 class="heading settled" data-level="7.8.23" id="api-mlgraphbuilder-instancen
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⑧">MLOperand</a></code>. The input 4-D tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⑦">MLOperand</a></code>. The input 4-D tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlinstancenormalizationoptions" id="ref-for-dictdef-mlinstancenormalizationoptions②">MLInstanceNormalizationOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⑨">MLOperand</a></code>. The instance-normalized 4-D tensor of the same shape as <em>input</em>.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⑧">MLOperand</a></code>. The instance-normalized 4-D tensor of the same shape as <em>input</em>.</p>
    </div>
    <details class="algorithm" data-algorithm="instanceNormalization(input, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="instanceNormalization(input, options)|instanceNormalization(input)" id="dom-mlgraphbuilder-instancenormalization"><code>instanceNormalization(<var>input</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -4989,7 +5026,7 @@ <h4 class="heading settled" data-level="7.8.23" id="api-mlgraphbuilder-instancen
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators②⑨">operator</a> for the instance normalization operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑧">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②①">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input②⑥">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -5038,22 +5075,22 @@ <h4 class="heading settled" data-level="7.8.23" id="api-mlgraphbuilder-instancen
    <h4 class="heading settled" data-level="7.8.24" id="api-mlgraphbuilder-layernorm"><span class="secno">7.8.24. </span><span class="content">layerNormalization</span><a class="self-link" href="#api-mlgraphbuilder-layernorm"></a></h4>
     Normalize the input using <a data-link-type="biblio" href="#biblio-layer-normalization" title="Layer Normalization">[Layer-Normalization]</a>. Unlike <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-batchnormalization" id="ref-for-dom-mlgraphbuilder-batchnormalization③">batchNormalization()</a></code> where the mean and variance values are computed across all the samples in the batch dimension while the model is trained, and in <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-instancenormalization" id="ref-for-dom-mlgraphbuilder-instancenormalization①">instanceNormalization()</a></code> where the mean and variance values are computed on the fly for each input feature of each individual sample in the batch, the means and variance values of the layer normalization are computed on the fly across all the input features of each individual sample in the batch. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mllayernormalizationoptions"><code><c- g>MLLayerNormalizationOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllayernormalizationoptions-scale" id="ref-for-dom-mllayernormalizationoptions-scale"><c- g>scale</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllayernormalizationoptions-bias" id="ref-for-dom-mllayernormalizationoptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllayernormalizationoptions-scale" id="ref-for-dom-mllayernormalizationoptions-scale"><c- g>scale</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllayernormalizationoptions-bias" id="ref-for-dom-mllayernormalizationoptions-bias"><c- g>bias</c-></a>;
   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑥"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange①⑨"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⓪"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<[EnforceRange] unsigned long> " href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes"><c- g>axes</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float②⑤"><c- b>float</c-></a> <a class="idl-code" data-default="1e-5" data-link-type="dict-member" data-type="float " href="#dom-mllayernormalizationoptions-epsilon" id="ref-for-dom-mllayernormalizationoptions-epsilon"><c- g>epsilon</c-></a> = 1e-5;
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-layernormalization" id="ref-for-dom-mlgraphbuilder-layernormalization"><c- g>layerNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/layerNormalization(input, options), MLGraphBuilder/layerNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-layernormalization-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-layernormalization-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllayernormalizationoptions" id="ref-for-dictdef-mllayernormalizationoptions"><c- n>MLLayerNormalizationOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/layerNormalization(input, options), MLGraphBuilder/layerNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-layernormalization-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-layernormalization-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-layernormalization" id="ref-for-dom-mlgraphbuilder-layernormalization"><c- g>layerNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/layerNormalization(input, options), MLGraphBuilder/layerNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-layernormalization-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-layernormalization-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllayernormalizationoptions" id="ref-for-dictdef-mllayernormalizationoptions"><c- n>MLLayerNormalizationOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/layerNormalization(input, options), MLGraphBuilder/layerNormalization(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-layernormalization-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-layernormalization-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mllayernormalizationoptions" id="ref-for-dictdef-mllayernormalizationoptions①">MLLayerNormalizationOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLayerNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mllayernormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④④">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLayerNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mllayernormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④③">MLOperand</a></span>
     <dd data-md>
      <p>The N-D tensor of the scaling values whose shape is determined by the <var>axes</var> member in that each value in <var>axes</var> indicates the dimension of the input tensor with scaling values. For example, for an <var>axes</var> values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the scaling value is assumed to be 1.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLayerNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mllayernormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⑤">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLayerNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mllayernormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④④">MLOperand</a></span>
     <dd data-md>
      <p>The N-D tensor of the bias values whose shape is determined by the <var>axes</var> member in that each value in <var>axes</var> indicates the dimension of the input tensor with bias values. For example, for an <var>axes</var> values of [1,2,3], the shape of this tensor is the list of the corresponding sizes of the input dimension 1, 2 and 3. When this member is not present, the bias value is assumed to be 0.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLayerNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mllayernormalizationoptions-axes"><code>axes</code></dfn>, <span> of type <code class="idl-code">sequence&lt;[EnforceRange] unsigned long></code></span>
@@ -5067,17 +5104,17 @@ <h4 class="heading settled" data-level="7.8.24" id="api-mlgraphbuilder-layernorm
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②④⑥">MLOperand</a></code>. The input N-D tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②④⑤">MLOperand</a></code>. The input N-D tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mllayernormalizationoptions" id="ref-for-dictdef-mllayernormalizationoptions②">MLLayerNormalizationOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②④⑦">MLOperand</a></code>. The layer-normalized N-D tensor of the same shape as <em>input</em>.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②④⑥">MLOperand</a></code>. The layer-normalized N-D tensor of the same shape as <em>input</em>.</p>
    </div>
    <details class="algorithm" data-algorithm="layerNormalization(input, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="layerNormalization(input, options)|layerNormalization(input)" id="dom-mlgraphbuilder-layernormalization"><code>layerNormalization(<var>input</var>, <var>options</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes①">axes</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists③⑦">exist</a>, then set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes②">axes</a></code> to a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⓪">list</a>, either equal to <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range⑤">the range</a> from 1 to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank③④">rank</a>, exclusive, if <var>input</var>’s <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank③⑤">rank</a> is greater than 1, or an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②①">list</a> otherwise.</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes①">axes</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists③⑦">exist</a>, then set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes②">axes</a></code> to a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②④">list</a>, either equal to <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range⑤">the range</a> from 1 to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank③④">rank</a>, exclusive, if <var>input</var>’s <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank③⑤">rank</a> is greater than 1, or an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑤">list</a> otherwise.</p>
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-scale" id="ref-for-dom-mllayernormalizationoptions-scale①">scale</a></code>'s <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank③⑥">rank</a> is not equal to <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllayernormalizationoptions-axes" id="ref-for-dom-mllayernormalizationoptions-axes③">axes</a></code>'s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②⑥">size</a>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①④①">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror⑧⑥">TypeError</a></code>.</p>
      <li data-md>
@@ -5104,7 +5141,7 @@ <h4 class="heading settled" data-level="7.8.24" id="api-mlgraphbuilder-layernorm
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⓪">operator</a> for the layer normalization operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot①⑨">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②②">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input②⑨">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -5156,7 +5193,7 @@ <h4 class="heading settled" data-level="7.8.25" id="api-mlgraphbuilder-leakyrelu
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-leakyrelu" id="ref-for-dom-mlgraphbuilder-leakyrelu"><c- g>leakyRelu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/leakyRelu(input, options), MLGraphBuilder/leakyRelu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-leakyrelu-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-leakyrelu-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlleakyreluoptions" id="ref-for-dictdef-mlleakyreluoptions"><c- n>MLLeakyReluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/leakyRelu(input, options), MLGraphBuilder/leakyRelu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-leakyrelu-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-leakyrelu-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-leakyrelu" id="ref-for-dom-mlgraphbuilder-leakyrelu"><c- g>leakyRelu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/leakyRelu(input, options), MLGraphBuilder/leakyRelu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-leakyrelu-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-leakyrelu-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlleakyreluoptions" id="ref-for-dictdef-mlleakyreluoptions"><c- n>MLLeakyReluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/leakyRelu(input, options), MLGraphBuilder/leakyRelu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-leakyrelu-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-leakyrelu-input-options-options"></a></dfn> = {});
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③⓪"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-leakyrelu-options" id="ref-for-dom-mlgraphbuilder-leakyrelu-options"><c- g>leakyRelu</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlleakyreluoptions" id="ref-for-dictdef-mlleakyreluoptions①"><c- n>MLLeakyReluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/leakyRelu(options), MLGraphBuilder/leakyRelu()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-leakyrelu-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-leakyrelu-options-options"></a></dfn> = {});
 };
 </pre>
@@ -5182,14 +5219,14 @@ <h5 class="heading settled" data-level="7.8.25.1" id="api-mlgraphbuilder-leaky-r
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⓪">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②④⑨">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlleakyreluoptions" id="ref-for-dictdef-mlleakyreluoptions③">MLLeakyReluOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤①">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⓪">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="leakyRelu(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -5203,7 +5240,7 @@ <h5 class="heading settled" data-level="7.8.25.1" id="api-mlgraphbuilder-leaky-r
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③①">operator</a> for the Leaky RELU operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⓪">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②③">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input③②">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -5230,7 +5267,7 @@ <h5 class="heading settled" data-level="7.8.25.2" id="api-mlgraphbuilder-leaky-r
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="leakyRelu(options)|leakyRelu()" id="dom-mlgraphbuilder-leakyrelu-options"><code>leakyRelu(<var>options</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation④">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑧">this</a>, "leakyRelu" and <var>options</var>.</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation④">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⓪">this</a>, "leakyRelu" and <var>options</var>.</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①④⑥">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①④⑦">throw</a> the error.</p>
@@ -5247,7 +5284,7 @@ <h4 class="heading settled" data-level="7.8.26" id="api-mlgraphbuilder-linear"><
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⑨"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-linear" id="ref-for-dom-mlgraphbuilder-linear"><c- g>linear</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/linear(input, options), MLGraphBuilder/linear(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-linear-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-linear-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllinearoptions" id="ref-for-dictdef-mllinearoptions"><c- n>MLLinearOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/linear(input, options), MLGraphBuilder/linear(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-linear-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-linear-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-linear" id="ref-for-dom-mlgraphbuilder-linear"><c- g>linear</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/linear(input, options), MLGraphBuilder/linear(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-linear-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-linear-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllinearoptions" id="ref-for-dictdef-mllinearoptions"><c- n>MLLinearOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/linear(input, options), MLGraphBuilder/linear(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-linear-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-linear-input-options-options"></a></dfn> = {});
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③②"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-linear-options" id="ref-for-dom-mlgraphbuilder-linear-options"><c- g>linear</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllinearoptions" id="ref-for-dictdef-mllinearoptions①"><c- n>MLLinearOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/linear(options), MLGraphBuilder/linear()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-linear-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-linear-options-options"></a></dfn> = {});
 };
 </pre>
@@ -5277,14 +5314,14 @@ <h5 class="heading settled" data-level="7.8.26.1" id="api-mlgraphbuilder-linear-
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤④">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤③">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mllinearoptions" id="ref-for-dictdef-mllinearoptions③">MLLinearOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑤">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤④">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="linear(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -5298,7 +5335,7 @@ <h5 class="heading settled" data-level="7.8.26.1" id="api-mlgraphbuilder-linear-
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③②">operator</a> for the linear operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②①">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②④">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input③③">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -5325,7 +5362,7 @@ <h5 class="heading settled" data-level="7.8.26.2" id="api-mlgraphbuilder-linear-
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="linear(options)|linear()" id="dom-mlgraphbuilder-linear-options"><code>linear(<var>options</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑤">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑨">this</a>,  "linear" and <var>options</var>.</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑤">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④①">this</a>,  "linear" and <var>options</var>.</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①④⑧">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①④⑨">throw</a> the error.</p>
@@ -5342,11 +5379,11 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
 };
 
 <c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mllstmoptions"><code><c- g>MLLstmOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-bias" id="ref-for-dom-mllstmoptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-recurrentbias" id="ref-for-dom-mllstmoptions-recurrentbias"><c- g>recurrentBias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-peepholeweight" id="ref-for-dom-mllstmoptions-peepholeweight"><c- g>peepholeWeight</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-initialhiddenstate" id="ref-for-dom-mllstmoptions-initialhiddenstate"><c- g>initialHiddenState</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-initialcellstate" id="ref-for-dom-mllstmoptions-initialcellstate"><c- g>initialCellState</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-bias" id="ref-for-dom-mllstmoptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-recurrentbias" id="ref-for-dom-mllstmoptions-recurrentbias"><c- g>recurrentBias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-peepholeweight" id="ref-for-dom-mllstmoptions-peepholeweight"><c- g>peepholeWeight</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-initialhiddenstate" id="ref-for-dom-mllstmoptions-initialhiddenstate"><c- g>initialHiddenState</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmoptions-initialcellstate" id="ref-for-dom-mllstmoptions-initialcellstate"><c- g>initialCellState</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①⑥"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mllstmoptions-returnsequence" id="ref-for-dom-mllstmoptions-returnsequence"><c- g>returnSequence</c-></a> = <c- b>false</c->;
   <a data-link-type="idl-name" href="#enumdef-mlrecurrentnetworkdirection" id="ref-for-enumdef-mlrecurrentnetworkdirection②"><c- n>MLRecurrentNetworkDirection</c-></a> <a class="idl-code" data-default="&quot;forward&quot;" data-link-type="dict-member" data-type="MLRecurrentNetworkDirection " href="#dom-mllstmoptions-direction" id="ref-for-dom-mllstmoptions-direction"><c- g>direction</c-></a> = "forward";
   <a data-link-type="idl-name" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout"><c- n>MLLstmWeightLayout</c-></a> <a class="idl-code" data-default="&quot;iofg&quot;" data-link-type="dict-member" data-type="MLLstmWeightLayout " href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout"><c- g>layout</c-></a> = "iofg";
@@ -5354,9 +5391,9 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑧"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥①"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lstm" id="ref-for-dom-mlgraphbuilder-lstm"><c- g>lstm</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-input"></a></dfn>,
-                           <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-weight"></a></dfn>,
-                           <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"></a></dfn>,
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑧"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⓪"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lstm" id="ref-for-dom-mlgraphbuilder-lstm"><c- g>lstm</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-input"></a></dfn>,
+                           <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-weight"></a></dfn>,
+                           <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"></a></dfn>,
                            [<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange②⓪"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③①"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-steps"><code><c- g>steps</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-steps"></a></dfn>,
                            [<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange②①"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③②"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-hiddensize"><code><c- g>hiddenSize</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-hiddensize"></a></dfn>,
                            <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllstmoptions" id="ref-for-dictdef-mllstmoptions"><c- n>MLLstmOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/lstm(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstm-input-weight-recurrentweight-steps-hiddensize-options-options"></a></dfn> = {});
@@ -5364,19 +5401,19 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mllstmoptions" id="ref-for-dictdef-mllstmoptions①">MLLstmOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑤">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥④">MLOperand</a></span>
     <dd data-md>
      <p>The 2-D input bias tensor of shape [numDirections, 4 * hiddenSize]. The ordering of the bias vectors in the second dimension of the tensor shape is specified according to <code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout①">layout</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑥">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑤">MLOperand</a></span>
     <dd data-md>
      <p>The 2-D recurrent bias tensor of shape [numDirections, 4 * hiddenSize]. The ordering of the bias vectors in the first dimension of the tensor shape is specified according to <code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout②">layout</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-peepholeweight"><code>peepholeWeight</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑦">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-peepholeweight"><code>peepholeWeight</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑥">MLOperand</a></span>
     <dd data-md>
      <p>The 2-D weight tensor for peepholes of shape [numDirections, 3 * hiddenSize]. The pack ordering of the weight vectors is for the <code>input (i)</code>, <code>output (o)</code>, and <code>forget (f)</code> gate, respectively.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-initialhiddenstate"><code>initialHiddenState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑧">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-initialhiddenstate"><code>initialHiddenState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑦">MLOperand</a></span>
     <dd data-md>
      <p>The 3-D initial hidden state tensor of shape [numDirections, batchSize, hiddenSize]. When not specified, implementations SHOULD use a tensor filled with zero.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-initialcellstate"><code>initialCellState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑨">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-initialcellstate"><code>initialCellState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥⑧">MLOperand</a></span>
     <dd data-md>
      <p>The 3-D initial hidden state tensor of shape [numDirections, batchSize, hiddenSize]. When not specified, implementations SHOULD use a tensor filled with zero.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-returnsequence"><code>returnSequence</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①⑦">boolean</a>, defaulting to <code>false</code></span>
@@ -5396,11 +5433,11 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦⓪">MLOperand</a></code>. The input 3-D tensor of shape [steps, batchSize, inputSize].</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑥⑨">MLOperand</a></code>. The input 3-D tensor of shape [steps, batchSize, inputSize].</p>
      <li data-md>
-      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦①">MLOperand</a></code>. The 3-D input weight tensor of shape [numDirections, 4 * hiddenSize, inputSize]. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to the <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout③">layout</a></code>.</p>
+      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦⓪">MLOperand</a></code>. The 3-D input weight tensor of shape [numDirections, 4 * hiddenSize, inputSize]. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to the <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout③">layout</a></code>.</p>
      <li data-md>
-      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦②">MLOperand</a></code>. The 3-D recurrent weight tensor of shape [numDirections, 4 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to the <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout④">layout</a></code> argument.</p>
+      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦①">MLOperand</a></code>. The 3-D recurrent weight tensor of shape [numDirections, 4 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the second dimension of the tensor shape is specified according to the <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-layout" id="ref-for-dom-mllstmoptions-layout④">layout</a></code> argument.</p>
      <li data-md>
       <p><em>steps</em>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③③">unsigned long</a></code> scalar. The number of time steps in the recurrent network. The value must be greater than 0.</p>
      <li data-md>
@@ -5408,7 +5445,7 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mllstmoptions" id="ref-for-dictdef-mllstmoptions②">MLLstmOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦③">MLOperand</a></code>. The first element of the sequence is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the output cell state from the last time step of the network. Additionally, if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-returnsequence" id="ref-for-dom-mllstmoptions-returnsequence①">returnSequence</a></code> is set to true, the third element is the 4-D output tensor of shape [steps, numDirections, batchSize, hiddenSize] containing every output from each time step in the temporal sequence.</p>
+    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦②">MLOperand</a></code>. The first element of the sequence is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the output hidden state from the last time step of the network. The second element is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the output cell state from the last time step of the network. Additionally, if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-returnsequence" id="ref-for-dom-mllstmoptions-returnsequence①">returnSequence</a></code> is set to true, the third element is the 4-D output tensor of shape [steps, numDirections, batchSize, hiddenSize] containing every output from each time step in the temporal sequence.</p>
    </div>
    <details class="algorithm" data-algorithm="lstm(input, weight, recurrentWeight, steps, hiddenSize, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="lstm(input, weight, recurrentWeight, steps, hiddenSize, options)|lstm(input, weight, recurrentWeight, steps, hiddenSize)" id="dom-mlgraphbuilder-lstm"><code>lstm(<var>input</var>, <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -5485,20 +5522,20 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
       <p><em>Calculate the output shape:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑧">MLOperandDescriptor</a></code>.</p>
+        <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⓪">MLOperandDescriptor</a></code>.</p>
        <li data-md>
-        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions①⑨">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②②">list</a> « <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
+        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②①">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑥">list</a> « <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
        <li data-md>
-        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑥">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑤">dataType</a>.</p>
+        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑧">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑦">dataType</a>.</p>
        <li data-md>
         <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-returnsequence" id="ref-for-dom-mllstmoptions-returnsequence②">returnSequence</a></code> is true:</p>
         <ol>
          <li data-md>
-          <p>Let <var>desc2</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑨">MLOperandDescriptor</a></code>.</p>
+          <p>Let <var>desc2</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③①">MLOperandDescriptor</a></code>.</p>
          <li data-md>
-          <p>Set <var>desc2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑦">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑥">dataType</a>.</p>
+          <p>Set <var>desc2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑨">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑧">dataType</a>.</p>
          <li data-md>
-          <p>Set <var>desc2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⓪">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②③">list</a> « <var>steps</var>, <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
+          <p>Set <var>desc2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②②">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑦">list</a> « <var>steps</var>, <var>numDirections</var>, <var>batchSize</var>, <var>hiddenSize</var> ».</p>
         </ol>
       </ol>
      <li data-md>
@@ -5507,25 +5544,27 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③③">operator</a> for the LSTM operation, given <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var> and <var>options</var>.</p>
        <li data-md>
-        <p>Let <var>output0</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑤">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⓪">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output0</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑦">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④②">this</a> and <var>desc</var>.</p>
        <li data-md>
-        <p>Let <var>output1</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑥">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④①">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output1</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑧">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④③">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mllstmoptions-returnsequence" id="ref-for-dom-mllstmoptions-returnsequence③">returnSequence</a></code> is true:</p>
         <ol>
          <li data-md>
-          <p>Let <var>output2</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑦">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④②">this</a> and <var>desc2</var>.</p>
+          <p>Let <var>output2</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑨">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④④">this</a> and <var>desc2</var>.</p>
          <li data-md>
-          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②④">list</a> « <var>output0</var>, <var>output1</var>, <var>output2</var> ».</p>
+          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑧">list</a> « <var>output0</var>, <var>output1</var>, <var>output2</var> ».</p>
+         <li data-md>
+          <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑤">[[operator]]</a></code>, <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑥">[[operator]]</a></code> and <var>output2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑦">[[operator]]</a></code> to <var>operator</var>.</p>
         </ol>
        <li data-md>
         <p>Otherwise:</p>
         <ol>
          <li data-md>
-          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑤">list</a> « <var>output0</var>, <var>output1</var> ».</p>
+          <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑨">list</a> « <var>output0</var>, <var>output1</var> ».</p>
+         <li data-md>
+          <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑧">[[operator]]</a></code> and <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑨">[[operator]]</a></code> to <var>operator</var>.</p>
         </ol>
-       <li data-md>
-        <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②②">[[operator]]</a></code>, <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②③">[[operator]]</a></code> and <var>output2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②④">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input③④">inputs</a> to <var>input</var>, <var>weight</var>, and <var>recurrentWeight</var>.</p>
        <li data-md>
@@ -5629,32 +5668,32 @@ <h4 class="heading settled" data-level="7.8.27" id="api-mlgraphbuilder-lstm"><sp
    <h4 class="heading settled" data-level="7.8.28" id="api-mlgraphbuilder-lstmcell"><span class="secno">7.8.28. </span><span class="content">lstmCell</span><a class="self-link" href="#api-mlgraphbuilder-lstmcell"></a></h4>
     A single time step of the Long Short-Term Memory <a data-link-type="biblio" href="#biblio-lstm" title="Long Short-Term Memory">[LSTM]</a> recurrent network using a cell state, an input, output, and forget gate to compute the cell state and the hidden state of the next time step that rolls into the output across the temporal sequence of the network. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mllstmcelloptions"><code><c- g>MLLstmCellOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-bias" id="ref-for-dom-mllstmcelloptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-recurrentbias" id="ref-for-dom-mllstmcelloptions-recurrentbias"><c- g>recurrentBias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-peepholeweight" id="ref-for-dom-mllstmcelloptions-peepholeweight"><c- g>peepholeWeight</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-bias" id="ref-for-dom-mllstmcelloptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-recurrentbias" id="ref-for-dom-mllstmcelloptions-recurrentbias"><c- g>recurrentBias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mllstmcelloptions-peepholeweight" id="ref-for-dom-mllstmcelloptions-peepholeweight"><c- g>peepholeWeight</c-></a>;
   <a data-link-type="idl-name" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout②"><c- n>MLLstmWeightLayout</c-></a> <a class="idl-code" data-default="&quot;iofg&quot;" data-link-type="dict-member" data-type="MLLstmWeightLayout " href="#dom-mllstmcelloptions-layout" id="ref-for-dom-mllstmcelloptions-layout"><c- g>layout</c-></a> = "iofg";
   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑨"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③⑥"><c- n>MLActivation</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<MLActivation> " href="#dom-mllstmcelloptions-activations" id="ref-for-dom-mllstmcelloptions-activations"><c- g>activations</c-></a>;
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④①"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②⓪"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑦"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lstmcell" id="ref-for-dom-mlgraphbuilder-lstmcell"><c- g>lstmCell</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-input"></a></dfn>,
-                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-weight"></a></dfn>,
-                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-recurrentweight"></a></dfn>,
-                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddenstate"><code><c- g>hiddenState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddenstate"></a></dfn>,
-                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-cellstate"><code><c- g>cellState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-cellstate"></a></dfn>,
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②⓪"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑥"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lstmcell" id="ref-for-dom-mlgraphbuilder-lstmcell"><c- g>lstmCell</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-input"></a></dfn>,
+                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-weight"></a></dfn>,
+                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-recurrentweight"></a></dfn>,
+                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddenstate"><code><c- g>hiddenState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddenstate"></a></dfn>,
+                               <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-cellstate"><code><c- g>cellState</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-cellstate"></a></dfn>,
                                [<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange②②"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⑤"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddensize"><code><c- g>hiddenSize</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-hiddensize"></a></dfn>,
                                <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mllstmcelloptions" id="ref-for-dictdef-mllstmcelloptions"><c- n>MLLstmCellOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options), MLGraphBuilder/lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lstmcell-input-weight-recurrentweight-hiddenstate-cellstate-hiddensize-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mllstmcelloptions" id="ref-for-dictdef-mllstmcelloptions①">MLLstmCellOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧③">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧②">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D input bias tensor of shape [4 * hiddenSize]. The ordering of the bias vectors in the first dimension of the tensor shape is specified according to the <code class="idl"><a data-link-type="idl" href="#dom-mllstmcelloptions-layout" id="ref-for-dom-mllstmcelloptions-layout①">layout</a></code> argument.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧④">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧③">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D recurrent bias tensor of shape [4 * hiddenSize]. The ordering of the bias vectors in the first dimension of the tensor shape is specified according to the <code class="idl"><a data-link-type="idl" href="#dom-mllstmcelloptions-layout" id="ref-for-dom-mllstmcelloptions-layout②">layout</a></code> argument.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-peepholeweight"><code>peepholeWeight</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧⑤">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-peepholeweight"><code>peepholeWeight</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧④">MLOperand</a></span>
     <dd data-md>
      <p>The 1-D weight tensor for peepholes of shape [3 * hiddenSize]. The pack ordering of the weight vectors is for the <code>input (i)</code>, <code>output (o)</code>, and <code>forget (f)</code> gate, respectively.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-layout"><code>layout</code></dfn>, <span> of type <a data-link-type="idl-name" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout③">MLLstmWeightLayout</a>, defaulting to <code>"iofg"</code></span>
@@ -5668,21 +5707,21 @@ <h4 class="heading settled" data-level="7.8.28" id="api-mlgraphbuilder-lstmcell"
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑥">MLOperand</a></code>. The input 2-D tensor of shape [batchSize, inputSize].</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑤">MLOperand</a></code>. The input 2-D tensor of shape [batchSize, inputSize].</p>
      <li data-md>
-      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑦">MLOperand</a></code>. The 2-D input weight tensor of shape [4 * hiddenSize, inputSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
+      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑥">MLOperand</a></code>. The 2-D input weight tensor of shape [4 * hiddenSize, inputSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
      <li data-md>
-      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑧">MLOperand</a></code>. The 2-D recurrent weight tensor of shape [4 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
+      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑦">MLOperand</a></code>. The 2-D recurrent weight tensor of shape [4 * hiddenSize, hiddenSize]. The ordering of the weight vectors in the first dimension of the tensor shape is specified according to the <em>options.layout</em> argument.</p>
      <li data-md>
-      <p><em>hiddenState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑨">MLOperand</a></code>. The 2-D input hidden state tensor of shape [batchSize, hiddenSize].</p>
+      <p><em>hiddenState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑧">MLOperand</a></code>. The 2-D input hidden state tensor of shape [batchSize, hiddenSize].</p>
      <li data-md>
-      <p><em>cellState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⓪">MLOperand</a></code>. The 2-D input cell state tensor of shape [batchSize, hiddenSize].</p>
+      <p><em>cellState</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⑨">MLOperand</a></code>. The 2-D input cell state tensor of shape [batchSize, hiddenSize].</p>
      <li data-md>
       <p><em>hiddenSize</em>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⑥">unsigned long</a></code> scalar. The value of the second dimension of the output tensor shape. It indicates the number of features in the hidden state.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mllstmcelloptions" id="ref-for-dictdef-mllstmcelloptions②">MLLstmCellOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨①">MLOperand</a></code>. The first element of the sequence is the output hidden state of the current time step of the recurrent network. The following element is the output cell state. Both elements are 2-D tensors of shape [batchSize, hiddenSize].</p>
+    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⓪">MLOperand</a></code>. The first element of the sequence is the output hidden state of the current time step of the recurrent network. The following element is the output cell state. Both elements are 2-D tensors of shape [batchSize, hiddenSize].</p>
    </div>
    <details class="algorithm" data-algorithm="lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize, options)|lstmCell(input, weight, recurrentWeight, hiddenState, cellState, hiddenSize)" id="dom-mlgraphbuilder-lstmcell"><code>lstmCell(<var>input</var>, <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, <var>cellState</var>, <var>hiddenSize</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -5722,24 +5761,24 @@ <h4 class="heading settled" data-level="7.8.28" id="api-mlgraphbuilder-lstmcell"
         <p>If its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③⓪">size</a> is not 3, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑦⑦">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①①⑧">TypeError</a></code>.</p>
       </ol>
      <li data-md>
-      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⓪">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③②">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②①">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑥">list</a> « <var>batchSize</var>, <var>hiddenSize</var> ».</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②③">dimensions</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⓪">list</a> « <var>batchSize</var>, <var>hiddenSize</var> ».</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑧">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑦">dataType</a>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②⓪">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑨">dataType</a>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output0</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑧">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④③">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output0</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⓪">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑤">this</a> and <var>desc</var>.</p>
        <li data-md>
-        <p>Let <var>output1</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①⑨">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④④">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output1</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②①">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑥">this</a> and <var>desc</var>.</p>
        <li data-md>
-        <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑦">list</a> « <var>output0</var>, <var>output1</var> ».</p>
+        <p>Let <var>output</var> be the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③①">list</a> « <var>output0</var>, <var>output1</var> ».</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③④">operator</a> for the LSTM cell operation, given <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, <var>cellState</var>, <var>hiddenSize</var> and <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑤">[[operator]]</a></code> and <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑥">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⓪">[[operator]]</a></code> and <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③①">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⓪">inputs</a> to <var>input</var>, <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, and <var>cellState</var>.</p>
        <li data-md>
@@ -5871,18 +5910,18 @@ <h4 class="heading settled" data-level="7.8.28" id="api-mlgraphbuilder-lstmcell"
    <h4 class="heading settled" data-level="7.8.29" id="api-mlgraphbuilder-matmul"><span class="secno">7.8.29. </span><span class="content">matmul</span><a class="self-link" href="#api-mlgraphbuilder-matmul"></a></h4>
     Compute the matrix product of two input tensors. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④②"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-matmul" id="ref-for-dom-mlgraphbuilder-matmul"><c- g>matmul</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/matmul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-matmul-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-matmul-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/matmul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-matmul-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-matmul-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-matmul" id="ref-for-dom-mlgraphbuilder-matmul"><c- g>matmul</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/matmul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-matmul-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-matmul-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/matmul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-matmul-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-matmul-a-b-b"></a></dfn>);
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>a</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑤">MLOperand</a></code>. The first input tensor which is at least 2-D.</p>
+      <p><em>a</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨④">MLOperand</a></code>. The first input tensor which is at least 2-D.</p>
      <li data-md>
-      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑥">MLOperand</a></code>. The second input tensor which is at least 2-D.</p>
+      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑤">MLOperand</a></code>. The second input tensor which is at least 2-D.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑦">MLOperand</a></code>. The output tensor that contains the matrix
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑥">MLOperand</a></code>. The output tensor that contains the matrix
     product of two input tensors.</p>
    </div>
    <div>
@@ -5934,22 +5973,22 @@ <h4 class="heading settled" data-level="7.8.29" id="api-mlgraphbuilder-matmul"><
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="dom-mlgraphbuilder-matmul"><code>matmul(<var>a</var>, <var>b</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③①">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③③">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②②">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-matmul-output-sizes" id="ref-for-mlgraphbuilder-calculate-matmul-output-sizes">calculating matmul output sizes</a> given <var>a</var> and <var>b</var>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②④">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-matmul-output-sizes" id="ref-for-mlgraphbuilder-calculate-matmul-output-sizes">calculating matmul output sizes</a> given <var>a</var> and <var>b</var>.</p>
      <li data-md>
       <p>If that throws an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑧①">throw</a> the error.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype①⑨">dataType</a></code> to <var>a</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑧">dataType</a>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②①">dataType</a></code> to <var>a</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③⓪">dataType</a>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⓪">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑤">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②②">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑦">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⑤">operator</a> for the matrix multiplication operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑦">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③②">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④④">inputs</a> to <var>a</var> and <var>b</var>.</p>
        <li data-md>
@@ -5974,7 +6013,7 @@ <h4 class="heading settled" data-level="7.8.30" id="api-mlgraphbuilder-pad"><spa
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④③"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-pad" id="ref-for-dom-mlgraphbuilder-pad"><c- g>pad</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options), MLGraphBuilder/pad(input, beginningPadding, endingPadding)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-input"></a></dfn>,
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-pad" id="ref-for-dom-mlgraphbuilder-pad"><c- g>pad</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options), MLGraphBuilder/pad(input, beginningPadding, endingPadding)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-input"></a></dfn>,
                 <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②①"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange②③"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⑦"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options), MLGraphBuilder/pad(input, beginningPadding, endingPadding)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-beginningpadding"><code><c- g>beginningPadding</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-beginningpadding"></a></dfn>,
                 <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②②"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange②④"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⑧"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options), MLGraphBuilder/pad(input, beginningPadding, endingPadding)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-endingpadding"><code><c- g>endingPadding</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-endingpadding"></a></dfn>,
                 <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpadoptions" id="ref-for-dictdef-mlpadoptions"><c- n>MLPadOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pad(input, beginningPadding, endingPadding, options), MLGraphBuilder/pad(input, beginningPadding, endingPadding)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pad-input-beginningpadding-endingpadding-options-options"></a></dfn> = {});
@@ -5993,7 +6032,7 @@ <h4 class="heading settled" data-level="7.8.30" id="api-mlgraphbuilder-pad"><spa
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⓪">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨⑨">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>beginningPadding</em>: a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③⑨">unsigned long</a></code>. The sequence of unsigned integer values indicating the number of padding values to add at the beginning of each input dimension, of length <em>N</em> where <em>N</em> is the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑤⓪">rank</a> of the input tensor. For each dimension <em>d</em> of <em>input</em>, <em>beginningPadding[d]</em> indicates how many values to add before the content in that dimension.</p>
      <li data-md>
@@ -6001,7 +6040,7 @@ <h4 class="heading settled" data-level="7.8.30" id="api-mlgraphbuilder-pad"><spa
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlpadoptions" id="ref-for-dictdef-mlpadoptions②">MLPadOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪①">MLOperand</a></code>. The padded output tensor. Each dimension of the output tensor can be calculated as follow:</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⓪">MLOperand</a></code>. The padded output tensor. Each dimension of the output tensor can be calculated as follow:</p>
     <p><em>output size = beginning padding + input size + ending padding</em></p>
    </div>
    <details class="algorithm" data-algorithm="calculate padding output sizes" data-algorithm-for="MLGraphBuilder" open>
@@ -6029,16 +6068,16 @@ <h4 class="heading settled" data-level="7.8.30" id="api-mlgraphbuilder-pad"><spa
      <li data-md>
       <p>Let <var>desc</var> be a copy of <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑧">[[descriptor]]</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②③">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-padding-output-sizes" id="ref-for-mlgraphbuilder-calculate-padding-output-sizes">calculating padding output sizes</a> given <var>input</var>, <var>beginningPadding</var> and <var>endingPadding</var>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑤">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-padding-output-sizes" id="ref-for-mlgraphbuilder-calculate-padding-output-sizes">calculating padding output sizes</a> given <var>input</var>, <var>beginningPadding</var> and <var>endingPadding</var>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②①">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑥">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②③">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑧">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⑥">operator</a> for the padding operation, given <var>beginningPadding</var>, <var>endingPadding</var> and <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑧">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③③">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⑤">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6107,9 +6146,9 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④④"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-averagepool2d" id="ref-for-dom-mlgraphbuilder-averagepool2d"><c- g>averagePool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/averagePool2d(input, options), MLGraphBuilder/averagePool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-averagepool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-averagepool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/averagePool2d(input, options), MLGraphBuilder/averagePool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-averagepool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-averagepool2d-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-l2pool2d" id="ref-for-dom-mlgraphbuilder-l2pool2d"><c- g>l2Pool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/l2Pool2d(input, options), MLGraphBuilder/l2Pool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-l2pool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-l2pool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions①"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/l2Pool2d(input, options), MLGraphBuilder/l2Pool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-l2pool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-l2pool2d-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-maxpool2d" id="ref-for-dom-mlgraphbuilder-maxpool2d"><c- g>maxPool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/maxPool2d(input, options), MLGraphBuilder/maxPool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-maxpool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-maxpool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions②"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/maxPool2d(input, options), MLGraphBuilder/maxPool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-maxpool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-maxpool2d-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-averagepool2d" id="ref-for-dom-mlgraphbuilder-averagepool2d"><c- g>averagePool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/averagePool2d(input, options), MLGraphBuilder/averagePool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-averagepool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-averagepool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/averagePool2d(input, options), MLGraphBuilder/averagePool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-averagepool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-averagepool2d-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-l2pool2d" id="ref-for-dom-mlgraphbuilder-l2pool2d"><c- g>l2Pool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/l2Pool2d(input, options), MLGraphBuilder/l2Pool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-l2pool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-l2pool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions①"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/l2Pool2d(input, options), MLGraphBuilder/l2Pool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-l2pool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-l2pool2d-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-maxpool2d" id="ref-for-dom-mlgraphbuilder-maxpool2d"><c- g>maxPool2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/maxPool2d(input, options), MLGraphBuilder/maxPool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-maxpool2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-maxpool2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions②"><c- n>MLPool2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/maxPool2d(input, options), MLGraphBuilder/maxPool2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-maxpool2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-maxpool2d-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions③">MLPool2dOptions</a></code> has the following members:</p>
@@ -6167,12 +6206,12 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑧">MLOperand</a></code>. The input 4-D tensor. The logical shape
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑦">MLOperand</a></code>. The input 4-D tensor. The logical shape
 is interpreted according to the value of <em>options.layout</em>.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions④">MLPool2dOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑨">MLOperand</a></code>. The output 4-D tensor that contains the
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑧">MLOperand</a></code>. The output 4-D tensor that contains the
     result of the reduction. The logical shape is interpreted according to the
     value of <em>layout</em>. More specifically, if the <em>options.roundingType</em> is <code class="idl"><a data-link-type="idl" href="#dom-mlroundingtype-floor" id="ref-for-dom-mlroundingtype-floor">"floor"</a></code>, the spatial dimensions of the output tensor can be calculated as follow:</p>
     <p><em>output size = floor(1 + (input size - filter size + beginning padding + ending padding) / stride)</em></p>
@@ -6186,7 +6225,7 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
 </pre>
    </div>
    <details class="algorithm" data-algorithm="calculate pool2d output sizes" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-pool2d-output-sizes">calculate pool2d output sizes</dfn> given <code class="idl"><a data-link-type="idl" href="#enumdef-mlinputoperandlayout" id="ref-for-enumdef-mlinputoperandlayout⑧">MLInputOperandLayout</a></code> <var>layout</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑧">list</a> of 4 unsigned integers <var>inputShape</var>, <code class="idl"><a data-link-type="idl" href="#enumdef-mlroundingtype" id="ref-for-enumdef-mlroundingtype②">MLRoundingType</a></code> <var>roundingType</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list②⑨">list</a> of 2 unsigned integers <var>windowDimensions</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⓪">list</a> of 4 unsigned integers <var>padding</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③①">list</a> of 2 unsigned integers <var>strides</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③②">list</a> of 2 unsigned integers <var>dilations</var>, and optional <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③③">list</a> of 2 unsigned integers <var>outputSizes</var>, perform these steps. They return a <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③④">list</a> of 4 unsigned integers. </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-pool2d-output-sizes">calculate pool2d output sizes</dfn> given <code class="idl"><a data-link-type="idl" href="#enumdef-mlinputoperandlayout" id="ref-for-enumdef-mlinputoperandlayout⑧">MLInputOperandLayout</a></code> <var>layout</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③②">list</a> of 4 unsigned integers <var>inputShape</var>, <code class="idl"><a data-link-type="idl" href="#enumdef-mlroundingtype" id="ref-for-enumdef-mlroundingtype②">MLRoundingType</a></code> <var>roundingType</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③③">list</a> of 2 unsigned integers <var>windowDimensions</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③④">list</a> of 4 unsigned integers <var>padding</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑤">list</a> of 2 unsigned integers <var>strides</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑥">list</a> of 2 unsigned integers <var>dilations</var>, and optional <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑦">list</a> of 2 unsigned integers <var>outputSizes</var>, perform these steps. They return a <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑧">list</a> of 4 unsigned integers. </summary>
     <ol>
      <li data-md>
       <p>Switch on <var>layout</var>:</p>
@@ -6267,7 +6306,7 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
     </ol>
    </details>
    <details class="algorithm" data-algorithm="pooling-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="pooling-op" data-noexport id="mlgraphbuilder-pooling-op">create pooling operation</dfn> given <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string⑧">string</a> <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①⓪">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions⑤">MLPool2dOptions</a></code> <var>options</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="pooling-op" data-noexport id="mlgraphbuilder-pooling-op">create pooling operation</dfn> given <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string⑧">string</a> <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑨">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlpool2doptions" id="ref-for-dictdef-mlpool2doptions⑤">MLPool2dOptions</a></code> <var>options</var>, run the following steps: </summary>
     <ol>
      <li data-md>
       <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#assert" id="ref-for-assert④">Assert</a>: <var>op</var> is one of "averagePool2d", "l2Pool2d", "maxPool2d".</p>
@@ -6278,11 +6317,11 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
      <li data-md>
       <p>Otherwise, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-windowdimensions" id="ref-for-dom-mlpool2doptions-windowdimensions②">windowDimensions</a></code> to the height and width dimensions of the shape of <var>input</var>.</p>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes①">outputSizes</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑤⑨">exists</a>, or if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding①">padding</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⓪">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding②">padding</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑤">list</a> « 0, 0, 0, 0 ».</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes①">outputSizes</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑤⑨">exists</a>, or if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding①">padding</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⓪">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding②">padding</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑨">list</a> « 0, 0, 0, 0 ».</p>
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding③">padding</a></code>'s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③⑥">size</a> is not 4, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑧⑤">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①②⑤">TypeError</a></code>.</p>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides①">strides</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥①">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides②">strides</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑥">list</a> « 1, 1 ».</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides①">strides</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥①">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides②">strides</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⓪">list</a> « 1, 1 ».</p>
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides③">strides</a></code>'s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③⑦">size</a> is not 2, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑧⑥">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①②⑥">TypeError</a></code>.</p>
      <li data-md>
@@ -6296,7 +6335,7 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
         <p>If the elements of <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes④">outputSizes</a></code> are not smaller than the elements at the same dimension (index) for <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides⑤">strides</a></code>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑧⑨">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①②⑨">TypeError</a></code>.</p>
       </ol>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations①">dilations</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥③">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations②">dilations</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑦">list</a> « 1, 1 ».</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations①">dilations</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥③">exist</a>, set <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations②">dilations</a></code> to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④①">list</a> « 1, 1 ».</p>
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations③">dilations</a></code>'s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③⑨">size</a> is not 2, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑨⓪">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①③⓪">TypeError</a></code>.</p>
      <li data-md>
@@ -6307,13 +6346,13 @@ <h4 class="heading settled" data-level="7.8.31" id="api-mlgraphbuilder-pool2d"><
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②④">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-pool2d-output-sizes" id="ref-for-mlgraphbuilder-calculate-pool2d-output-sizes">calculating pool2d output sizes</a> given <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-layout" id="ref-for-dom-mlpool2doptions-layout①">layout</a></code>, <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥②">shape</a>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-roundingtype" id="ref-for-dom-mlpool2doptions-roundingtype②">roundingType</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-windowdimensions" id="ref-for-dom-mlpool2doptions-windowdimensions③">windowDimensions</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding④">padding</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides⑥">strides</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations⑤">dilations</a></code>, and <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes⑤">outputSizes</a></code> (if it <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥④">exists</a>).</p>
+        <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑥">dimensions</a></code> to the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-pool2d-output-sizes" id="ref-for-mlgraphbuilder-calculate-pool2d-output-sizes">calculating pool2d output sizes</a> given <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-layout" id="ref-for-dom-mlpool2doptions-layout①">layout</a></code>, <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥②">shape</a>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-roundingtype" id="ref-for-dom-mlpool2doptions-roundingtype②">roundingType</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-windowdimensions" id="ref-for-dom-mlpool2doptions-windowdimensions③">windowDimensions</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-padding" id="ref-for-dom-mlpool2doptions-padding④">padding</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-strides" id="ref-for-dom-mlpool2doptions-strides⑥">strides</a></code>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-dilations" id="ref-for-dom-mlpool2doptions-dilations⑤">dilations</a></code>, and <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes⑤">outputSizes</a></code> (if it <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥④">exists</a>).</p>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②②">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑦">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②④">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑨">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⑦">operator</a> for the <var>op</var> pooling operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②⑨">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③④">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⑥">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6374,32 +6413,32 @@ <h5 class="heading settled" data-level="7.8.31.3" id="api-mlgraphbuilder-pool2d-
    <h4 class="heading settled" data-level="7.8.32" id="api-mlgraphbuilder-prelu"><span class="secno">7.8.32. </span><span class="content">prelu</span><a class="self-link" href="#api-mlgraphbuilder-prelu"></a></h4>
     Calculate the <a href="https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Parametric_ReLU">parametric version of rectified linear function (Parametric ReLU)</a> on the input tensor element-wise. Parametric ReLU is a type of leaky ReLU that, instead of having a scalar slope like 0.01, making the slope (coefficient of leakage) into a parameter that is learned during the model training phase of this operation. The calculation follows the expression <code>max(0, x) + slope ∗ min(0, x)</code>. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⑤"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-prelu" id="ref-for-dom-mlgraphbuilder-prelu"><c- g>prelu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/prelu(input, slope)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-prelu-input-slope-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-prelu-input-slope-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/prelu(input, slope)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-prelu-input-slope-slope"><code><c- g>slope</c-></code><a class="self-link" href="#dom-mlgraphbuilder-prelu-input-slope-slope"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-prelu" id="ref-for-dom-mlgraphbuilder-prelu"><c- g>prelu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/prelu(input, slope)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-prelu-input-slope-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-prelu-input-slope-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/prelu(input, slope)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-prelu-input-slope-slope"><code><c- g>slope</c-></code><a class="self-link" href="#dom-mlgraphbuilder-prelu-input-slope-slope"></a></dfn>);
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①④">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①③">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
-      <p><em>slope</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①⑤">MLOperand</a></code>. The slope tensor. Its shape is either the same as, or <a data-link-type="dfn" href="#unidirectionally-broadcastable" id="ref-for-unidirectionally-broadcastable③">unidirectionally broadcastable</a> to the shape of input tensor <em>input</em>.</p>
+      <p><em>slope</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①④">MLOperand</a></code>. The slope tensor. Its shape is either the same as, or <a data-link-type="dfn" href="#unidirectionally-broadcastable" id="ref-for-unidirectionally-broadcastable③">unidirectionally broadcastable</a> to the shape of input tensor <em>input</em>.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①⑥">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①⑤">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="prelu(input, slope)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="dom-mlgraphbuilder-prelu"><code>prelu(<var>input</var>, <var>slope</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>descriptor</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③②">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>descriptor</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③④">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②⓪">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype②⑨">dataType</a>.</p>
+      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②②">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③①">dataType</a>.</p>
      <li data-md>
-      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑤">dimensions</a></code> to the result of <a data-link-type="dfn" href="#unidirectionally-broadcasting-the-shapes" id="ref-for-unidirectionally-broadcasting-the-shapes①">unidirectionally broadcasting the shapes</a> <var>slope</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥③">shape</a> and <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥④">shape</a>.</p>
+      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑦">dimensions</a></code> to the result of <a data-link-type="dfn" href="#unidirectionally-broadcasting-the-shapes" id="ref-for-unidirectionally-broadcasting-the-shapes①">unidirectionally broadcasting the shapes</a> <var>slope</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥③">shape</a> and <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥④">shape</a>.</p>
       <ol>
        <li data-md>
         <p>If that returns failure, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑨⑧">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①③②">TypeError</a></code>.</p>
@@ -6408,11 +6447,11 @@ <h4 class="heading settled" data-level="7.8.32" id="api-mlgraphbuilder-prelu"><s
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②③">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑧">this</a> and <var>descriptor</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑤">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⓪">this</a> and <var>descriptor</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⑧">operator</a> for the PReLU operation, given <var>slope</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⓪">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑤">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⑦">inputs</a> to <var>input</var> and <var>slope</var>.</p>
        <li data-md>
@@ -6441,16 +6480,16 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⑥"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducel1" id="ref-for-dom-mlgraphbuilder-reducel1"><c- g>reduceL1</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL1(input, options), MLGraphBuilder/reduceL1(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel1-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel1-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL1(input, options), MLGraphBuilder/reduceL1(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel1-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel1-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducel2" id="ref-for-dom-mlgraphbuilder-reducel2"><c- g>reduceL2</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL2(input, options), MLGraphBuilder/reduceL2(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel2-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel2-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL2(input, options), MLGraphBuilder/reduceL2(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel2-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel2-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducelogsum" id="ref-for-dom-mlgraphbuilder-reducelogsum"><c- g>reduceLogSum</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSum(input, options), MLGraphBuilder/reduceLogSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsum-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsum-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions②"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSum(input, options), MLGraphBuilder/reduceLogSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsum-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsum-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducelogsumexp" id="ref-for-dom-mlgraphbuilder-reducelogsumexp"><c- g>reduceLogSumExp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSumExp(input, options), MLGraphBuilder/reduceLogSumExp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsumexp-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsumexp-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions③"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSumExp(input, options), MLGraphBuilder/reduceLogSumExp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsumexp-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsumexp-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemax" id="ref-for-dom-mlgraphbuilder-reducemax"><c- g>reduceMax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMax(input, options), MLGraphBuilder/reduceMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemax-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemax-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions④"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMax(input, options), MLGraphBuilder/reduceMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemax-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemax-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemean" id="ref-for-dom-mlgraphbuilder-reducemean"><c- g>reduceMean</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMean(input, options), MLGraphBuilder/reduceMean(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemean-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemean-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑤"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMean(input, options), MLGraphBuilder/reduceMean(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemean-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemean-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemin" id="ref-for-dom-mlgraphbuilder-reducemin"><c- g>reduceMin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMin(input, options), MLGraphBuilder/reduceMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemin-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemin-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑥"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMin(input, options), MLGraphBuilder/reduceMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemin-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemin-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reduceproduct" id="ref-for-dom-mlgraphbuilder-reduceproduct"><c- g>reduceProduct</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceProduct(input, options), MLGraphBuilder/reduceProduct(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reduceproduct-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reduceproduct-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑦"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceProduct(input, options), MLGraphBuilder/reduceProduct(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reduceproduct-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reduceproduct-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducesum" id="ref-for-dom-mlgraphbuilder-reducesum"><c- g>reduceSum</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSum(input, options), MLGraphBuilder/reduceSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesum-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesum-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑧"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSum(input, options), MLGraphBuilder/reduceSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesum-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesum-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducesumsquare" id="ref-for-dom-mlgraphbuilder-reducesumsquare"><c- g>reduceSumSquare</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSumSquare(input, options), MLGraphBuilder/reduceSumSquare(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesumsquare-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesumsquare-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑨"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSumSquare(input, options), MLGraphBuilder/reduceSumSquare(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesumsquare-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesumsquare-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducel1" id="ref-for-dom-mlgraphbuilder-reducel1"><c- g>reduceL1</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL1(input, options), MLGraphBuilder/reduceL1(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel1-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel1-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL1(input, options), MLGraphBuilder/reduceL1(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel1-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel1-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducel2" id="ref-for-dom-mlgraphbuilder-reducel2"><c- g>reduceL2</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③①⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL2(input, options), MLGraphBuilder/reduceL2(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel2-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel2-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceL2(input, options), MLGraphBuilder/reduceL2(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducel2-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducel2-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducelogsum" id="ref-for-dom-mlgraphbuilder-reducelogsum"><c- g>reduceLogSum</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSum(input, options), MLGraphBuilder/reduceLogSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsum-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsum-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions②"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSum(input, options), MLGraphBuilder/reduceLogSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsum-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsum-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducelogsumexp" id="ref-for-dom-mlgraphbuilder-reducelogsumexp"><c- g>reduceLogSumExp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSumExp(input, options), MLGraphBuilder/reduceLogSumExp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsumexp-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsumexp-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions③"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceLogSumExp(input, options), MLGraphBuilder/reduceLogSumExp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducelogsumexp-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducelogsumexp-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemax" id="ref-for-dom-mlgraphbuilder-reducemax"><c- g>reduceMax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMax(input, options), MLGraphBuilder/reduceMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemax-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemax-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions④"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMax(input, options), MLGraphBuilder/reduceMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemax-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemax-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemean" id="ref-for-dom-mlgraphbuilder-reducemean"><c- g>reduceMean</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMean(input, options), MLGraphBuilder/reduceMean(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemean-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemean-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑤"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMean(input, options), MLGraphBuilder/reduceMean(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemean-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemean-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducemin" id="ref-for-dom-mlgraphbuilder-reducemin"><c- g>reduceMin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMin(input, options), MLGraphBuilder/reduceMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemin-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemin-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑥"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceMin(input, options), MLGraphBuilder/reduceMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducemin-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducemin-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reduceproduct" id="ref-for-dom-mlgraphbuilder-reduceproduct"><c- g>reduceProduct</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceProduct(input, options), MLGraphBuilder/reduceProduct(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reduceproduct-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reduceproduct-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑦"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceProduct(input, options), MLGraphBuilder/reduceProduct(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reduceproduct-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reduceproduct-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducesum" id="ref-for-dom-mlgraphbuilder-reducesum"><c- g>reduceSum</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSum(input, options), MLGraphBuilder/reduceSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesum-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesum-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑧"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSum(input, options), MLGraphBuilder/reduceSum(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesum-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesum-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reducesumsquare" id="ref-for-dom-mlgraphbuilder-reducesumsquare"><c- g>reduceSumSquare</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSumSquare(input, options), MLGraphBuilder/reduceSumSquare(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesumsquare-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesumsquare-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions⑨"><c- n>MLReduceOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reduceSumSquare(input, options), MLGraphBuilder/reduceSumSquare(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reducesumsquare-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reducesumsquare-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①⓪">MLReduceOptions</a></code> has the following members:</p>
@@ -6466,11 +6505,11 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑦">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑥">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①①">MLReduceOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑧">MLOperand</a></code>. The reduced output tensor.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑦">MLOperand</a></code>. The reduced output tensor.</p>
    </div>
    <div class="note" role="note">
      <strong>Reduction types:</strong> 
@@ -6498,7 +6537,7 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
     </ul>
    </div>
    <details class="algorithm" data-algorithm="calculate reduction output sizes" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-reduction-output-sizes">calculate reduction output sizes</dfn>, given a <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑧">list</a> of unsigned integers <var>inputShape</var>, a optional <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list③⑨">list</a> of unsigned integers <var>axes</var>, and <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#boolean" id="ref-for-boolean">boolean</a> <var>keepDimensions</var>, perform the following steps. They return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⓪">list</a> of unsigned integers. </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-reduction-output-sizes">calculate reduction output sizes</dfn>, given a <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④②">list</a> of unsigned integers <var>inputShape</var>, a optional <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④③">list</a> of unsigned integers <var>axes</var>, and <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#boolean" id="ref-for-boolean">boolean</a> <var>keepDimensions</var>, perform the following steps. They return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④④">list</a> of unsigned integers. </summary>
     <ol>
      <li data-md>
       <p>Let <var>inputSize</var> be <var>inputShape</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④①">size</a>.</p>
@@ -6520,7 +6559,7 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
       <p>Otherwise:</p>
       <ol>
        <li data-md>
-        <p>Let <var>outputShape</var> be an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④①">list</a>.</p>
+        <p>Let <var>outputShape</var> be an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑤">list</a>.</p>
        <li data-md>
         <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-iterate" id="ref-for-list-iterate⑥">For each</a> <var>index</var> in <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range⑨">the range</a> 0 to <var>inputSize</var>, exclusive:</p>
         <ol>
@@ -6533,7 +6572,7 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
     </ol>
    </details>
    <details class="algorithm" data-algorithm="reduce-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="reduce-op" data-noexport id="mlgraphbuilder-reduce-op">create reduce operation</dfn> given <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string⑨">string</a> <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑨">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①②">MLReduceOptions</a></code> <var>options</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="reduce-op" data-noexport id="mlgraphbuilder-reduce-op">create reduce operation</dfn> given <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string⑨">string</a> <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑧">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlreduceoptions" id="ref-for-dictdef-mlreduceoptions①②">MLReduceOptions</a></code> <var>options</var>, run the following steps: </summary>
     <ol>
      <li data-md>
       <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#assert" id="ref-for-assert⑤">Assert</a>: <var>op</var> is one of "reduceL1", "reduceL2", "reduceLogSum", "reduceLogSumExp", "reduceMax", "reduceMean", "reduceMin", "reduceProduct", "reduceSum", "reduceSumSquare".</p>
@@ -6542,20 +6581,20 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
      <li data-md>
       <p>Let <var>outputShape</var> be the result of <a data-link-type="dfn" href="#mlgraphbuilder-calculate-reduction-output-sizes" id="ref-for-mlgraphbuilder-calculate-reduction-output-sizes①">calculating reduction output sizes</a> given <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑥⑤">shape</a>, <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlreduceoptions-axes" id="ref-for-dom-mlreduceoptions-axes③">axes</a></code> (if it <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑥">exists</a>), and <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlreduceoptions-keepdimensions" id="ref-for-dom-mlreduceoptions-keepdimensions②">keepDimensions</a></code>.</p>
      <li data-md>
-      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③③">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⑤">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②①">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③⓪">dataType</a>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②③">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③②">dataType</a>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑥">dimensions</a></code> to <var>outputShape</var>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑧">dimensions</a></code> to <var>outputShape</var>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②④">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑨">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑥">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤①">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators③⑨">operator</a> for the <var>op</var> reduce operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③①">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑥">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⑧">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6701,7 +6740,7 @@ <h4 class="heading settled" data-level="7.8.33" id="api-mlgraphbuilder-reduce"><
    <h4 class="heading settled" data-level="7.8.34" id="api-mlgraphbuilder-relu-method"><span class="secno">7.8.34. </span><span class="content">relu</span><a class="self-link" href="#api-mlgraphbuilder-relu-method"></a></h4>
     Compute the <a href="https://en.wikipedia.org/wiki/Rectifier_(neural_networks)">rectified linear function</a> of the input tensor. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-relu" id="ref-for-dom-mlgraphbuilder-relu"><c- g>relu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/relu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-relu-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-relu-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-relu" id="ref-for-dom-mlgraphbuilder-relu"><c- g>relu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/relu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-relu-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-relu-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③⑧"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#relu-noargs" id="ref-for-relu-noargs②"><c- g>relu</c-></a>();
 };
 </pre>
@@ -6720,12 +6759,12 @@ <h5 class="heading settled" data-level="7.8.34.1" id="api-mlgraphbuilder-relu-in
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④②">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④①">MLOperand</a></code>. The input tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④③">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④②">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="relu(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -6739,7 +6778,7 @@ <h5 class="heading settled" data-level="7.8.34.1" id="api-mlgraphbuilder-relu-in
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⓪">operator</a> for the ReLU operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③②">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑦">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input④⑨">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6766,7 +6805,7 @@ <h5 class="heading settled" data-level="7.8.34.2" id="api-mlgraphbuilder-relu"><
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="relu-noargs"><code>relu()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑥">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⓪">this</a> and "relu".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑥">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤②">this</a> and "relu".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②②⓪">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②②①">throw</a> the error.</p>
@@ -6790,18 +6829,18 @@ <h4 class="heading settled" data-level="7.8.35" id="api-mlgraphbuilder-resample2
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-resample2d" id="ref-for-dom-mlgraphbuilder-resample2d"><c- g>resample2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/resample2d(input, options), MLGraphBuilder/resample2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-resample2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-resample2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions"><c- n>MLResample2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/resample2d(input, options), MLGraphBuilder/resample2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-resample2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-resample2d-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-resample2d" id="ref-for-dom-mlgraphbuilder-resample2d"><c- g>resample2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/resample2d(input, options), MLGraphBuilder/resample2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-resample2d-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-resample2d-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions"><c- n>MLResample2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/resample2d(input, options), MLGraphBuilder/resample2d(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-resample2d-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-resample2d-input-options-options"></a></dfn> = {});
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑥">MLOperand</a></code>. The input 4-D tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑤">MLOperand</a></code>. The input 4-D tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions①">MLResample2dOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑦">MLOperand</a></code>. The output 4-D tensor.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑥">MLOperand</a></code>. The output 4-D tensor.</p>
    </div>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions②">MLResample2dOptions</a></code> has the following members:</p>
    <dl>
@@ -6827,13 +6866,13 @@ <h4 class="heading settled" data-level="7.8.35" id="api-mlgraphbuilder-resample2
     <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-check-resample-options">check resample options</dfn> given <var>options</var>, run the following steps: </summary>
     <ol>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-scales" id="ref-for-dom-mlresample2doptions-scales②">scales</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑦">exist</a>, set it to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④②">list</a> « 1.0, 1.0 ».</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-scales" id="ref-for-dom-mlresample2doptions-scales②">scales</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑦">exist</a>, set it to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑥">list</a> « 1.0, 1.0 ».</p>
      <li data-md>
       <p>Otherwise, if any of its values is not greater than 0, or if its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④②">size</a> is not 2, return false.</p>
      <li data-md>
       <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-sizes" id="ref-for-dom-mlresample2doptions-sizes①">sizes</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑧">exists</a>, and if its size is not 2, or if any of its values is not greater than 0, return false.</p>
      <li data-md>
-      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-axes" id="ref-for-dom-mlresample2doptions-axes①">axes</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑨">exists</a>, set it to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④③">list</a> « 2, 3 ».</p>
+      <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-axes" id="ref-for-dom-mlresample2doptions-axes①">axes</a></code> does not <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑥⑨">exists</a>, set it to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑦">list</a> « 2, 3 ».</p>
      <li data-md>
       <p>Otherwise, if its value is not one of « 0, 1», « 1, 2», « 2, 3 », return false.</p>
      <li data-md>
@@ -6841,10 +6880,10 @@ <h4 class="heading settled" data-level="7.8.35" id="api-mlgraphbuilder-resample2
     </ol>
    </details>
    <details class="algorithm" data-algorithm="calculate resample output sizes" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-resample-output-sizes">calculate resample output sizes</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑧">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions③">MLResample2dOptions</a></code> <var>options</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-noexport id="mlgraphbuilder-calculate-resample-output-sizes">calculate resample output sizes</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④⑦">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlresample2doptions" id="ref-for-dictdef-mlresample2doptions③">MLResample2dOptions</a></code> <var>options</var>, run the following steps: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③④">MLOperandDescriptor</a></code> initialized to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot①⓪">[[descriptor]]</a></code>.</p>
+      <p>Let <var>desc</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⑥">MLOperandDescriptor</a></code> initialized to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot①⓪">[[descriptor]]</a></code>.</p>
      <li data-md>
       <p>For <var>index</var> in <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range①①">the range</a> 0 to <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlresample2doptions-axes" id="ref-for-dom-mlresample2doptions-axes②">axes</a></code>'s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④③">size</a>, exclusive:</p>
       <ol>
@@ -6870,11 +6909,11 @@ <h4 class="heading settled" data-level="7.8.35" id="api-mlgraphbuilder-resample2
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑤">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤①">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑦">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤③">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④①">operator</a> for the resample 2D operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③③">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑧">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤⓪">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6887,20 +6926,20 @@ <h4 class="heading settled" data-level="7.8.35" id="api-mlgraphbuilder-resample2
    <h4 class="heading settled" data-level="7.8.36" id="api-mlgraphbuilder-reshape-method"><span class="secno">7.8.36. </span><span class="content">reshape</span><a class="self-link" href="#api-mlgraphbuilder-reshape-method"></a></h4>
     Alter the shape of a tensor to a new shape. Reshape does not copy or change the content of the tensor. It just changes the tensor’s logical dimensions for the subsequent operations. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder④⑨"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reshape" id="ref-for-dom-mlgraphbuilder-reshape"><c- g>reshape</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reshape(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reshape-input-newshape-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reshape-input-newshape-input"></a></dfn>, <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③②"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③③"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⑨"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reshape(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reshape-input-newshape-newshape"><code><c- g>newShape</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reshape-input-newshape-newshape"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reshape" id="ref-for-dom-mlgraphbuilder-reshape"><c- g>reshape</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reshape(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reshape-input-newshape-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reshape-input-newshape-input"></a></dfn>, <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③②"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③③"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⑨"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reshape(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reshape-input-newshape-newshape"><code><c- g>newShape</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reshape-input-newshape-newshape"></a></dfn>);
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤①">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⓪">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>newShape</em>: a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⓪">unsigned long</a></code>. The shape of the output tensor.
 The number of elements implied by <em>newShape</em> must be the same as the
 number of elements in the input tensor.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤②">MLOperand</a></code>. The output tensor. The values of the output
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤①">MLOperand</a></code>. The output tensor. The values of the output
     tensor are the same as values of the input tensor. The shape of the output
     tensor is specified by the <em>newShape</em> argument.</p>
    </div>
@@ -6910,7 +6949,7 @@ <h4 class="heading settled" data-level="7.8.36" id="api-mlgraphbuilder-reshape-m
      <li data-md>
       <p>Let <var>outputShape</var> be an empty array of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤①">unsigned long</a></code>.</p>
      <li data-md>
-      <p>If <var>newShape</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④④">size</a> is 0, set <var>outputShape</var> to an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④④">list</a> for a scalar.</p>
+      <p>If <var>newShape</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④④">size</a> is 0, set <var>outputShape</var> to an empty <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑧">list</a> for a scalar.</p>
      <li data-md>
       <p>If any value in <var>newShape</var> is 0, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②②④">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①③⑥">TypeError</a></code>.</p>
      <li data-md>
@@ -6920,16 +6959,16 @@ <h4 class="heading settled" data-level="7.8.36" id="api-mlgraphbuilder-reshape-m
      <li data-md>
       <p>Let <var>desc</var> be a copy of <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot①①">[[descriptor]]</a></code>.</p>
      <li data-md>
-      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑦">dimensions</a></code> to <var>newShape</var>.</p>
+      <p>Set <var>desc</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑨">dimensions</a></code> to <var>newShape</var>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑥">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤②">this</a> and <var>desc</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑧">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤④">this</a> and <var>desc</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④②">operator</a> for the reshape operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③④">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑨">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤①">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -6975,7 +7014,7 @@ <h4 class="heading settled" data-level="7.8.36" id="api-mlgraphbuilder-reshape-m
    <h4 class="heading settled" data-level="7.8.37" id="api-mlgraphbuilder-sigmoid-method"><span class="secno">7.8.37. </span><span class="content">sigmoid</span><a class="self-link" href="#api-mlgraphbuilder-sigmoid-method"></a></h4>
     Compute the <a href="https://en.wikipedia.org/wiki/Sigmoid_function">sigmoid function</a> of the input tensor. The calculation follows the expression <code>1 / (exp(-x) + 1)</code>. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sigmoid" id="ref-for-dom-mlgraphbuilder-sigmoid"><c- g>sigmoid</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sigmoid-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sigmoid-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sigmoid" id="ref-for-dom-mlgraphbuilder-sigmoid"><c- g>sigmoid</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sigmoid(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sigmoid-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sigmoid-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④⓪"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#sigmoid-noargs" id="ref-for-sigmoid-noargs③"><c- g>sigmoid</c-></a>();
 };
 </pre>
@@ -6998,12 +7037,12 @@ <h5 class="heading settled" data-level="7.8.37.1" id="api-mlgraphbuilder-sigmoid
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑤">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤④">MLOperand</a></code>. The input tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑥">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑤">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="sigmoid(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -7017,7 +7056,7 @@ <h5 class="heading settled" data-level="7.8.37.1" id="api-mlgraphbuilder-sigmoid
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④③">operator</a> for the sigmoid operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑤">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⓪">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤②">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7044,7 +7083,7 @@ <h5 class="heading settled" data-level="7.8.37.2" id="api-mlgraphbuilder-sigmoid
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="sigmoid-noargs"><code>sigmoid()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑦">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤③">this</a> and "sigmoid".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑦">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑤">this</a> and "sigmoid".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②②⑥">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②②⑦">throw</a> the error.</p>
@@ -7056,7 +7095,7 @@ <h5 class="heading settled" data-level="7.8.37.2" id="api-mlgraphbuilder-sigmoid
    <h4 class="heading settled" data-level="7.8.38" id="api-mlgraphbuilder-slice"><span class="secno">7.8.38. </span><span class="content">slice</span><a class="self-link" href="#api-mlgraphbuilder-slice"></a></h4>
     Produce a slice of the input tensor. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤①"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-slice" id="ref-for-dom-mlgraphbuilder-slice"><c- g>slice</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/slice(input, starts, sizes)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-slice-input-starts-sizes-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-slice-input-starts-sizes-input"></a></dfn>,
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-slice" id="ref-for-dom-mlgraphbuilder-slice"><c- g>slice</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/slice(input, starts, sizes)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-slice-input-starts-sizes-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-slice-input-starts-sizes-input"></a></dfn>,
                   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③③"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③④"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤②"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/slice(input, starts, sizes)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-slice-input-starts-sizes-starts"><code><c- g>starts</c-></code><a class="self-link" href="#dom-mlgraphbuilder-slice-input-starts-sizes-starts"></a></dfn>,
                   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③④"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③⑤"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤③"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/slice(input, starts, sizes)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-slice-input-starts-sizes-sizes"><code><c- g>sizes</c-></code><a class="self-link" href="#dom-mlgraphbuilder-slice-input-starts-sizes-sizes"></a></dfn>);
 };
@@ -7065,13 +7104,13 @@ <h4 class="heading settled" data-level="7.8.38" id="api-mlgraphbuilder-slice"><s
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑨">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑧">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>starts</em>: a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤④">unsigned long</a></code>. The sequence of unsigned integer values indicating the starting index to slice of each input dimension, of length N where N is the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑤⑧">rank</a> of the input tensor. For each dimension <em>d</em> of <em>input</em>, <em>starts[d]</em> indicates the starting index to slice in that dimension. The starting index must be in the range [0, input size - 1] in that dimension.</p>
      <li data-md>
       <p><em>sizes</em>: a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⑤">unsigned long</a></code>. The sequence of unsigned integer values indicating the number of elements to slice of each input dimension, of length N where N is the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑤⑨">rank</a> of the input tensor. For each dimension <em>d</em> of <em>input</em>, <em>sizes[d]</em> indicates the number of elements to slice in that dimension. The size must not be 0 and must satisfy the constraint <em>starting index + size &lt;= input size</em> in that dimension.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥⓪">MLOperand</a></code>. The output tensor of the same rank as the input tensor with tensor values stripped to the specified starting and ending indices in each dimension.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤⑨">MLOperand</a></code>. The output tensor of the same rank as the input tensor with tensor values stripped to the specified starting and ending indices in each dimension.</p>
    </div>
    <details class="algorithm" data-algorithm="slice(input, starts, sizes)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="dom-mlgraphbuilder-slice"><code>slice(<var>input</var>, <var>starts</var>, <var>sizes</var>)</code></dfn> method steps are: </summary>
@@ -7088,7 +7127,7 @@ <h4 class="heading settled" data-level="7.8.38" id="api-mlgraphbuilder-slice"><s
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④④">operator</a> for the slice operation, given <var>starts</var> and <var>sizes</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑥">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④①">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤③">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7102,7 +7141,7 @@ <h4 class="heading settled" data-level="7.8.39" id="api-mlgraphbuilder-softmax-m
     Compute the <a href="https://en.wikipedia.org/wiki/Softmax_function">softmax</a> values of
 the 2-D input tensor along axis 1. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤②"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softmax" id="ref-for-dom-mlgraphbuilder-softmax"><c- g>softmax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softmax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softmax-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softmax-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softmax" id="ref-for-dom-mlgraphbuilder-softmax"><c- g>softmax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softmax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softmax-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softmax-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④②"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#softmax-noargs" id="ref-for-softmax-noargs①"><c- g>softmax</c-></a>();
 };
 </pre>
@@ -7128,12 +7167,12 @@ <h5 class="heading settled" data-level="7.8.39.1" id="api-mlgraphbuilder-softmax
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥③">MLOperand</a></code>. The input 2-D tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥②">MLOperand</a></code>. The input 2-D tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥④">MLOperand</a></code>. The output 2-D tensor that contains the softmax results, of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥③">MLOperand</a></code>. The output 2-D tensor that contains the softmax results, of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softmax(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -7149,7 +7188,7 @@ <h5 class="heading settled" data-level="7.8.39.1" id="api-mlgraphbuilder-softmax
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⑤">operator</a> for the softmax operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑦">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④②">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤④">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7176,7 +7215,7 @@ <h5 class="heading settled" data-level="7.8.39.2" id="api-mlgraphbuilder-softmax
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="softmax-noargs"><code>softmax()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑧">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤④">this</a> and  "softmax".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑧">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑥">this</a> and  "softmax".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③①">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③②">throw</a> the error.</p>
@@ -7192,7 +7231,7 @@ <h4 class="heading settled" data-level="7.8.40" id="api-mlgraphbuilder-softplus-
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤③"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softplus" id="ref-for-dom-mlgraphbuilder-softplus"><c- g>softplus</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softplus(input, options), MLGraphBuilder/softplus(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softplus-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softplus-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlsoftplusoptions" id="ref-for-dictdef-mlsoftplusoptions"><c- n>MLSoftplusOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softplus(input, options), MLGraphBuilder/softplus(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softplus-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softplus-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softplus" id="ref-for-dom-mlgraphbuilder-softplus"><c- g>softplus</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softplus(input, options), MLGraphBuilder/softplus(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softplus-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softplus-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlsoftplusoptions" id="ref-for-dictdef-mlsoftplusoptions"><c- n>MLSoftplusOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softplus(input, options), MLGraphBuilder/softplus(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softplus-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softplus-input-options-options"></a></dfn> = {});
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④④"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softplus-options" id="ref-for-dom-mlgraphbuilder-softplus-options"><c- g>softplus</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlsoftplusoptions" id="ref-for-dictdef-mlsoftplusoptions①"><c- n>MLSoftplusOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softplus(options), MLGraphBuilder/softplus()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softplus-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softplus-options-options"></a></dfn> = {});
 };
 </pre>
@@ -7222,14 +7261,14 @@ <h5 class="heading settled" data-level="7.8.40.1" id="api-mlgraphbuilder-softplu
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥⑦">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥⑥">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlsoftplusoptions" id="ref-for-dictdef-mlsoftplusoptions③">MLSoftplusOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥⑧">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥⑦">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softplus(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -7243,7 +7282,7 @@ <h5 class="heading settled" data-level="7.8.40.1" id="api-mlgraphbuilder-softplu
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⑥">operator</a> for the softplus operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑧">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④③">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤⑤">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7270,7 +7309,7 @@ <h5 class="heading settled" data-level="7.8.40.2" id="api-mlgraphbuilder-softplu
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="softplus(options)|softplus()" id="dom-mlgraphbuilder-softplus-options"><code>softplus(<var>options</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑨">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑤">this</a>, "softplus" and <var>options</var>.</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation⑨">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑦">this</a>, "softplus" and <var>options</var>.</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③③">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③④">throw</a> the error.</p>
@@ -7282,7 +7321,7 @@ <h5 class="heading settled" data-level="7.8.40.2" id="api-mlgraphbuilder-softplu
    <h4 class="heading settled" data-level="7.8.41" id="api-mlgraphbuilder-softsign-method"><span class="secno">7.8.41. </span><span class="content">softsign</span><a class="self-link" href="#api-mlgraphbuilder-softsign-method"></a></h4>
     Compute the <a href="https://pytorch.org/docs/stable/generated/torch.nn.Softsign.html">softsign function</a> of the input tensor. The calculation follows the expression <code>x / (1 + |x|)</code>. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤④"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softsign" id="ref-for-dom-mlgraphbuilder-softsign"><c- g>softsign</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softsign(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softsign-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softsign-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-softsign" id="ref-for-dom-mlgraphbuilder-softsign"><c- g>softsign</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/softsign(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-softsign-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-softsign-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④⑥"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#softsign-noargs" id="ref-for-softsign-noargs"><c- g>softsign</c-></a>();
 };
 </pre>
@@ -7301,12 +7340,12 @@ <h5 class="heading settled" data-level="7.8.41.1" id="api-mlgraphbuilder-softsig
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦①">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⓪">MLOperand</a></code>. The input tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦②">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦①">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softsign(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -7320,7 +7359,7 @@ <h5 class="heading settled" data-level="7.8.41.1" id="api-mlgraphbuilder-softsig
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⑦">operator</a> for the softsign operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot③⑨">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④④">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤⑥">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7347,7 +7386,7 @@ <h5 class="heading settled" data-level="7.8.41.2" id="api-mlgraphbuilder-softsig
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="softsign-noargs"><code>softsign()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation①⓪">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑥">this</a> and "softsign".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation①⓪">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑧">this</a> and "softsign".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③⑤">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③⑥">throw</a> the error.</p>
@@ -7363,7 +7402,7 @@ <h4 class="heading settled" data-level="7.8.42" id="api-mlgraphbuilder-split"><s
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⑤"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③⑤"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦③"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-split" id="ref-for-dom-mlgraphbuilder-split"><c- g>split</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/split(input, splits, options), MLGraphBuilder/split(input, splits)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-split-input-splits-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-split-input-splits-options-input"></a></dfn>,
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③⑤"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦②"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-split" id="ref-for-dom-mlgraphbuilder-split"><c- g>split</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/split(input, splits, options), MLGraphBuilder/split(input, splits)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-split-input-splits-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-split-input-splits-options-input"></a></dfn>,
                           ([<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③⑦"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⑦"><c- b>unsigned</c-> <c- b>long</c-></a> <c- b>or</c-> <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③⑥"><c- b>sequence</c-></a>&lt;[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#EnforceRange" id="ref-for-EnforceRange③⑧"><c- g>EnforceRange</c-></a>] <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⑧"><c- b>unsigned</c-> <c- b>long</c-></a>>) <dfn class="idl-code" data-dfn-for="MLGraphBuilder/split(input, splits, options), MLGraphBuilder/split(input, splits)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-split-input-splits-options-splits"><code><c- g>splits</c-></code><a class="self-link" href="#dom-mlgraphbuilder-split-input-splits-options-splits"></a></dfn>,
                           <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlsplitoptions" id="ref-for-dictdef-mlsplitoptions"><c- n>MLSplitOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/split(input, splits, options), MLGraphBuilder/split(input, splits)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-split-input-splits-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-split-input-splits-options-options"></a></dfn> = {});
 };
@@ -7372,13 +7411,13 @@ <h4 class="heading settled" data-level="7.8.42" id="api-mlgraphbuilder-split"><s
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑤">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦④">MLOperand</a></code>. The input tensor.</p>
      <li data-md>
       <p><em>splits</em>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⑨">unsigned long</a></code> or a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥⓪">unsigned long</a></code>. If an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥①">unsigned long</a></code>, it specifies the number of output tensors along the axis. The number must evenly divide the dimension size of <em>input</em> along <em>options.axis</em>. If a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥②">unsigned long</a></code>, it specifies the sizes of each output tensor along the <em>options.axis</em>. The sum of sizes must equal to the dimension size of <em>input</em> along <em>options.axis</em>.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlsplitoptions" id="ref-for-dictdef-mlsplitoptions①">MLSplitOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑥">MLOperand</a></code>. The splitted output tensors. If <em>splits</em> is an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥③">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④⑧">size</a> of the output sequence equals to <em>splits</em>. The shape of each output tensor is the same as <em>input</em> except the dimension size of <em>axis</em> equals to the quotient of dividing the dimension size of <em>input</em> along <em>axis</em> by <em>splits</em>. If <em>splits</em> is a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥④">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④⑨">size</a> of the output sequence equals to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤⓪">size</a> of <em>splits</em>. The shape of the i-th output tensor is the same as <em>input</em> except along <em>axis</em> where the dimension size is <em>splits[i]</em>.</p>
+    <p><strong>Returns:</strong> a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑤">MLOperand</a></code>. The splitted output tensors. If <em>splits</em> is an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥③">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④⑧">size</a> of the output sequence equals to <em>splits</em>. The shape of each output tensor is the same as <em>input</em> except the dimension size of <em>axis</em> equals to the quotient of dividing the dimension size of <em>input</em> along <em>axis</em> by <em>splits</em>. If <em>splits</em> is a sequence of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥④">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size④⑨">size</a> of the output sequence equals to the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤⓪">size</a> of <em>splits</em>. The shape of the i-th output tensor is the same as <em>input</em> except along <em>axis</em> where the dimension size is <em>splits[i]</em>.</p>
    </div>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlsplitoptions" id="ref-for-dictdef-mlsplitoptions②">MLSplitOptions</a></code> has the following members:</p>
    <dl>
@@ -7413,7 +7452,7 @@ <h4 class="heading settled" data-level="7.8.42" id="api-mlgraphbuilder-split"><s
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⑧">operator</a> for the split operation, given <var>splits</var> and <var>options</var>.</p>
        <li data-md>
-        <p>Let <var>outputs</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑤">list</a>.</p>
+        <p>Let <var>outputs</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑨">list</a>.</p>
        <li data-md>
         <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-iterate" id="ref-for-list-iterate⑦">For each</a> <var>index</var> in <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range①②">the range</a> 0 to <var>splitCount</var>, exclusive:</p>
         <ol>
@@ -7426,7 +7465,7 @@ <h4 class="heading settled" data-level="7.8.42" id="api-mlgraphbuilder-split"><s
          <li data-md>
           <p>Set <var>operand</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑦③">shape</a>[<var>axis</var>] to <var>newDimension</var>.</p>
          <li data-md>
-          <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⓪">[[operator]]</a></code> to <var>operator</var>.</p>
+          <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⑤">[[operator]]</a></code> to <var>operator</var>.</p>
          <li data-md>
           <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-append" id="ref-for-list-append②">Append</a> <var>operand</var> to <var>outputs</var>.</p>
         </ol>
@@ -7463,7 +7502,7 @@ <h4 class="heading settled" data-level="7.8.42" id="api-mlgraphbuilder-split"><s
    <h4 class="heading settled" data-level="7.8.43" id="api-mlgraphbuilder-tanh-method"><span class="secno">7.8.43. </span><span class="content">tanh</span><a class="self-link" href="#api-mlgraphbuilder-tanh-method"></a></h4>
     Compute the <a href="https://en.wikipedia.org/wiki/Hyperbolic_functions">hyperbolic tangent function</a> of the input tensor. The calculation follows the expression <code>(exp(2 * x) - 1) / (exp(2 * x) + 1)</code>. 
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⑥"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-tanh" id="ref-for-dom-mlgraphbuilder-tanh"><c- g>tanh</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/tanh(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-tanh-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-tanh-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-tanh" id="ref-for-dom-mlgraphbuilder-tanh"><c- g>tanh</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/tanh(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-tanh-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-tanh-input-input"></a></dfn>);
   <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④⑧"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#tanh-noargs" id="ref-for-tanh-noargs②"><c- g>tanh</c-></a>();
 };
 </pre>
@@ -7484,12 +7523,12 @@ <h5 class="heading settled" data-level="7.8.43.1" id="api-mlgraphbuilder-tanh-in
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑨">MLOperand</a></code>. The input tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑧">MLOperand</a></code>. The input tensor.</p>
     </ul>
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧⓪">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑦⑨">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="tanh(input)" data-algorithm-for="MLGraphBuilder" open>
@@ -7503,7 +7542,7 @@ <h5 class="heading settled" data-level="7.8.43.1" id="api-mlgraphbuilder-tanh-in
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators④⑨">operator</a> for the hyperbolic tangent operation.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④①">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⑥">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤⑧">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7530,7 +7569,7 @@ <h5 class="heading settled" data-level="7.8.43.2" id="api-mlgraphbuilder-tanh"><
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="tanh-noargs"><code>tanh()</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation①①">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑦">this</a> and "tanh".</p>
+      <p>Let <var>op</var> be the result of <a data-link-type="dfn" href="#create-an-mlactivation" id="ref-for-create-an-mlactivation①①">creating an MLActivation</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑨">this</a> and "tanh".</p>
       <ol>
        <li data-md>
         <p>If that <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②③⑨">throws</a> an error, re-<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⓪">throw</a> the error.</p>
@@ -7546,7 +7585,7 @@ <h4 class="heading settled" data-level="7.8.44" id="api-mlgraphbuilder-transpose
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-transpose" id="ref-for-dom-mlgraphbuilder-transpose"><c- g>transpose</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/transpose(input, options), MLGraphBuilder/transpose(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-transpose-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-transpose-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mltransposeoptions" id="ref-for-dictdef-mltransposeoptions"><c- n>MLTransposeOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/transpose(input, options), MLGraphBuilder/transpose(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-transpose-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-transpose-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-transpose" id="ref-for-dom-mlgraphbuilder-transpose"><c- g>transpose</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/transpose(input, options), MLGraphBuilder/transpose(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-transpose-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-transpose-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mltransposeoptions" id="ref-for-dictdef-mltransposeoptions"><c- n>MLTransposeOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/transpose(input, options), MLGraphBuilder/transpose(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-transpose-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-transpose-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mltransposeoptions" id="ref-for-dictdef-mltransposeoptions①">MLTransposeOptions</a></code> has the following members:</p>
@@ -7561,11 +7600,11 @@ <h4 class="heading settled" data-level="7.8.44" id="api-mlgraphbuilder-transpose
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧③">MLOperand</a></code>. The input N-D tensor.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧②">MLOperand</a></code>. The input N-D tensor.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mltransposeoptions" id="ref-for-dictdef-mltransposeoptions②">MLTransposeOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧④">MLOperand</a></code>. The permuted or transposed N-D tensor.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧③">MLOperand</a></code>. The permuted or transposed N-D tensor.</p>
    </div>
    <details class="algorithm" data-algorithm="transpose(input, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="transpose(input, options)|transpose(input)" id="dom-mlgraphbuilder-transpose"><code>transpose(<var>input</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -7590,7 +7629,7 @@ <h4 class="heading settled" data-level="7.8.44" id="api-mlgraphbuilder-transpose
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators⑤⓪">operator</a> for the transpose operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④②">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⑦">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑤⑨">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7608,7 +7647,7 @@ <h4 class="heading settled" data-level="7.8.45" id="api-mlgraphbuilder-triangula
 };
 
 <c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-triangular" id="ref-for-dom-mlgraphbuilder-triangular"><c- g>triangular</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/triangular(input, options), MLGraphBuilder/triangular(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-triangular-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-triangular-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mltriangularoptions" id="ref-for-dictdef-mltriangularoptions"><c- n>MLTriangularOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/triangular(input, options), MLGraphBuilder/triangular(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-triangular-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-triangular-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-triangular" id="ref-for-dom-mlgraphbuilder-triangular"><c- g>triangular</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/triangular(input, options), MLGraphBuilder/triangular(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-triangular-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-triangular-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mltriangularoptions" id="ref-for-dictdef-mltriangularoptions"><c- n>MLTriangularOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/triangular(input, options), MLGraphBuilder/triangular(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-triangular-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-triangular-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mltriangularoptions" id="ref-for-dictdef-mltriangularoptions①">MLTriangularOptions</a></code> has the following members:</p>
@@ -7624,11 +7663,11 @@ <h4 class="heading settled" data-level="7.8.45" id="api-mlgraphbuilder-triangula
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧⑦">MLOperand</a></code>. The input tensor which is at least 2-D.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧⑥">MLOperand</a></code>. The input tensor which is at least 2-D.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mltriangularoptions" id="ref-for-dictdef-mltriangularoptions②">MLTriangularOptions</a></code>. The optional parameters of the operation.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧⑧">MLOperand</a></code>. The output tensor representing a triangular matrix, or batch of matrices which is the same shape as the input.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑧⑦">MLOperand</a></code>. The output tensor representing a triangular matrix, or batch of matrices which is the same shape as the input.</p>
    </div>
    <details class="algorithm" data-algorithm="triangular(input, options)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export data-lt="triangular(input, options)|triangular(input)" id="dom-mlgraphbuilder-triangular"><code>triangular(<var>input</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -7643,7 +7682,7 @@ <h4 class="heading settled" data-level="7.8.45" id="api-mlgraphbuilder-triangula
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators⑤①">operator</a> for the triangular operation, given <var>options</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④③">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⑧">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑥⓪">input</a> to <var>input</var>.</p>
        <li data-md>
@@ -7716,49 +7755,49 @@ <h4 class="heading settled" data-level="7.8.46" id="api-mlgraphbuilder-where"><s
    <p>The input tensors will be broadcasted according to <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a> to the final output shape. The <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑥⑨">rank</a> of the output tensor is the maximum <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑦⓪">rank</a> of the input tensors.
 For each dimension of the output tensor, its size is the maximum size along that dimension of the input tensors.</p>
 <pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑤⑨"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-where" id="ref-for-dom-mlgraphbuilder-where"><c- g>where</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-condition"><code><c- g>condition</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-condition"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-other"><code><c- g>other</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-other"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-where" id="ref-for-dom-mlgraphbuilder-where"><c- g>where</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-condition"><code><c- g>condition</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-condition"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/where(condition, input, other)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-where-condition-input-other-other"><code><c- g>other</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-other"></a></dfn>);
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>condition</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨③">MLOperand</a></code>. The condition tensor.</p>
+      <p><em>condition</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨②">MLOperand</a></code>. The condition tensor.</p>
      <li data-md>
-      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨④">MLOperand</a></code>. The input tensor from which the value is selected when the condition of the corresponding element is set to true.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨③">MLOperand</a></code>. The input tensor from which the value is selected when the condition of the corresponding element is set to true.</p>
      <li data-md>
-      <p><em>other</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑤">MLOperand</a></code>. The other tensor from which the value is selected when the condition of the corresponding element is set to false.</p>
+      <p><em>other</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨④">MLOperand</a></code>. The other tensor from which the value is selected when the condition of the corresponding element is set to false.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑥">MLOperand</a></code>. The output tensor that contains the values selected element-wise from either the input or the other tensor.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑤">MLOperand</a></code>. The output tensor that contains the values selected element-wise from either the input or the other tensor.</p>
    </div>
    <details class="algorithm" data-algorithm="where(condition, input, other)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="method" data-export id="dom-mlgraphbuilder-where"><code>where(<var>condition</var>, <var>input</var>, <var>other</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
-      <p>If <var>condition</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③①">dataType</a> is not equal to <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-uint8" id="ref-for-dom-mloperanddatatype-uint8②">"uint8"</a></code>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑤">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①④⑦">TypeError</a></code>.</p>
+      <p>If <var>condition</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③③">dataType</a> is not equal to <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-uint8" id="ref-for-dom-mloperanddatatype-uint8②">"uint8"</a></code>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑤">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①④⑦">TypeError</a></code>.</p>
      <li data-md>
-      <p>If <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③②">dataType</a> is not equal to <var>other</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③③">dataType</a>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑥">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①④⑧">TypeError</a></code>.</p>
+      <p>If <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③④">dataType</a> is not equal to <var>other</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③⑤">dataType</a>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑥">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①④⑧">TypeError</a></code>.</p>
      <li data-md>
-      <p>Let <var>descriptor</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⑤">MLOperandDescriptor</a></code>.</p>
+      <p>Let <var>descriptor</var> be a new <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③⑦">MLOperandDescriptor</a></code>.</p>
      <li data-md>
-      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②②">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③④">dataType</a>.</p>
+      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype②④">dataType</a></code> to <var>input</var>’s <a data-link-type="dfn" href="#mloperand-datatype" id="ref-for-mloperand-datatype③⑥">dataType</a>.</p>
      <li data-md>
-      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑧">dimensions</a></code> to the result of <a data-link-type="dfn" href="#bidirectionally-broadcasting-the-shapes" id="ref-for-bidirectionally-broadcasting-the-shapes③">bidirectionally broadcasting the shapes</a> <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑦⑤">shape</a> and <var>other</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑦⑥">shape</a>.</p>
+      <p>Set <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions③⓪">dimensions</a></code> to the result of <a data-link-type="dfn" href="#bidirectionally-broadcasting-the-shapes" id="ref-for-bidirectionally-broadcasting-the-shapes③">bidirectionally broadcasting the shapes</a> <var>input</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑦⑤">shape</a> and <var>other</var>’s <a data-link-type="dfn" href="#mloperand-shape" id="ref-for-mloperand-shape⑦⑥">shape</a>.</p>
       <ol>
        <li data-md>
         <p>If that returns failure, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑦">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①④⑨">TypeError</a></code>.</p>
       </ol>
      <li data-md>
-      <p>If <var>condition</var> is not <a data-link-type="dfn" href="#bidirectionally-broadcastable" id="ref-for-bidirectionally-broadcastable">bidirectionally broadcastable</a> to <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions②⑨">dimensions</a></code>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑧">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①⑤⓪">TypeError</a></code>.</p>
+      <p>If <var>condition</var> is not <a data-link-type="dfn" href="#bidirectionally-broadcastable" id="ref-for-bidirectionally-broadcastable">bidirectionally broadcastable</a> to <var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions③①">dimensions</a></code>, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw②④⑧">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror①⑤⓪">TypeError</a></code>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
-        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑦">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑤⑧">this</a> and <var>descriptor</var>.</p>
+        <p>Let <var>output</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand②⑨">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this⑥⓪">this</a> and <var>descriptor</var>.</p>
        <li data-md>
         <p>Let <var>operator</var> be an <a data-link-type="dfn" href="#operators" id="ref-for-operators⑤②">operator</a> for the where operation, given <var>condition</var>, <var>input</var> and <var>other</var>.</p>
        <li data-md>
-        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④④">[[operator]]</a></code> to <var>operator</var>.</p>
+        <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot④⑨">[[operator]]</a></code> to <var>operator</var>.</p>
        <li data-md>
         <p>Set <var>operator</var>’s <a data-link-type="dfn" href="#operator-input" id="ref-for-operator-input⑥①">inputs</a> to <var>condition</var>, <var>input</var> and <var>other</var>.</p>
        <li data-md>
@@ -7786,7 +7825,7 @@ <h2 class="heading settled" data-level="8" id="algorithms"><span class="secno">8
    <h3 class="heading settled" data-level="8.1" id="algorithms-broadcasting"><span class="secno">8.1. </span><span class="content">Broadcasting</span><a class="self-link" href="#algorithms-broadcasting"></a></h3>
    <p>Broadcasting refers to how operations treat tensors with different shapes, and follow the precedent set by <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>.</p>
    <details class="algorithm" data-algorithm="unidirectionally broadcasting the shapes" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-lt="unidirectionally broadcasting the shapes" data-noexport id="unidirectionally-broadcasting-the-shapes">unidirectionally broadcast the shapes</dfn> <var>A</var> and <var>B</var>, perform the following steps. <var>A</var> and <var>B</var> are <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑥">lists</a> of positive integers, representing the dimensions of tensors, and the steps return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑦">list</a> of positive integers, or failure. </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-lt="unidirectionally broadcasting the shapes" data-noexport id="unidirectionally-broadcasting-the-shapes">unidirectionally broadcast the shapes</dfn> <var>A</var> and <var>B</var>, perform the following steps. <var>A</var> and <var>B</var> are <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤⓪">lists</a> of positive integers, representing the dimensions of tensors, and the steps return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤①">list</a> of positive integers, or failure. </summary>
     <ol>
      <li data-md>
       <p>Let <var>sizeA</var> be <var>A</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤②">size</a>.</p>
@@ -7799,7 +7838,7 @@ <h3 class="heading settled" data-level="8.1" id="algorithms-broadcasting"><span
      <li data-md>
       <p>While <var>paddedB</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤④">size</a> is less than <var>sizeA</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-prepend" id="ref-for-list-prepend">prepend</a> 1 to <var>paddedB</var>.</p>
      <li data-md>
-      <p>Let <var>outputShape</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑧">list</a>.</p>
+      <p>Let <var>outputShape</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤②">list</a>.</p>
      <li data-md>
       <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-iterate" id="ref-for-list-iterate⑧">For each</a> <var>index</var> in <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range①④">the range</a> 0 to <var>sizeA</var>, exclusive:</p>
       <ol>
@@ -7818,7 +7857,7 @@ <h3 class="heading settled" data-level="8.1" id="algorithms-broadcasting"><span
    </details>
    <p class="algorithm" data-algorithm="unidirectionally broadcastable"> <var>A</var> is <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="unidirectionally-broadcastable">unidirectionally broadcastable</dfn> to <var>B</var> if <a data-link-type="dfn" href="#unidirectionally-broadcasting-the-shapes" id="ref-for-unidirectionally-broadcasting-the-shapes②">unidirectionally broadcasting the shapes</a> <var>A</var> and <var>B</var> does not result in failure. </p>
    <details class="algorithm" data-algorithm="bidirectionally broadcasting the shapes" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-lt="bidirectionally broadcasting the shapes" data-noexport id="bidirectionally-broadcasting-the-shapes">bidirectionally broadcast the shapes</dfn> <var>A</var> and <var>B</var>, perform the following steps. <var>A</var> and <var>B</var> are <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list④⑨">lists</a> of positive integers, representing the dimensions of tensors, and the steps return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤⓪">list</a> of positive integers, or failure. </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-lt="bidirectionally broadcasting the shapes" data-noexport id="bidirectionally-broadcasting-the-shapes">bidirectionally broadcast the shapes</dfn> <var>A</var> and <var>B</var>, perform the following steps. <var>A</var> and <var>B</var> are <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤③">lists</a> of positive integers, representing the dimensions of tensors, and the steps return a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤④">list</a> of positive integers, or failure. </summary>
     <ol>
      <li data-md>
       <p>Let <var>sizeA</var> be <var>A</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤⑤">size</a>.</p>
@@ -7835,7 +7874,7 @@ <h3 class="heading settled" data-level="8.1" id="algorithms-broadcasting"><span
      <li data-md>
       <p>While <var>paddedB</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤⑧">size</a> is less than <var>outputSize</var>, <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-prepend" id="ref-for-list-prepend②">prepend</a> 1 to <var>paddedB</var>.</p>
      <li data-md>
-      <p>Let <var>outputShape</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤①">list</a>.</p>
+      <p>Let <var>outputShape</var> be a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list" id="ref-for-list⑤⑤">list</a>.</p>
      <li data-md>
       <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-iterate" id="ref-for-list-iterate⑨">For each</a> <var>index</var> in <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#the-range" id="ref-for-the-range①⑤">the range</a> 0 to <var>outputSize</var>, exclusive:</p>
       <ol>
@@ -9346,7 +9385,6 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
    <div class="issue"> Decide how to specify graph initialization. <a href="https://github.com/webmachinelearning/webnn/issues/552">[Issue #552]</a> <a class="issue-return" href="#issue-f13b8ed7" title="Jump to section">↵</a></div>
    <div class="issue"> Decide how to specify graph initialization. <a href="https://github.com/webmachinelearning/webnn/issues/552">[Issue #552]</a> <a class="issue-return" href="#issue-f13b8ed7①" title="Jump to section">↵</a></div>
    <div class="issue"> More fully specify the behavior of <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build">build()</a></code> in terms of traversing the graph of <a data-link-type="dfn" href="#operators">operators</a> and <code class="idl"><a data-link-type="idl" href="#mloperand">MLOperand</a></code>s, creating the corresponding <a data-link-type="dfn" href="#platform-operators">platform operators</a> and <a data-link-type="dfn" href="#platform-operands">platform operands</a>, and connecting <a data-link-type="dfn" href="#operator-input">inputs</a>, <a data-link-type="dfn" href="#operator-output">outputs</a>, and <a data-link-type="dfn" href="#operator-activation">activations</a>. <a href="https://github.com/webmachinelearning/webnn/issues/448">[Issue #448]</a> <a class="issue-return" href="#issue-c2da0124" title="Jump to section">↵</a></div>
-   <div class="issue"> Populate <var>output</var> <a class="issue-return" href="#issue-0952c806" title="Jump to section">↵</a></div>
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"ref-for-mloperand\u2461\u24ea\u2468"}], "title": "7.8.19. gru"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u24ea"}, {"id": "ref-for-mloperand\u2461\u2460\u2460"}, {"id": "ref-for-mloperand\u2461\u2460\u2461"}, {"id": "ref-for-mloperand\u2461\u2460\u2462"}, {"id": "ref-for-mloperand\u2461\u2460\u2463"}, {"id": "ref-for-mloperand\u2461\u2460\u2464"}, {"id": "ref-for-mloperand\u2461\u2460\u2465"}, {"id": "ref-for-mloperand\u2461\u2460\u2466"}, {"id": "ref-for-mloperand\u2461\u2460\u2467"}, {"id": "ref-for-mloperand\u2461\u2460\u2468"}, {"id": "ref-for-mloperand\u2461\u2461\u24ea"}, {"id": "ref-for-mloperand\u2461\u2461\u2460"}, {"id": "ref-for-mloperand\u2461\u2461\u2461"}, {"id": "ref-for-mloperand\u2461\u2461\u2462"}], "title": "7.8.20. gruCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2463"}, {"id": "ref-for-mloperand\u2461\u2461\u2464"}], "title": "7.8.21. hardSigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2465"}, {"id": "ref-for-mloperand\u2461\u2461\u2466"}], "title": "7.8.21.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2467"}, {"id": "ref-for-mloperand\u2461\u2461\u2468"}], "title": "7.8.22. hardSwish"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u24ea"}, {"id": "ref-for-mloperand\u2461\u2462\u2460"}], "title": "7.8.22.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2461"}, {"id": "ref-for-mloperand\u2461\u2462\u2462"}, {"id": "ref-for-mloperand\u2461\u2462\u2463"}, {"id": "ref-for-mloperand\u2461\u2462\u2464"}, {"id": "ref-for-mloperand\u2461\u2462\u2465"}, {"id": "ref-for-mloperand\u2461\u2462\u2466"}, {"id": "ref-for-mloperand\u2461\u2462\u2467"}, {"id": "ref-for-mloperand\u2461\u2462\u2468"}], "title": "7.8.23. instanceNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u24ea"}, {"id": "ref-for-mloperand\u2461\u2463\u2460"}, {"id": "ref-for-mloperand\u2461\u2463\u2461"}, {"id": "ref-for-mloperand\u2461\u2463\u2462"}, {"id": "ref-for-mloperand\u2461\u2463\u2463"}, {"id": "ref-for-mloperand\u2461\u2463\u2464"}, {"id": "ref-for-mloperand\u2461\u2463\u2465"}, {"id": "ref-for-mloperand\u2461\u2463\u2466"}], "title": "7.8.24. layerNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2467"}, {"id": "ref-for-mloperand\u2461\u2463\u2468"}], "title": "7.8.25. leakyRelu"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u24ea"}, {"id": "ref-for-mloperand\u2461\u2464\u2460"}], "title": "7.8.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2461"}, {"id": "ref-for-mloperand\u2461\u2464\u2462"}], "title": "7.8.26. linear"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2463"}, {"id": "ref-for-mloperand\u2461\u2464\u2464"}], "title": "7.8.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2465"}, {"id": "ref-for-mloperand\u2461\u2464\u2466"}, {"id": "ref-for-mloperand\u2461\u2464\u2467"}, {"id": "ref-for-mloperand\u2461\u2464\u2468"}, {"id": "ref-for-mloperand\u2461\u2465\u24ea"}, {"id": "ref-for-mloperand\u2461\u2465\u2460"}, {"id": "ref-for-mloperand\u2461\u2465\u2461"}, {"id": "ref-for-mloperand\u2461\u2465\u2462"}, {"id": "ref-for-mloperand\u2461\u2465\u2463"}, {"id": "ref-for-mloperand\u2461\u2465\u2464"}, {"id": "ref-for-mloperand\u2461\u2465\u2465"}, {"id": "ref-for-mloperand\u2461\u2465\u2466"}, {"id": "ref-for-mloperand\u2461\u2465\u2467"}, {"id": "ref-for-mloperand\u2461\u2465\u2468"}, {"id": "ref-for-mloperand\u2461\u2466\u24ea"}, {"id": "ref-for-mloperand\u2461\u2466\u2460"}, {"id": "ref-for-mloperand\u2461\u2466\u2461"}, {"id": "ref-for-mloperand\u2461\u2466\u2462"}], "title": "7.8.27. lstm"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2466\u2463"}, {"id": "ref-for-mloperand\u2461\u2466\u2464"}, {"id": "ref-for-mloperand\u2461\u2466\u2465"}, {"id": "ref-for-mloperand\u2461\u2466\u2466"}, {"id": "ref-for-mloperand\u2461\u2466\u2467"}, {"id": "ref-for-mloperand\u2461\u2466\u2468"}, {"id": "ref-for-mloperand\u2461\u2467\u24ea"}, {"id": "ref-for-mloperand\u2461\u2467\u2460"}, {"id": "ref-for-mloperand\u2461\u2467\u2461"}, {"id": "ref-for-mloperand\u2461\u2467\u2462"}, {"id": "ref-for-mloperand\u2461\u2467\u2463"}, {"id": "ref-for-mloperand\u2461\u2467\u2464"}, {"id": "ref-for-mloperand\u2461\u2467\u2465"}, {"id": "ref-for-mloperand\u2461\u2467\u2466"}, {"id": "ref-for-mloperand\u2461\u2467\u2467"}, {"id": "ref-for-mloperand\u2461\u2467\u2468"}, {"id": "ref-for-mloperand\u2461\u2468\u24ea"}, {"id": "ref-for-mloperand\u2461\u2468\u2460"}], "title": "7.8.28. lstmCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2468\u2461"}, {"id": "ref-for-mloperand\u2461\u2468\u2462"}, {"id": "ref-for-mloperand\u2461\u2468\u2463"}, {"id": "ref-for-mloperand\u2461\u2468\u2464"}, {"id": "ref-for-mloperand\u2461\u2468\u2465"}, {"id": "ref-for-mloperand\u2461\u2468\u2466"}], "title": "7.8.29. matmul"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2468\u2467"}, {"id": "ref-for-mloperand\u2461\u2468\u2468"}, {"id": "ref-for-mloperand\u2462\u24ea\u24ea"}, {"id": "ref-for-mloperand\u2462\u24ea\u2460"}], "title": "7.8.30. pad"}, {"refs": [{"id": "ref-for-mloperand\u2462\u24ea\u2461"}, {"id": "ref-for-mloperand\u2462\u24ea\u2462"}, {"id": "ref-for-mloperand\u2462\u24ea\u2463"}, {"id": "ref-for-mloperand\u2462\u24ea\u2464"}, {"id": "ref-for-mloperand\u2462\u24ea\u2465"}, {"id": "ref-for-mloperand\u2462\u24ea\u2466"}, {"id": "ref-for-mloperand\u2462\u24ea\u2467"}, {"id": "ref-for-mloperand\u2462\u24ea\u2468"}, {"id": "ref-for-mloperand\u2462\u2460\u24ea"}], "title": "7.8.31. Pooling operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2460\u2460"}, {"id": "ref-for-mloperand\u2462\u2460\u2461"}, {"id": "ref-for-mloperand\u2462\u2460\u2462"}, {"id": "ref-for-mloperand\u2462\u2460\u2463"}, {"id": "ref-for-mloperand\u2462\u2460\u2464"}, {"id": "ref-for-mloperand\u2462\u2460\u2465"}], "title": "7.8.32. prelu"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2460\u2466"}, {"id": "ref-for-mloperand\u2462\u2460\u2467"}, {"id": "ref-for-mloperand\u2462\u2460\u2468"}, {"id": "ref-for-mloperand\u2462\u2461\u24ea"}, {"id": "ref-for-mloperand\u2462\u2461\u2460"}, {"id": "ref-for-mloperand\u2462\u2461\u2461"}, {"id": "ref-for-mloperand\u2462\u2461\u2462"}, {"id": "ref-for-mloperand\u2462\u2461\u2463"}, {"id": "ref-for-mloperand\u2462\u2461\u2464"}, {"id": "ref-for-mloperand\u2462\u2461\u2465"}, {"id": "ref-for-mloperand\u2462\u2461\u2466"}, {"id": "ref-for-mloperand\u2462\u2461\u2467"}, {"id": "ref-for-mloperand\u2462\u2461\u2468"}, {"id": "ref-for-mloperand\u2462\u2462\u24ea"}, {"id": "ref-for-mloperand\u2462\u2462\u2460"}, {"id": "ref-for-mloperand\u2462\u2462\u2461"}, {"id": "ref-for-mloperand\u2462\u2462\u2462"}, {"id": "ref-for-mloperand\u2462\u2462\u2463"}, {"id": "ref-for-mloperand\u2462\u2462\u2464"}, {"id": "ref-for-mloperand\u2462\u2462\u2465"}, {"id": "ref-for-mloperand\u2462\u2462\u2466"}, {"id": "ref-for-mloperand\u2462\u2462\u2467"}, {"id": "ref-for-mloperand\u2462\u2462\u2468"}], "title": "7.8.33. Reduction operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u24ea"}, {"id": "ref-for-mloperand\u2462\u2463\u2460"}], "title": "7.8.34. relu"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2461"}, {"id": "ref-for-mloperand\u2462\u2463\u2462"}], "title": "7.8.34.1. relu(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2463"}, {"id": "ref-for-mloperand\u2462\u2463\u2464"}, {"id": "ref-for-mloperand\u2462\u2463\u2465"}, {"id": "ref-for-mloperand\u2462\u2463\u2466"}, {"id": "ref-for-mloperand\u2462\u2463\u2467"}], "title": "7.8.35. resample2d"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2468"}, {"id": "ref-for-mloperand\u2462\u2464\u24ea"}, {"id": "ref-for-mloperand\u2462\u2464\u2460"}, {"id": "ref-for-mloperand\u2462\u2464\u2461"}], "title": "7.8.36. reshape"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2462"}, {"id": "ref-for-mloperand\u2462\u2464\u2463"}], "title": "7.8.37. sigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2464"}, {"id": "ref-for-mloperand\u2462\u2464\u2465"}], "title": "7.8.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2466"}, {"id": "ref-for-mloperand\u2462\u2464\u2467"}, {"id": "ref-for-mloperand\u2462\u2464\u2468"}, {"id": "ref-for-mloperand\u2462\u2465\u24ea"}], "title": "7.8.38. slice"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2460"}, {"id": "ref-for-mloperand\u2462\u2465\u2461"}], "title": "7.8.39. softmax"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2462"}, {"id": "ref-for-mloperand\u2462\u2465\u2463"}], "title": "7.8.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2464"}, {"id": "ref-for-mloperand\u2462\u2465\u2465"}], "title": "7.8.40. softplus"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2466"}, {"id": "ref-for-mloperand\u2462\u2465\u2467"}], "title": "7.8.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2468"}, {"id": "ref-for-mloperand\u2462\u2466\u24ea"}], "title": "7.8.41. softsign"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2460"}, {"id": "ref-for-mloperand\u2462\u2466\u2461"}], "title": "7.8.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2462"}, {"id": "ref-for-mloperand\u2462\u2466\u2463"}, {"id": "ref-for-mloperand\u2462\u2466\u2464"}, {"id": "ref-for-mloperand\u2462\u2466\u2465"}], "title": "7.8.42. split"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2466"}, {"id": "ref-for-mloperand\u2462\u2466\u2467"}], "title": "7.8.43. tanh"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2468"}, {"id": "ref-for-mloperand\u2462\u2467\u24ea"}], "title": "7.8.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u2460"}, {"id": "ref-for-mloperand\u2462\u2467\u2461"}, {"id": "ref-for-mloperand\u2462\u2467\u2462"}, {"id": "ref-for-mloperand\u2462\u2467\u2463"}], "title": "7.8.44. transpose"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u2464"}, {"id": "ref-for-mloperand\u2462\u2467\u2465"}, {"id": "ref-for-mloperand\u2462\u2467\u2466"}, {"id": "ref-for-mloperand\u2462\u2467\u2467"}], "title": "7.8.45. triangular"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u2468"}, {"id": "ref-for-mloperand\u2462\u2468\u24ea"}, {"id": "ref-for-mloperand\u2462\u2468\u2460"}, {"id": "ref-for-mloperand\u2462\u2468\u2461"}, {"id": "ref-for-mloperand\u2462\u2468\u2462"}, {"id": "ref-for-mloperand\u2462\u2468\u2463"}, {"id": "ref-for-mloperand\u2462\u2468\u2464"}, {"id": "ref-for-mloperand\u2462\u2468\u2465"}], "title": "7.8.46. where"}], "external": false};
+window.dfnpanelData['mloperand'] = {"dfnID": "mloperand", "url": "#mloperand", "dfnText": "MLOperand", "refSections": [{"refs": [{"id": "ref-for-mloperand"}, {"id": "ref-for-mloperand\u2460"}, {"id": "ref-for-mloperand\u2461"}, {"id": "ref-for-mloperand\u2462"}, {"id": "ref-for-mloperand\u2463"}, {"id": "ref-for-mloperand\u2464"}, {"id": "ref-for-mloperand\u2465"}, {"id": "ref-for-mloperand\u2466"}, {"id": "ref-for-mloperand\u2467"}, {"id": "ref-for-mloperand\u2468"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mloperand\u2460\u24ea"}, {"id": "ref-for-mloperand\u2460\u2460"}, {"id": "ref-for-mloperand\u2460\u2461"}, {"id": "ref-for-mloperand\u2460\u2462"}], "title": "7.4. MLGraph interface"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2463"}], "title": "7.5. MLOperandDescriptor dictionary"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2464"}, {"id": "ref-for-mloperand\u2460\u2465"}, {"id": "ref-for-mloperand\u2460\u2466"}, {"id": "ref-for-mloperand\u2460\u2467"}, {"id": "ref-for-mloperand\u2460\u2468"}, {"id": "ref-for-mloperand\u2461\u24ea"}, {"id": "ref-for-mloperand\u2461\u2460"}, {"id": "ref-for-mloperand\u2461\u2461"}, {"id": "ref-for-mloperand\u2461\u2462"}, {"id": "ref-for-mloperand\u2461\u2463"}, {"id": "ref-for-mloperand\u2461\u2464"}, {"id": "ref-for-mloperand\u2461\u2465"}], "title": "7.6. MLOperand interface"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2466"}, {"id": "ref-for-mloperand\u2461\u2467"}, {"id": "ref-for-mloperand\u2461\u2468"}, {"id": "ref-for-mloperand\u2462\u24ea"}, {"id": "ref-for-mloperand\u2462\u2460"}, {"id": "ref-for-mloperand\u2462\u2461"}], "title": "7.6.1. Creating an MLOperand"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2462"}], "title": "7.6.2. dataType()"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463"}], "title": "7.6.3. shape()"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464"}, {"id": "ref-for-mloperand\u2462\u2465"}, {"id": "ref-for-mloperand\u2462\u2466"}, {"id": "ref-for-mloperand\u2462\u2467"}], "title": "7.8. MLGraphBuilder interface"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2468"}, {"id": "ref-for-mloperand\u2463\u24ea"}], "title": "7.8.2. input operands"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2460"}], "title": "7.8.3. constant operands"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2461"}, {"id": "ref-for-mloperand\u2463\u2462"}], "title": "7.8.3.1. constant(descriptor, bufferView)"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2463"}, {"id": "ref-for-mloperand\u2463\u2464"}], "title": "7.8.3.2. constant(value, type)"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2465"}, {"id": "ref-for-mloperand\u2463\u2466"}], "title": "7.8.3.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2467"}], "title": "7.8.4. build method"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2468"}, {"id": "ref-for-mloperand\u2464\u24ea"}, {"id": "ref-for-mloperand\u2464\u2460"}, {"id": "ref-for-mloperand\u2464\u2461"}, {"id": "ref-for-mloperand\u2464\u2462"}, {"id": "ref-for-mloperand\u2464\u2463"}, {"id": "ref-for-mloperand\u2464\u2464"}], "title": "7.8.5. argMin/argMax operations"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2465"}, {"id": "ref-for-mloperand\u2464\u2466"}, {"id": "ref-for-mloperand\u2464\u2467"}, {"id": "ref-for-mloperand\u2464\u2468"}, {"id": "ref-for-mloperand\u2465\u24ea"}, {"id": "ref-for-mloperand\u2465\u2460"}, {"id": "ref-for-mloperand\u2465\u2461"}, {"id": "ref-for-mloperand\u2465\u2462"}, {"id": "ref-for-mloperand\u2465\u2463"}, {"id": "ref-for-mloperand\u2465\u2464"}, {"id": "ref-for-mloperand\u2465\u2465"}, {"id": "ref-for-mloperand\u2465\u2466"}], "title": "7.8.6. batchNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2465\u2467"}, {"id": "ref-for-mloperand\u2465\u2468"}, {"id": "ref-for-mloperand\u2466\u24ea"}, {"id": "ref-for-mloperand\u2466\u2460"}], "title": "7.8.7. cast"}, {"refs": [{"id": "ref-for-mloperand\u2466\u2461"}, {"id": "ref-for-mloperand\u2466\u2462"}], "title": "7.8.8. clamp"}, {"refs": [{"id": "ref-for-mloperand\u2466\u2463"}, {"id": "ref-for-mloperand\u2466\u2464"}], "title": "7.8.8.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2466\u2465"}, {"id": "ref-for-mloperand\u2466\u2466"}, {"id": "ref-for-mloperand\u2466\u2467"}, {"id": "ref-for-mloperand\u2466\u2468"}], "title": "7.8.9. concat"}, {"refs": [{"id": "ref-for-mloperand\u2467\u24ea"}, {"id": "ref-for-mloperand\u2467\u2460"}, {"id": "ref-for-mloperand\u2467\u2461"}, {"id": "ref-for-mloperand\u2467\u2462"}, {"id": "ref-for-mloperand\u2467\u2463"}, {"id": "ref-for-mloperand\u2467\u2464"}, {"id": "ref-for-mloperand\u2467\u2465"}, {"id": "ref-for-mloperand\u2467\u2466"}], "title": "7.8.10. conv2d"}, {"refs": [{"id": "ref-for-mloperand\u2467\u2467"}, {"id": "ref-for-mloperand\u2467\u2468"}, {"id": "ref-for-mloperand\u2468\u24ea"}, {"id": "ref-for-mloperand\u2468\u2460"}, {"id": "ref-for-mloperand\u2468\u2461"}, {"id": "ref-for-mloperand\u2468\u2462"}, {"id": "ref-for-mloperand\u2468\u2463"}, {"id": "ref-for-mloperand\u2468\u2464"}], "title": "7.8.11. convTranspose2d"}, {"refs": [{"id": "ref-for-mloperand\u2468\u2465"}, {"id": "ref-for-mloperand\u2468\u2466"}, {"id": "ref-for-mloperand\u2468\u2467"}, {"id": "ref-for-mloperand\u2468\u2468"}, {"id": "ref-for-mloperand\u2460\u24ea\u24ea"}, {"id": "ref-for-mloperand\u2460\u24ea\u2460"}, {"id": "ref-for-mloperand\u2460\u24ea\u2461"}, {"id": "ref-for-mloperand\u2460\u24ea\u2462"}, {"id": "ref-for-mloperand\u2460\u24ea\u2463"}, {"id": "ref-for-mloperand\u2460\u24ea\u2464"}, {"id": "ref-for-mloperand\u2460\u24ea\u2465"}, {"id": "ref-for-mloperand\u2460\u24ea\u2466"}, {"id": "ref-for-mloperand\u2460\u24ea\u2467"}, {"id": "ref-for-mloperand\u2460\u24ea\u2468"}, {"id": "ref-for-mloperand\u2460\u2460\u24ea"}, {"id": "ref-for-mloperand\u2460\u2460\u2460"}, {"id": "ref-for-mloperand\u2460\u2460\u2461"}, {"id": "ref-for-mloperand\u2460\u2460\u2462"}, {"id": "ref-for-mloperand\u2460\u2460\u2463"}, {"id": "ref-for-mloperand\u2460\u2460\u2464"}, {"id": "ref-for-mloperand\u2460\u2460\u2465"}, {"id": "ref-for-mloperand\u2460\u2460\u2466"}, {"id": "ref-for-mloperand\u2460\u2460\u2467"}, {"id": "ref-for-mloperand\u2460\u2460\u2468"}, {"id": "ref-for-mloperand\u2460\u2461\u24ea"}, {"id": "ref-for-mloperand\u2460\u2461\u2460"}], "title": "7.8.12. Element-wise binary operations"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2461\u2461"}, {"id": "ref-for-mloperand\u2460\u2461\u2462"}, {"id": "ref-for-mloperand\u2460\u2461\u2463"}, {"id": "ref-for-mloperand\u2460\u2461\u2464"}, {"id": "ref-for-mloperand\u2460\u2461\u2465"}, {"id": "ref-for-mloperand\u2460\u2461\u2466"}, {"id": "ref-for-mloperand\u2460\u2461\u2467"}, {"id": "ref-for-mloperand\u2460\u2461\u2468"}, {"id": "ref-for-mloperand\u2460\u2462\u24ea"}, {"id": "ref-for-mloperand\u2460\u2462\u2460"}, {"id": "ref-for-mloperand\u2460\u2462\u2461"}, {"id": "ref-for-mloperand\u2460\u2462\u2462"}, {"id": "ref-for-mloperand\u2460\u2462\u2463"}, {"id": "ref-for-mloperand\u2460\u2462\u2464"}, {"id": "ref-for-mloperand\u2460\u2462\u2465"}, {"id": "ref-for-mloperand\u2460\u2462\u2466"}, {"id": "ref-for-mloperand\u2460\u2462\u2467"}, {"id": "ref-for-mloperand\u2460\u2462\u2468"}, {"id": "ref-for-mloperand\u2460\u2463\u24ea"}, {"id": "ref-for-mloperand\u2460\u2463\u2460"}, {"id": "ref-for-mloperand\u2460\u2463\u2461"}, {"id": "ref-for-mloperand\u2460\u2463\u2462"}], "title": "7.8.13. Element-wise logical operations"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2463\u2463"}, {"id": "ref-for-mloperand\u2460\u2463\u2464"}, {"id": "ref-for-mloperand\u2460\u2463\u2465"}, {"id": "ref-for-mloperand\u2460\u2463\u2466"}, {"id": "ref-for-mloperand\u2460\u2463\u2467"}, {"id": "ref-for-mloperand\u2460\u2463\u2468"}, {"id": "ref-for-mloperand\u2460\u2464\u24ea"}, {"id": "ref-for-mloperand\u2460\u2464\u2460"}, {"id": "ref-for-mloperand\u2460\u2464\u2461"}, {"id": "ref-for-mloperand\u2460\u2464\u2462"}, {"id": "ref-for-mloperand\u2460\u2464\u2463"}, {"id": "ref-for-mloperand\u2460\u2464\u2464"}, {"id": "ref-for-mloperand\u2460\u2464\u2465"}, {"id": "ref-for-mloperand\u2460\u2464\u2466"}, {"id": "ref-for-mloperand\u2460\u2464\u2467"}, {"id": "ref-for-mloperand\u2460\u2464\u2468"}, {"id": "ref-for-mloperand\u2460\u2465\u24ea"}, {"id": "ref-for-mloperand\u2460\u2465\u2460"}, {"id": "ref-for-mloperand\u2460\u2465\u2461"}, {"id": "ref-for-mloperand\u2460\u2465\u2462"}, {"id": "ref-for-mloperand\u2460\u2465\u2463"}, {"id": "ref-for-mloperand\u2460\u2465\u2464"}, {"id": "ref-for-mloperand\u2460\u2465\u2465"}, {"id": "ref-for-mloperand\u2460\u2465\u2466"}, {"id": "ref-for-mloperand\u2460\u2465\u2467"}, {"id": "ref-for-mloperand\u2460\u2465\u2468"}, {"id": "ref-for-mloperand\u2460\u2466\u24ea"}, {"id": "ref-for-mloperand\u2460\u2466\u2460"}, {"id": "ref-for-mloperand\u2460\u2466\u2461"}], "title": "7.8.14. Element-wise unary operations"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2466\u2462"}, {"id": "ref-for-mloperand\u2460\u2466\u2463"}], "title": "7.8.15. elu"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2466\u2464"}, {"id": "ref-for-mloperand\u2460\u2466\u2465"}], "title": "7.8.15.1. elu(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2466\u2466"}, {"id": "ref-for-mloperand\u2460\u2466\u2467"}, {"id": "ref-for-mloperand\u2460\u2466\u2468"}, {"id": "ref-for-mloperand\u2460\u2467\u24ea"}], "title": "7.8.16. expand"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2467\u2460"}, {"id": "ref-for-mloperand\u2460\u2467\u2461"}, {"id": "ref-for-mloperand\u2460\u2467\u2462"}, {"id": "ref-for-mloperand\u2460\u2467\u2463"}, {"id": "ref-for-mloperand\u2460\u2467\u2464"}, {"id": "ref-for-mloperand\u2460\u2467\u2465"}], "title": "7.8.17. gather"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2467\u2466"}, {"id": "ref-for-mloperand\u2460\u2467\u2467"}, {"id": "ref-for-mloperand\u2460\u2467\u2468"}, {"id": "ref-for-mloperand\u2460\u2468\u24ea"}, {"id": "ref-for-mloperand\u2460\u2468\u2460"}, {"id": "ref-for-mloperand\u2460\u2468\u2461"}, {"id": "ref-for-mloperand\u2460\u2468\u2462"}, {"id": "ref-for-mloperand\u2460\u2468\u2463"}], "title": "7.8.18. gemm"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2468\u2464"}, {"id": "ref-for-mloperand\u2460\u2468\u2465"}, {"id": "ref-for-mloperand\u2460\u2468\u2466"}, {"id": "ref-for-mloperand\u2460\u2468\u2467"}, {"id": "ref-for-mloperand\u2460\u2468\u2468"}, {"id": "ref-for-mloperand\u2461\u24ea\u24ea"}, {"id": "ref-for-mloperand\u2461\u24ea\u2460"}, {"id": "ref-for-mloperand\u2461\u24ea\u2461"}, {"id": "ref-for-mloperand\u2461\u24ea\u2462"}, {"id": "ref-for-mloperand\u2461\u24ea\u2463"}, {"id": "ref-for-mloperand\u2461\u24ea\u2464"}, {"id": "ref-for-mloperand\u2461\u24ea\u2465"}, {"id": "ref-for-mloperand\u2461\u24ea\u2466"}, {"id": "ref-for-mloperand\u2461\u24ea\u2467"}], "title": "7.8.19. gru"}, {"refs": [{"id": "ref-for-mloperand\u2461\u24ea\u2468"}, {"id": "ref-for-mloperand\u2461\u2460\u24ea"}, {"id": "ref-for-mloperand\u2461\u2460\u2460"}, {"id": "ref-for-mloperand\u2461\u2460\u2461"}, {"id": "ref-for-mloperand\u2461\u2460\u2462"}, {"id": "ref-for-mloperand\u2461\u2460\u2463"}, {"id": "ref-for-mloperand\u2461\u2460\u2464"}, {"id": "ref-for-mloperand\u2461\u2460\u2465"}, {"id": "ref-for-mloperand\u2461\u2460\u2466"}, {"id": "ref-for-mloperand\u2461\u2460\u2467"}, {"id": "ref-for-mloperand\u2461\u2460\u2468"}, {"id": "ref-for-mloperand\u2461\u2461\u24ea"}, {"id": "ref-for-mloperand\u2461\u2461\u2460"}, {"id": "ref-for-mloperand\u2461\u2461\u2461"}], "title": "7.8.20. gruCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2462"}, {"id": "ref-for-mloperand\u2461\u2461\u2463"}], "title": "7.8.21. hardSigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2464"}, {"id": "ref-for-mloperand\u2461\u2461\u2465"}], "title": "7.8.21.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2466"}, {"id": "ref-for-mloperand\u2461\u2461\u2467"}], "title": "7.8.22. hardSwish"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2468"}, {"id": "ref-for-mloperand\u2461\u2462\u24ea"}], "title": "7.8.22.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2460"}, {"id": "ref-for-mloperand\u2461\u2462\u2461"}, {"id": "ref-for-mloperand\u2461\u2462\u2462"}, {"id": "ref-for-mloperand\u2461\u2462\u2463"}, {"id": "ref-for-mloperand\u2461\u2462\u2464"}, {"id": "ref-for-mloperand\u2461\u2462\u2465"}, {"id": "ref-for-mloperand\u2461\u2462\u2466"}, {"id": "ref-for-mloperand\u2461\u2462\u2467"}], "title": "7.8.23. instanceNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2468"}, {"id": "ref-for-mloperand\u2461\u2463\u24ea"}, {"id": "ref-for-mloperand\u2461\u2463\u2460"}, {"id": "ref-for-mloperand\u2461\u2463\u2461"}, {"id": "ref-for-mloperand\u2461\u2463\u2462"}, {"id": "ref-for-mloperand\u2461\u2463\u2463"}, {"id": "ref-for-mloperand\u2461\u2463\u2464"}, {"id": "ref-for-mloperand\u2461\u2463\u2465"}], "title": "7.8.24. layerNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2466"}, {"id": "ref-for-mloperand\u2461\u2463\u2467"}], "title": "7.8.25. leakyRelu"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2468"}, {"id": "ref-for-mloperand\u2461\u2464\u24ea"}], "title": "7.8.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2460"}, {"id": "ref-for-mloperand\u2461\u2464\u2461"}], "title": "7.8.26. linear"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2462"}, {"id": "ref-for-mloperand\u2461\u2464\u2463"}], "title": "7.8.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2464\u2464"}, {"id": "ref-for-mloperand\u2461\u2464\u2465"}, {"id": "ref-for-mloperand\u2461\u2464\u2466"}, {"id": "ref-for-mloperand\u2461\u2464\u2467"}, {"id": "ref-for-mloperand\u2461\u2464\u2468"}, {"id": "ref-for-mloperand\u2461\u2465\u24ea"}, {"id": "ref-for-mloperand\u2461\u2465\u2460"}, {"id": "ref-for-mloperand\u2461\u2465\u2461"}, {"id": "ref-for-mloperand\u2461\u2465\u2462"}, {"id": "ref-for-mloperand\u2461\u2465\u2463"}, {"id": "ref-for-mloperand\u2461\u2465\u2464"}, {"id": "ref-for-mloperand\u2461\u2465\u2465"}, {"id": "ref-for-mloperand\u2461\u2465\u2466"}, {"id": "ref-for-mloperand\u2461\u2465\u2467"}, {"id": "ref-for-mloperand\u2461\u2465\u2468"}, {"id": "ref-for-mloperand\u2461\u2466\u24ea"}, {"id": "ref-for-mloperand\u2461\u2466\u2460"}, {"id": "ref-for-mloperand\u2461\u2466\u2461"}], "title": "7.8.27. lstm"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2466\u2462"}, {"id": "ref-for-mloperand\u2461\u2466\u2463"}, {"id": "ref-for-mloperand\u2461\u2466\u2464"}, {"id": "ref-for-mloperand\u2461\u2466\u2465"}, {"id": "ref-for-mloperand\u2461\u2466\u2466"}, {"id": "ref-for-mloperand\u2461\u2466\u2467"}, {"id": "ref-for-mloperand\u2461\u2466\u2468"}, {"id": "ref-for-mloperand\u2461\u2467\u24ea"}, {"id": "ref-for-mloperand\u2461\u2467\u2460"}, {"id": "ref-for-mloperand\u2461\u2467\u2461"}, {"id": "ref-for-mloperand\u2461\u2467\u2462"}, {"id": "ref-for-mloperand\u2461\u2467\u2463"}, {"id": "ref-for-mloperand\u2461\u2467\u2464"}, {"id": "ref-for-mloperand\u2461\u2467\u2465"}, {"id": "ref-for-mloperand\u2461\u2467\u2466"}, {"id": "ref-for-mloperand\u2461\u2467\u2467"}, {"id": "ref-for-mloperand\u2461\u2467\u2468"}, {"id": "ref-for-mloperand\u2461\u2468\u24ea"}], "title": "7.8.28. lstmCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2468\u2460"}, {"id": "ref-for-mloperand\u2461\u2468\u2461"}, {"id": "ref-for-mloperand\u2461\u2468\u2462"}, {"id": "ref-for-mloperand\u2461\u2468\u2463"}, {"id": "ref-for-mloperand\u2461\u2468\u2464"}, {"id": "ref-for-mloperand\u2461\u2468\u2465"}], "title": "7.8.29. matmul"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2468\u2466"}, {"id": "ref-for-mloperand\u2461\u2468\u2467"}, {"id": "ref-for-mloperand\u2461\u2468\u2468"}, {"id": "ref-for-mloperand\u2462\u24ea\u24ea"}], "title": "7.8.30. pad"}, {"refs": [{"id": "ref-for-mloperand\u2462\u24ea\u2460"}, {"id": "ref-for-mloperand\u2462\u24ea\u2461"}, {"id": "ref-for-mloperand\u2462\u24ea\u2462"}, {"id": "ref-for-mloperand\u2462\u24ea\u2463"}, {"id": "ref-for-mloperand\u2462\u24ea\u2464"}, {"id": "ref-for-mloperand\u2462\u24ea\u2465"}, {"id": "ref-for-mloperand\u2462\u24ea\u2466"}, {"id": "ref-for-mloperand\u2462\u24ea\u2467"}, {"id": "ref-for-mloperand\u2462\u24ea\u2468"}], "title": "7.8.31. 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 window.dfnpanelData['dom-mloperand-builder-slot'] = {"dfnID": "dom-mloperand-builder-slot", "url": "#dom-mloperand-builder-slot", "dfnText": "[[builder]]", "refSections": [{"refs": [{"id": "ref-for-dom-mloperand-builder-slot"}], "title": "7.6. MLOperand interface"}, {"refs": [{"id": "ref-for-dom-mloperand-builder-slot\u2460"}, {"id": "ref-for-dom-mloperand-builder-slot\u2461"}, {"id": "ref-for-dom-mloperand-builder-slot\u2462"}, {"id": "ref-for-dom-mloperand-builder-slot\u2463"}], "title": "7.6.1. Creating an MLOperand"}], "external": false};
 window.dfnpanelData['dom-mloperand-descriptor-slot'] = {"dfnID": "dom-mloperand-descriptor-slot", "url": "#dom-mloperand-descriptor-slot", "dfnText": "[[descriptor]]", "refSections": [{"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460"}], "title": "7.6. MLOperand interface"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464"}], "title": "7.6.1. Creating an MLOperand"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2465"}], "title": "7.8.4. build method"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2466"}], "title": "7.8.6. batchNormalization"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2467"}], "title": "7.8.30. pad"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2468"}], "title": "7.8.31. Pooling operations"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea"}], "title": "7.8.35. resample2d"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460"}], "title": "7.8.36. reshape"}], "external": false};
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 window.dfnpanelData['copy-an-mloperand'] = {"dfnID": "copy-an-mloperand", "url": "#copy-an-mloperand", "dfnText": "copy an MLOperand", "refSections": [{"refs": [{"id": "ref-for-copy-an-mloperand"}], "title": "7.8.7. cast"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460"}], "title": "7.8.8.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2461"}], "title": "7.8.14. Element-wise unary operations"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2462"}], "title": "7.8.15.1. elu(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2463"}], "title": "7.8.21.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2464"}], "title": "7.8.22.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2465"}], "title": "7.8.23. instanceNormalization"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2466"}], "title": "7.8.24. layerNormalization"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2467"}], "title": "7.8.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2468"}], "title": "7.8.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u24ea"}], "title": "7.8.34.1. relu(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2460"}], "title": "7.8.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2461"}], "title": "7.8.38. slice"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2462"}], "title": "7.8.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2463"}], "title": "7.8.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2464"}], "title": "7.8.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2465"}], "title": "7.8.42. split"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2466"}], "title": "7.8.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2467"}], "title": "7.8.44. transpose"}, {"refs": [{"id": "ref-for-copy-an-mloperand\u2460\u2468"}], "title": "7.8.45. triangular"}], "external": false};
 window.dfnpanelData['mloperand-validate-mloperand'] = {"dfnID": "mloperand-validate-mloperand", "url": "#mloperand-validate-mloperand", "dfnText": "validate MLOperand", "refSections": [{"refs": [{"id": "ref-for-mloperand-validate-mloperand"}], "title": "7.8.4. build method"}, {"refs": [{"id": "ref-for-mloperand-validate-mloperand\u2460"}], "title": "7.8.9. concat"}], "external": false};
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@@ -9939,7 +9977,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
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