From c11ea85751a0b7f8f24c96e889e6cf8a8023e4e1 Mon Sep 17 00:00:00 2001
From: Ningxin Hu <ningxin.hu@intel.com>
Date: Thu, 28 Nov 2024 00:06:30 +0000
Subject: [PATCH] Merge pull request #790 from
 huningxin/constant_supports_shared

SHA: bcc5dc2daac0127f332ef082155784176cf734ea
Reason: push, by huningxin

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
---
 index.html | 121 ++++++++++++++++++++++++-----------------------------
 1 file changed, 54 insertions(+), 67 deletions(-)

diff --git a/index.html b/index.html
index ce218c1b..e1c134e9 100644
--- a/index.html
+++ b/index.html
@@ -7,7 +7,7 @@
   <link href="https://www.w3.org/StyleSheets/TR/2021/W3C-ED" rel="stylesheet">
   <meta content="Bikeshed version 742f3d674, updated Mon Nov 4 14:56:54 2024 -0800" name="generator">
   <link href="https://www.w3.org/TR/webnn/" rel="canonical">
-  <meta content="dd5cbdc64d29ef5387259f52f710aa47ba3a434f" name="revision">
+  <meta content="bcc5dc2daac0127f332ef082155784176cf734ea" name="revision">
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 <style>
@@ -1018,7 +1018,7 @@
   <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="2024-11-26">26 November 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="2024-11-28">28 November 2024</time></p>
    <details open>
     <summary>More details about this document</summary>
     <div data-fill-with="spec-metadata">
@@ -1200,7 +1200,7 @@ <h2 class="no-num no-toc no-ref" id="contents">Table of Contents</h2>
         <li>
          <a href="#api-mlgraphbuilder-constant"><span class="secno">7.9.3</span> <span class="content">constant operands</span></a>
          <ol class="toc">
-          <li><a href="#api-mlgraphbuilder-constant-bufferview"><span class="secno">7.9.3.1</span> <span class="content"><code class="idl"><span>constant(descriptor, bufferView)</span></code></span></a>
+          <li><a href="#api-mlgraphbuilder-constant-buffer"><span class="secno">7.9.3.1</span> <span class="content"><code class="idl"><span>constant(descriptor, buffer)</span></code></span></a>
           <li><a href="#api-mlgraphbuilder-constant-type-value"><span class="secno">7.9.3.2</span> <span class="content"><code class="idl"><span>constant(type, value)</span></code></span></a>
          </ol>
         <li><a href="#api-mlgraphbuilder-build"><span class="secno">7.9.4</span> <span class="content">build method</span></a>
@@ -2620,7 +2620,8 @@ <h3 class="heading settled" data-level="7.9" id="api-mlgraphbuilder"><span class
   <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-input" id="ref-for-dom-mlgraphbuilder-input"><c- g>input</c-></a>(<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-USVString" id="ref-for-idl-USVString⑧"><c- b>USVString</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-input-name-descriptor-name" id="ref-for-dom-mlgraphbuilder-input-name-descriptor-name"><c- g>name</c-></a>, <a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑥"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-input-name-descriptor-descriptor" id="ref-for-dom-mlgraphbuilder-input-name-descriptor-descriptor"><c- g>descriptor</c-></a>);
 
   // Create an operand for a graph constant.
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑦"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor" id="ref-for-dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"><c- g>descriptor</c-></a>, <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView⑨"><c- n>ArrayBufferView</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview" id="ref-for-dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"><c- g>bufferView</c-></a>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑦"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-buffer-descriptor" id="ref-for-dom-mlgraphbuilder-constant-descriptor-buffer-descriptor"><c- g>descriptor</c-></a>,
+                     <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#AllowSharedBufferSource" id="ref-for-AllowSharedBufferSource⑤"><c- n>AllowSharedBufferSource</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-buffer-buffer" id="ref-for-dom-mlgraphbuilder-constant-descriptor-buffer-buffer"><c- g>buffer</c-></a>);
 
   // Create a scalar operand from the specified number of the specified type.
   <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant-type-value" id="ref-for-dom-mlgraphbuilder-constant-type-value"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype①③"><c- n>MLOperandDataType</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-type-value-type" id="ref-for-dom-mlgraphbuilder-constant-type-value-type"><c- g>type</c-></a>, <a data-link-type="idl-name" href="#typedefdef-mlnumber" id="ref-for-typedefdef-mlnumber③"><c- n>MLNumber</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-type-value-value" id="ref-for-dom-mlgraphbuilder-constant-type-value-value"><c- g>value</c-></a>);
@@ -2700,34 +2701,34 @@ <h4 class="heading settled" data-level="7.9.2" id="api-mlgraphbuilder-input"><sp
    <div class="note" role="note"> The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⓪">MLGraphBuilder</a></code> API allows creating an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②⑨">MLGraph</a></code> without input operands. If the underlying platform doesn’t support that, implementations may add a stub input, or pass constants as inputs to the graph. </div>
    <h4 class="heading settled" data-level="7.9.3" id="api-mlgraphbuilder-constant"><span class="secno">7.9.3. </span><span class="content">constant operands</span><a class="self-link" href="#api-mlgraphbuilder-constant"></a></h4>
     Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑦">MLOperand</a></code> that can be used in <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②①">MLGraphBuilder</a></code> methods. 
-   <h5 class="heading settled" data-level="7.9.3.1" id="api-mlgraphbuilder-constant-bufferview"><span class="secno">7.9.3.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant②">constant(descriptor, bufferView)</a></code></span><a class="self-link" href="#api-mlgraphbuilder-constant-bufferview"></a></h5>
+   <h5 class="heading settled" data-level="7.9.3.1" id="api-mlgraphbuilder-constant-buffer"><span class="secno">7.9.3.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant②">constant(descriptor, buffer)</a></code></span><a class="self-link" href="#api-mlgraphbuilder-constant-buffer"></a></h5>
     Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑧">MLOperand</a></code> of the specified data type and shape that contains the initializing data. 
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"><code>descriptor</code></dfn>: an <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑨">MLOperandDescriptor</a></code>. The descriptor of the output tensor.</p>
+      <p><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, buffer)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-buffer-descriptor"><code>descriptor</code></dfn>: an <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑨">MLOperandDescriptor</a></code>. The descriptor of the output tensor.</p>
      <li data-md>
-      <p><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"><code>bufferView</code></dfn>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView①⓪">ArrayBufferView</a></code>. The view of the buffer containing the initializing data.</p>
+      <p><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, buffer)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-buffer-buffer"><code>buffer</code></dfn>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#AllowSharedBufferSource" id="ref-for-AllowSharedBufferSource⑥">AllowSharedBufferSource</a></code>. The buffer containing the initializing data.</p>
     </ul>
      <strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑨">MLOperand</a></code>. The constant output tensor. 
    </div>
-   <details class="algorithm" data-algorithm="constant(descriptor, bufferView)" 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-constant"><code>constant(<var>descriptor</var>, <var>bufferView</var>)</code></dfn> method steps are: </summary>
+   <details class="algorithm" data-algorithm="constant(descriptor, buffer)" 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-constant"><code>constant(<var>descriptor</var>, <var>buffer</var>)</code></dfn> method steps are: </summary>
     <ol>
      <li data-md>
       <p>If <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③③">this</a>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-hasbuilt-slot" id="ref-for-dom-mlgraphbuilder-hasbuilt-slot②">[[hasBuilt]]</a></code> is true, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑤">throw</a> an "<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#invalidstateerror" id="ref-for-invalidstateerror①">InvalidStateError</a></code>" <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMException" id="ref-for-idl-DOMException①③">DOMException</a></code>.</p>
      <li data-md>
       <p>If <a data-link-type="dfn" href="#mloperanddescriptor-check-dimensions" id="ref-for-mloperanddescriptor-check-dimensions①">checking dimensions</a> given <var>descriptor</var> returns false, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑥">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror②⑧">TypeError</a></code>.</p>
      <li data-md>
-      <p>If <a data-link-type="dfn" href="#validate-buffer-with-descriptor" id="ref-for-validate-buffer-with-descriptor⑤">validating buffer with descriptor</a> given <var>bufferView</var> and <var>descriptor</var> returns false, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑦">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror②⑨">TypeError</a></code>.</p>
+      <p>If <a data-link-type="dfn" href="#validate-buffer-with-descriptor" id="ref-for-validate-buffer-with-descriptor⑤">validating buffer with descriptor</a> given <var>buffer</var> and <var>descriptor</var> returns false, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑦">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror②⑨">TypeError</a></code>.</p>
      <li data-md>
       <p><em>Make graph connections:</em></p>
       <ol>
        <li data-md>
         <p>Let <var>operand</var> be the result of <a data-link-type="dfn" href="#create-an-mloperand" id="ref-for-create-an-mloperand①">creating an MLOperand</a> given <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③④">this</a> and <var>descriptor</var>.</p>
        <li data-md>
-        <p>Let <var>bytes</var> be the result of <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-get-buffer-source-copy" id="ref-for-dfn-get-buffer-source-copy①">getting a copy of the bytes held by the buffer source</a> given <var>bufferView</var>.</p>
+        <p>Let <var>bytes</var> be the result of <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-get-buffer-source-copy" id="ref-for-dfn-get-buffer-source-copy①">getting a copy of the bytes held by the buffer source</a> given <var>buffer</var>.</p>
        <li data-md>
         <p>Add <var>operand</var> to <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this③⑤">this</a>'s <a data-link-type="dfn" href="#mlgraphbuilder-graph" id="ref-for-mlgraphbuilder-graph②">graph</a>'s <a data-link-type="dfn" href="#computational-graph-constants" id="ref-for-computational-graph-constants">constants</a> with <var>bytes</var> as value.</p>
       </ol>
@@ -2835,7 +2836,6 @@ <h4 class="heading settled" data-level="7.9.4" id="api-mlgraphbuilder-build"><sp
       <ol>
        <li data-md>
         <p>Set <var>graph</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraph-inputdescriptors-slot" id="ref-for-dom-mlgraph-inputdescriptors-slot③">[[inputDescriptors]]</a></code>[<var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-name-slot" id="ref-for-dom-mloperand-name-slot⑤">[[name]]</a></code>] to <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤">[[descriptor]]</a></code>.</p>
-        <p class="issue" id="issue-6f962d4f"><a class="self-link" href="#issue-6f962d4f"></a> If <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant③">constants'</a></code> <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-ArrayBuffer" id="ref-for-idl-ArrayBuffer④">ArrayBuffer</a></code>s are not <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#arraybuffer-transfer" id="ref-for-arraybuffer-transfer①">transferred</a>, make copies for <a data-link-type="dfn" href="#mlgraphbuilder-graph" id="ref-for-mlgraphbuilder-graph⑥">graph</a>'s <a data-link-type="dfn" href="#computational-graph-constants" id="ref-for-computational-graph-constants③">constants</a> here. <a href="https://github.com/webmachinelearning/webnn/issues/566">[Issue #566]</a></p>
       </ol>
      <li data-md>
       <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-iterate" id="ref-for-map-iterate⑧">For each</a> <var>name</var> → <var>operand</var> of <var>outputs</var>:</p>
@@ -2849,7 +2849,7 @@ <h4 class="heading settled" data-level="7.9.4" id="api-mlgraphbuilder-build"><sp
       <p>Run the following steps <a data-link-type="dfn" href="https://html.spec.whatwg.org/multipage/infrastructure.html#in-parallel" id="ref-for-in-parallel③">in parallel</a>:</p>
       <ol>
        <li data-md>
-        <p>Let <var>graphImpl</var> be the result of converting <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑥">this</a>'s <a data-link-type="dfn" href="#mlgraphbuilder-graph" id="ref-for-mlgraphbuilder-graph⑦">graph</a> with <var>operands</var>, <var>operators</var>, <var>inputs</var>, and <var>outputs</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-getting-the-values" id="ref-for-map-getting-the-values③">values</a> into an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④">implementation-defined</a> format which can be interpreted by the underlying platform.</p>
+        <p>Let <var>graphImpl</var> be the result of converting <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#this" id="ref-for-this④⑥">this</a>'s <a data-link-type="dfn" href="#mlgraphbuilder-graph" id="ref-for-mlgraphbuilder-graph⑥">graph</a> with <var>operands</var>, <var>operators</var>, <var>inputs</var>, and <var>outputs</var>’s <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-getting-the-values" id="ref-for-map-getting-the-values③">values</a> into an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④">implementation-defined</a> format which can be interpreted by the underlying platform.</p>
         <ol>
          <li data-md>
           <p>If the underlying platform does not support a requested feature, then <a data-link-type="dfn" href="#queue-an-ml-task" id="ref-for-queue-an-ml-task①②">queue an ML task</a> with <var>global</var> to <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#reject" id="ref-for-reject②⑨">reject</a> <var>promise</var> with an "<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#operationerror" id="ref-for-operationerror④">OperationError</a></code>" <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMException" id="ref-for-idl-DOMException①⑥">DOMException</a></code>, and abort these steps.</p>
@@ -2865,7 +2865,7 @@ <h4 class="heading settled" data-level="7.9.4" id="api-mlgraphbuilder-build"><sp
       <p>Return <var>promise</var>.</p>
     </ol>
    </details>
-   <p class="note" role="note"><span class="marker">NOTE:</span> Specifying an <a data-link-type="dfn" href="#computational-graph-input" id="ref-for-computational-graph-input④">input</a> operand or <a data-link-type="dfn" href="#computational-graph-constants" id="ref-for-computational-graph-constants④">constant</a> operand as a graph <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build-outputs-outputs" id="ref-for-dom-mlgraphbuilder-build-outputs-outputs①">output</a></code> results in an error, as this is usually an incorrect usage of the API. Callers can work around this by introducing an <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-identity" id="ref-for-dom-mlgraphbuilder-identity">identity()</a></code> operator.</p>
+   <p class="note" role="note"><span class="marker">NOTE:</span> Specifying an <a data-link-type="dfn" href="#computational-graph-input" id="ref-for-computational-graph-input④">input</a> operand or <a data-link-type="dfn" href="#computational-graph-constants" id="ref-for-computational-graph-constants③">constant</a> operand as a graph <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build-outputs-outputs" id="ref-for-dom-mlgraphbuilder-build-outputs-outputs①">output</a></code> results in an error, as this is usually an incorrect usage of the API. Callers can work around this by introducing an <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-identity" id="ref-for-dom-mlgraphbuilder-identity">identity()</a></code> operator.</p>
    <h4 class="heading settled" data-level="7.9.5" id="api-mlgraphbuilder-argminmax"><span class="secno">7.9.5. </span><span class="content">argMin/argMax operations</span><a class="self-link" href="#api-mlgraphbuilder-argminmax"></a></h4>
     Return the index location of the minimum or maximum values of all the input values along the axis. In case of ties, the identity of the return value is implementation dependent. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlargminmaxoptions"><code><c- g>MLArgMinMaxOptions</c-></code></dfn> : <a data-link-type="idl-name" href="#dictdef-mloperatoroptions" id="ref-for-dictdef-mloperatoroptions①"><c- n>MLOperatorOptions</c-></a> {
@@ -3398,8 +3398,7 @@ <h4 class="heading settled" data-level="7.9.8" id="api-mlgraphbuilder-clamp"><sp
         input<c- p>,</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> options<c- p>.</c->minValue<c- p>));</c->
     <c- p>}</c-> <c- k>else</c-> <c- p>{</c->
       <c- k>return</c-> builder<c- p>.</c->min<c- p>(</c->
-        builder<c- p>.</c->max<c- p>(</c->
-          input<c- p>,</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> options<c- p>.</c->minValue<c- p>)),</c->
+        builder<c- p>.</c->max<c- p>(</c->input<c- p>,</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> options<c- p>.</c->minValue<c- p>)),</c->
         builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> options<c- p>.</c->maxValue<c- p>));</c->
     <c- p>}</c->
   <c- p>}</c->
@@ -5272,8 +5271,8 @@ <h4 class="heading settled" data-level="7.9.17" id="api-mlgraphbuilder-gather"><
       <p>Return <var>output</var>.</p>
     </ol>
    </details>
-   <div class="example" id="example-f859eef7">
-    <a class="self-link" href="#example-f859eef7"></a> 
+   <div class="example" id="example-4cef02ec">
+    <a class="self-link" href="#example-4cef02ec"></a> 
     <details open>
      <summary> Examples of how gather works in different slicing schemes. </summary>
 <pre class="highlight"><c- c1>// input of shape [4,3]:</c->
@@ -5285,8 +5284,8 @@ <h4 class="heading settled" data-level="7.9.17" id="api-mlgraphbuilder-gather"><
   <c- p>{</c->shape<c- o>:</c-> <c- p>[</c-><c- mf>4</c-><c- p>,</c-> <c- mf>3</c-><c- p>]},</c->
   <c- ow>new</c-> Float32Array<c- p>([</c-><c- mf>0</c-><c- p>,</c-> <c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>,</c-> <c- mf>10</c-><c- p>,</c-> <c- mf>11</c-><c- p>,</c-> <c- mf>12</c-><c- p>,</c-> <c- mf>20</c-><c- p>,</c-> <c- mf>21</c-><c- p>,</c-> <c- mf>22</c-><c- p>,</c-> <c- mf>30</c-><c- p>,</c-> <c- mf>31</c-><c- p>,</c-> <c- mf>32</c-><c- p>]));</c->
 
-<c- a>const</c-> indices1 <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->
-  <c- p>{</c->dataType<c- o>:</c-> <c- t>'uint32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c-><c- mf>2</c-><c- p>]},</c-> <c- ow>new</c-> Uint32Array<c- p>([</c-><c- mf>3</c-><c- p>,</c-> <c- mf>1</c-><c- p>]));</c->
+<c- a>const</c-> indices1 <c- o>=</c->
+  builder<c- p>.</c->constant<c- p>({</c->dataType<c- o>:</c-> <c- t>'uint32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c-><c- mf>2</c-><c- p>]},</c-> <c- ow>new</c-> Uint32Array<c- p>([</c-><c- mf>3</c-><c- p>,</c-> <c- mf>1</c-><c- p>]));</c->
 
 <c- a>const</c-> indices2 <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->
   <c- p>{</c->dataType<c- o>:</c-> <c- t>'uint32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c-><c- mf>3</c-><c- p>]},</c-> <c- ow>new</c-> Uint32Array<c- p>([</c-><c- mf>2</c-><c- p>,</c-> <c- mf>1</c-><c- p>,</c-> <c- mf>1</c-><c- p>]));</c->
@@ -5893,10 +5892,7 @@ <h4 class="heading settled" data-level="7.9.20" id="api-mlgraphbuilder-gru"><spa
   <c- a>let</c-> hiddenState <c- o>=</c-> options<c- p>.</c->initialHiddenState<c- p>;</c->
 
   <c- k>if</c-> <c- p>(</c-><c- o>!</c->hiddenState<c- p>)</c-> <c- p>{</c->
-    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->
-      dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c->
-      shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]</c->
-    <c- p>};</c->
+    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]};</c->
     <c- a>const</c-> totalSize <c- o>=</c-> numDirections <c- o>*</c-> hiddenSize<c- p>;</c->
     hiddenState <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->desc<c- p>,</c-> <c- ow>new</c-> Float32Array<c- p>(</c->totalSize<c- p>).</c->fill<c- p>(</c-><c- mf>0</c-><c- p>));</c->
   <c- p>}</c->
@@ -6654,8 +6650,7 @@ <h4 class="heading settled" data-level="7.9.24" id="api-mlgraphbuilder-instancen
   <c- a>const</c-> reduceOptions <c- o>=</c-> <c- p>{</c->axes<c- o>:</c-> <c- p>[</c-><c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>],</c-> keepDimensions<c- o>:</c-> <c- kc>true</c-><c- p>};</c->
   <c- a>const</c-> mean <c- o>=</c-> builder<c- p>.</c->reduceMean<c- p>(</c->input<c- p>,</c-> reduceOptions<c- p>);</c->
   <c- a>const</c-> variance <c- o>=</c-> builder<c- p>.</c->reduceMean<c- p>(</c->
-    builder<c- p>.</c->pow<c- p>(</c->
-      builder<c- p>.</c->sub<c- p>(</c->input<c- p>,</c-> mean<c- p>),</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> <c- mf>2</c-><c- p>)),</c->
+    builder<c- p>.</c->pow<c- p>(</c->builder<c- p>.</c->sub<c- p>(</c->input<c- p>,</c-> mean<c- p>),</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> <c- mf>2</c-><c- p>)),</c->
     reduceOptions<c- p>);</c->
 
   <c- c1>// The scale and bias values are applied per input feature</c->
@@ -6831,8 +6826,7 @@ <h4 class="heading settled" data-level="7.9.25" id="api-mlgraphbuilder-layernorm
   <c- a>const</c-> reduceOptions <c- o>=</c-> <c- p>{</c->axes<c- o>:</c-> <c- p>[</c-><c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>],</c-> keepDimensions<c- o>:</c-> <c- kc>true</c-><c- p>};</c->
   <c- a>const</c-> mean <c- o>=</c-> builder<c- p>.</c->reduceMean<c- p>(</c->input<c- p>,</c-> reduceOptions<c- p>);</c->
   <c- a>const</c-> variance <c- o>=</c-> builder<c- p>.</c->reduceMean<c- p>(</c->
-    builder<c- p>.</c->pow<c- p>(</c->
-      builder<c- p>.</c->sub<c- p>(</c->input<c- p>,</c-> mean<c- p>),</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> <c- mf>2</c-><c- p>)),</c->
+    builder<c- p>.</c->pow<c- p>(</c->builder<c- p>.</c->sub<c- p>(</c->input<c- p>,</c-> mean<c- p>),</c-> builder<c- p>.</c->constant<c- p>(</c->input<c- p>.</c->dataType<c- p>,</c-> <c- mf>2</c-><c- p>)),</c->
     reduceOptions<c- p>);</c->
 
   <c- c1>// The scale and bias tensors are of the shape of the input</c->
@@ -7387,19 +7381,13 @@ <h4 class="heading settled" data-level="7.9.28" id="api-mlgraphbuilder-lstm"><sp
   <c- a>let</c-> cellState <c- o>=</c-> options<c- p>.</c->initialCellState<c- p>;</c->
 
   <c- k>if</c-> <c- p>(</c-><c- o>!</c->hiddenState<c- p>)</c-> <c- p>{</c->
-    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->
-      dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c->
-      shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]</c->
-    <c- p>};</c->
+    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]};</c->
     <c- a>const</c-> totalSize <c- o>=</c-> numDirections <c- o>*</c-> hiddenSize<c- p>;</c->
     hiddenState <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->desc<c- p>,</c-> <c- ow>new</c-> Float32Array<c- p>(</c->totalSize<c- p>).</c->fill<c- p>(</c-><c- mf>0</c-><c- p>));</c->
   <c- p>}</c->
 
   <c- k>if</c-> <c- p>(</c-><c- o>!</c->cellState<c- p>)</c-> <c- p>{</c->
-    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->
-      dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c->
-      shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]</c->
-    <c- p>};</c->
+    <c- a>const</c-> desc <c- o>=</c-> <c- p>{</c->dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c->numDirections<c- p>,</c-> <c- mf>1</c-><c- p>,</c-> hiddenSize<c- p>]};</c->
     <c- a>const</c-> totalSize <c- o>=</c-> numDirections <c- o>*</c-> hiddenSize<c- p>;</c->
     cellState <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->desc<c- p>,</c-> <c- ow>new</c-> Float32Array<c- p>(</c->totalSize<c- p>).</c->fill<c- p>(</c-><c- mf>0</c-><c- p>));</c->
   <c- p>}</c->
@@ -8151,8 +8139,7 @@ <h4 class="heading settled" data-level="7.9.31" id="api-mlgraphbuilder-pad"><spa
      <summary> Examples for constant, edge, reflection and symmetric padding: </summary>
 <pre class="highlight"><c- c1>// input: [[1,2,3], [4,5,6]]</c->
 <c- a>const</c-> input <c- o>=</c-> builder<c- p>.</c->constant<c- p>(</c->
-  <c- p>{</c->dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c-><c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>]},</c->
-  <c- ow>new</c-> Float32Array<c- p>([</c-><c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>,</c-> <c- mf>4</c-><c- p>,</c-> <c- mf>5</c-><c- p>,</c-> <c- mf>6</c-><c- p>]));</c->
+  <c- p>{</c->dataType<c- o>:</c-> <c- t>'float32'</c-><c- p>,</c-> shape<c- o>:</c-> <c- p>[</c-><c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>]},</c-> <c- ow>new</c-> Float32Array<c- p>([</c-><c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>,</c-> <c- mf>3</c-><c- p>,</c-> <c- mf>4</c-><c- p>,</c-> <c- mf>5</c-><c- p>,</c-> <c- mf>6</c-><c- p>]));</c->
 
 <c- a>const</c-> beginningPadding <c- o>=</c-> <c- p>[</c-><c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>];</c->
 <c- a>const</c-> endingPadding <c- o>=</c-> <c- p>[</c-><c- mf>1</c-><c- p>,</c-> <c- mf>2</c-><c- p>];</c->
@@ -10661,12 +10648,12 @@ <h3 class="heading settled" data-level="10.3" id="emulation-flatten"><span class
     </details>
    </div>
    <h2 class="heading settled" data-level="11" id="appendices"><span class="secno">11. </span><span class="content">Appendices</span><a class="self-link" href="#appendices"></a></h2>
-   <h3 class="heading settled" data-level="11.1" id="appendices-mloperanddatatype-arraybufferview-compatibility"><span class="secno">11.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype②③">MLOperandDataType</a></code> and <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView①①">ArrayBufferView</a></code> compatibility</span><a class="self-link" href="#appendices-mloperanddatatype-arraybufferview-compatibility"></a></h3>
+   <h3 class="heading settled" data-level="11.1" id="appendices-mloperanddatatype-arraybufferview-compatibility"><span class="secno">11.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype②③">MLOperandDataType</a></code> and <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView⑨">ArrayBufferView</a></code> compatibility</span><a class="self-link" href="#appendices-mloperanddatatype-arraybufferview-compatibility"></a></h3>
    <table class="data">
     <thead class="stickyheader">
      <tr>
       <th><code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype②④">MLOperandDataType</a></code>
-      <th><code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView①②">ArrayBufferView</a></code>
+      <th><code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#ArrayBufferView" id="ref-for-ArrayBufferView①⓪">ArrayBufferView</a></code>
     <tbody>
      <tr>
       <td><code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-float32" id="ref-for-dom-mloperanddatatype-float32⑤②">float32</a></code>
@@ -10905,7 +10892,7 @@ <h3 class="no-num no-ref heading settled" id="index-defined-here"><span class="c
    <li><a href="#dom-mlwheresupportlimits-condition">condition</a><span>, in § 7.9.47</span>
    <li><a href="#dom-mlpaddingmode-constant">"constant"</a><span>, in § 7.9.31</span>
    <li><a href="#dom-mlopsupportlimits-constant">constant</a><span>, in § 7.3.8.1</span>
-   <li><a href="#dom-mlgraphbuilder-constant">constant(descriptor, bufferView)</a><span>, in § 7.9.3.1</span>
+   <li><a href="#dom-mlgraphbuilder-constant">constant(descriptor, buffer)</a><span>, in § 7.9.3.1</span>
    <li><a href="#computational-graph-constants">constants</a><span>, in § 6.1</span>
    <li><a href="#dom-mlgraphbuilder-constant-type-value">constant(type, value)</a><span>, in § 7.9.3.2</span>
    <li><a href="#dom-mlgraphbuilder-mlgraphbuilder">constructor(context)</a><span>, in § 7.9.1</span>
@@ -11949,7 +11936,8 @@ <h2 class="no-num no-ref heading settled" id="idl-index"><span class="content">I
   <a data-link-type="idl-name" href="#mloperand"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-input"><c- g>input</c-></a>(<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-USVString"><c- b>USVString</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-input-name-descriptor-name"><c- g>name</c-></a>, <a data-link-type="idl-name" href="#dictdef-mloperanddescriptor"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-input-name-descriptor-descriptor"><c- g>descriptor</c-></a>);
 
   // Create an operand for a graph constant.
-  <a data-link-type="idl-name" href="#mloperand"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"><c- g>descriptor</c-></a>, <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#ArrayBufferView"><c- n>ArrayBufferView</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"><c- g>bufferView</c-></a>);
+  <a data-link-type="idl-name" href="#mloperand"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor"><c- n>MLOperandDescriptor</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-buffer-descriptor"><c- g>descriptor</c-></a>,
+                     <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#AllowSharedBufferSource"><c- n>AllowSharedBufferSource</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-descriptor-buffer-buffer"><c- g>buffer</c-></a>);
 
   // Create a scalar operand from the specified number of the specified type.
   <a data-link-type="idl-name" href="#mloperand"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant-type-value"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#enumdef-mloperanddatatype"><c- n>MLOperandDataType</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-type-value-type"><c- g>type</c-></a>, <a data-link-type="idl-name" href="#typedefdef-mlnumber"><c- n>MLNumber</c-></a> <a class="idl-code" data-link-type="argument" href="#dom-mlgraphbuilder-constant-type-value-value"><c- g>value</c-></a>);
@@ -12775,7 +12763,6 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
    <div class="issue"> Should 0-size dimensions be supported? <a href="https://github.com/webmachinelearning/webnn/issues/391">[Issue #391]</a> <a class="issue-return" href="#issue-f000a729" title="Jump to section">↵</a></div>
    <div class="issue"> The maximum number of operand dimensions is not defined, but native ML APIs usually have a maximum supported size. <a href="https://github.com/webmachinelearning/webnn/issues/456">[Issue #456]</a> <a class="issue-return" href="#issue-8054fa8e" title="Jump to section">↵</a></div>
    <div class="issue"> Support for unions of <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-bigint">bigint</a></code> and <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-numeric-type">numeric types</a> is new in <a data-link-type="biblio" href="#biblio-webidl" title="Web IDL Standard">[WEBIDL]</a>, and implementation support is also limited. Prototype implementations are encouraged to provide feedback for this approach. <a href="https://github.com/whatwg/webidl/issues/1388">[Issue #whatwg/webidl#1388]</a> <a class="issue-return" href="#issue-63bb2306" title="Jump to section">↵</a></div>
-   <div class="issue"> If <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant">constants'</a></code> <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-ArrayBuffer">ArrayBuffer</a></code>s are not <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#arraybuffer-transfer">transferred</a>, make copies for <a data-link-type="dfn" href="#mlgraphbuilder-graph">graph</a>'s <a data-link-type="dfn" href="#computational-graph-constants">constants</a> here. <a href="https://github.com/webmachinelearning/webnn/issues/566">[Issue #566]</a> <a class="issue-return" href="#issue-6f962d4f" title="Jump to section">↵</a></div>
    <div class="issue"> If 0-size dimensions are allowed, revise these steps. <a href="https://github.com/webmachinelearning/webnn/issues/391">[Issue #391]</a> <a class="issue-return" href="#issue-b8ab142b" title="Jump to section">↵</a></div>
    <div class="issue"> If 0-size dimensions are allowed, revise the above step. <a href="https://github.com/webmachinelearning/webnn/issues/391">[Issue #391]</a> <a class="issue-return" href="#issue-578f9ca8" title="Jump to section">↵</a></div>
   </div>
@@ -12997,7 +12984,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
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MLGraphBuilder interface"},{"refs":[{"id":"ref-for-mloperand\u2463\u2463"},{"id":"ref-for-mloperand\u2463\u2464"},{"id":"ref-for-mloperand\u2463\u2465"}],"title":"7.9.2. input operands"},{"refs":[{"id":"ref-for-mloperand\u2463\u2466"}],"title":"7.9.3. constant operands"},{"refs":[{"id":"ref-for-mloperand\u2463\u2467"},{"id":"ref-for-mloperand\u2463\u2468"}],"title":"7.9.3.1. constant(descriptor, bufferView)"},{"refs":[{"id":"ref-for-mloperand\u2464\u24ea"},{"id":"ref-for-mloperand\u2464\u2460"}],"title":"7.9.3.2. constant(type, value)"},{"refs":[{"id":"ref-for-mloperand\u2464\u2461"}],"title":"7.9.4. build method"},{"refs":[{"id":"ref-for-mloperand\u2464\u2462"},{"id":"ref-for-mloperand\u2464\u2463"},{"id":"ref-for-mloperand\u2464\u2464"},{"id":"ref-for-mloperand\u2464\u2465"},{"id":"ref-for-mloperand\u2464\u2466"},{"id":"ref-for-mloperand\u2464\u2467"},{"id":"ref-for-mloperand\u2464\u2468"}],"title":"7.9.5. argMin/argMax operations"},{"refs":[{"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"},{"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":"7.9.6. batchNormalization"},{"refs":[{"id":"ref-for-mloperand\u2466\u2461"},{"id":"ref-for-mloperand\u2466\u2462"},{"id":"ref-for-mloperand\u2466\u2463"},{"id":"ref-for-mloperand\u2466\u2464"}],"title":"7.9.7. cast"},{"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":"7.9.8. clamp"},{"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"}],"title":"7.9.9. concat"},{"refs":[{"id":"ref-for-mloperand\u2467\u2463"},{"id":"ref-for-mloperand\u2467\u2464"},{"id":"ref-for-mloperand\u2467\u2465"},{"id":"ref-for-mloperand\u2467\u2466"},{"id":"ref-for-mloperand\u2467\u2467"},{"id":"ref-for-mloperand\u2467\u2468"},{"id":"ref-for-mloperand\u2468\u24ea"},{"id":"ref-for-mloperand\u2468\u2460"}],"title":"7.9.10. conv2d"},{"refs":[{"id":"ref-for-mloperand\u2468\u2461"},{"id":"ref-for-mloperand\u2468\u2462"},{"id":"ref-for-mloperand\u2468\u2463"},{"id":"ref-for-mloperand\u2468\u2464"},{"id":"ref-for-mloperand\u2468\u2465"},{"id":"ref-for-mloperand\u2468\u2466"},{"id":"ref-for-mloperand\u2468\u2467"},{"id":"ref-for-mloperand\u2468\u2468"}],"title":"7.9.11. convTranspose2d"},{"refs":[{"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"},{"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"}],"title":"7.9.12. Element-wise binary operations"},{"refs":[{"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"},{"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"}],"title":"7.9.13. Element-wise logical operations"},{"refs":[{"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"},{"id":"ref-for-mloperand\u2460\u2466\u2462"},{"id":"ref-for-mloperand\u2460\u2466\u2463"},{"id":"ref-for-mloperand\u2460\u2466\u2464"},{"id":"ref-for-mloperand\u2460\u2466\u2465"}],"title":"7.9.14. Element-wise unary operations"},{"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":"7.9.15. elu"},{"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"}],"title":"7.9.16. expand"},{"refs":[{"id":"ref-for-mloperand\u2460\u2467\u2464"},{"id":"ref-for-mloperand\u2460\u2467\u2465"},{"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"}],"title":"7.9.17. gather"},{"refs":[{"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":"7.9.18. gelu"},{"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"}],"title":"7.9.19. gemm"},{"refs":[{"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"},{"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"}],"title":"7.9.20. gru"},{"refs":[{"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"},{"id":"ref-for-mloperand\u2461\u2461\u2463"},{"id":"ref-for-mloperand\u2461\u2461\u2464"},{"id":"ref-for-mloperand\u2461\u2461\u2465"},{"id":"ref-for-mloperand\u2461\u2461\u2466"},{"id":"ref-for-mloperand\u2461\u2461\u2467"},{"id":"ref-for-mloperand\u2461\u2461\u2468"},{"id":"ref-for-mloperand\u2461\u2462\u24ea"}],"title":"7.9.21. gruCell"},{"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"}],"title":"7.9.22. hardSigmoid"},{"refs":[{"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":"7.9.23. hardSwish"},{"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":"7.9.24. instanceNormalization"},{"refs":[{"id":"ref-for-mloperand\u2461\u2463\u2466"},{"id":"ref-for-mloperand\u2461\u2463\u2467"},{"id":"ref-for-mloperand\u2461\u2463\u2468"},{"id":"ref-for-mloperand\u2461\u2464\u24ea"},{"id":"ref-for-mloperand\u2461\u2464\u2460"},{"id":"ref-for-mloperand\u2461\u2464\u2461"},{"id":"ref-for-mloperand\u2461\u2464\u2462"},{"id":"ref-for-mloperand\u2461\u2464\u2463"}],"title":"7.9.25. layerNormalization"},{"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"}],"title":"7.9.26. leakyRelu"},{"refs":[{"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"}],"title":"7.9.27. linear"},{"refs":[{"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"},{"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"}],"title":"7.9.28. lstm"},{"refs":[{"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"},{"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"},{"id":"ref-for-mloperand\u2461\u2468\u2467"}],"title":"7.9.29. lstmCell"},{"refs":[{"id":"ref-for-mloperand\u2461\u2468\u2468"},{"id":"ref-for-mloperand\u2462\u24ea\u24ea"},{"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"}],"title":"7.9.30. matmul"},{"refs":[{"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":"7.9.31. pad"},{"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"},{"id":"ref-for-mloperand\u2462\u2460\u2466"},{"id":"ref-for-mloperand\u2462\u2460\u2467"},{"id":"ref-for-mloperand\u2462\u2460\u2468"}],"title":"7.9.32. Pooling operations"},{"refs":[{"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"}],"title":"7.9.33. prelu"},{"refs":[{"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"},{"id":"ref-for-mloperand\u2462\u2463\u24ea"},{"id":"ref-for-mloperand\u2462\u2463\u2460"},{"id":"ref-for-mloperand\u2462\u2463\u2461"},{"id":"ref-for-mloperand\u2462\u2463\u2462"},{"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":"7.9.34. Reduction operations"},{"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":"7.9.35. relu"},{"refs":[{"id":"ref-for-mloperand\u2462\u2464\u2462"},{"id":"ref-for-mloperand\u2462\u2464\u2463"},{"id":"ref-for-mloperand\u2462\u2464\u2464"},{"id":"ref-for-mloperand\u2462\u2464\u2465"},{"id":"ref-for-mloperand\u2462\u2464\u2466"}],"title":"7.9.36. resample2d"},{"refs":[{"id":"ref-for-mloperand\u2462\u2464\u2467"},{"id":"ref-for-mloperand\u2462\u2464\u2468"},{"id":"ref-for-mloperand\u2462\u2465\u24ea"},{"id":"ref-for-mloperand\u2462\u2465\u2460"}],"title":"7.9.37. reshape"},{"refs":[{"id":"ref-for-mloperand\u2462\u2465\u2461"},{"id":"ref-for-mloperand\u2462\u2465\u2462"},{"id":"ref-for-mloperand\u2462\u2465\u2463"},{"id":"ref-for-mloperand\u2462\u2465\u2464"}],"title":"7.9.38. sigmoid"},{"refs":[{"id":"ref-for-mloperand\u2462\u2465\u2465"},{"id":"ref-for-mloperand\u2462\u2465\u2466"},{"id":"ref-for-mloperand\u2462\u2465\u2467"},{"id":"ref-for-mloperand\u2462\u2465\u2468"}],"title":"7.9.39. slice"},{"refs":[{"id":"ref-for-mloperand\u2462\u2466\u24ea"},{"id":"ref-for-mloperand\u2462\u2466\u2460"},{"id":"ref-for-mloperand\u2462\u2466\u2461"},{"id":"ref-for-mloperand\u2462\u2466\u2462"}],"title":"7.9.40. softmax"},{"refs":[{"id":"ref-for-mloperand\u2462\u2466\u2463"},{"id":"ref-for-mloperand\u2462\u2466\u2464"},{"id":"ref-for-mloperand\u2462\u2466\u2465"},{"id":"ref-for-mloperand\u2462\u2466\u2466"}],"title":"7.9.41. softplus"},{"refs":[{"id":"ref-for-mloperand\u2462\u2466\u2467"},{"id":"ref-for-mloperand\u2462\u2466\u2468"},{"id":"ref-for-mloperand\u2462\u2467\u24ea"},{"id":"ref-for-mloperand\u2462\u2467\u2460"}],"title":"7.9.42. softsign"},{"refs":[{"id":"ref-for-mloperand\u2462\u2467\u2461"},{"id":"ref-for-mloperand\u2462\u2467\u2462"},{"id":"ref-for-mloperand\u2462\u2467\u2463"},{"id":"ref-for-mloperand\u2462\u2467\u2464"}],"title":"7.9.43. split"},{"refs":[{"id":"ref-for-mloperand\u2462\u2467\u2465"},{"id":"ref-for-mloperand\u2462\u2467\u2466"},{"id":"ref-for-mloperand\u2462\u2467\u2467"},{"id":"ref-for-mloperand\u2462\u2467\u2468"}],"title":"7.9.44. tanh"},{"refs":[{"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"}],"title":"7.9.45. transpose"},{"refs":[{"id":"ref-for-mloperand\u2462\u2468\u2463"},{"id":"ref-for-mloperand\u2462\u2468\u2464"},{"id":"ref-for-mloperand\u2462\u2468\u2465"},{"id":"ref-for-mloperand\u2462\u2468\u2466"}],"title":"7.9.46. triangular"},{"refs":[{"id":"ref-for-mloperand\u2462\u2468\u2467"},{"id":"ref-for-mloperand\u2462\u2468\u2468"},{"id":"ref-for-mloperand\u2463\u24ea\u24ea"},{"id":"ref-for-mloperand\u2463\u24ea\u2460"},{"id":"ref-for-mloperand\u2463\u24ea\u2461"},{"id":"ref-for-mloperand\u2463\u24ea\u2462"},{"id":"ref-for-mloperand\u2463\u24ea\u2463"},{"id":"ref-for-mloperand\u2463\u24ea\u2464"}],"title":"7.9.47. where"},{"refs":[{"id":"ref-for-mloperand\u2463\u24ea\u2465"}],"title":"8.2. Casting"}],"url":"#mloperand"},
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MLGraphBuilder interface"},{"refs":[{"id":"ref-for-mloperand\u2463\u2463"},{"id":"ref-for-mloperand\u2463\u2464"},{"id":"ref-for-mloperand\u2463\u2465"}],"title":"7.9.2. input operands"},{"refs":[{"id":"ref-for-mloperand\u2463\u2466"}],"title":"7.9.3. constant operands"},{"refs":[{"id":"ref-for-mloperand\u2463\u2467"},{"id":"ref-for-mloperand\u2463\u2468"}],"title":"7.9.3.1. constant(descriptor, buffer)"},{"refs":[{"id":"ref-for-mloperand\u2464\u24ea"},{"id":"ref-for-mloperand\u2464\u2460"}],"title":"7.9.3.2. constant(type, value)"},{"refs":[{"id":"ref-for-mloperand\u2464\u2461"}],"title":"7.9.4. build method"},{"refs":[{"id":"ref-for-mloperand\u2464\u2462"},{"id":"ref-for-mloperand\u2464\u2463"},{"id":"ref-for-mloperand\u2464\u2464"},{"id":"ref-for-mloperand\u2464\u2465"},{"id":"ref-for-mloperand\u2464\u2466"},{"id":"ref-for-mloperand\u2464\u2467"},{"id":"ref-for-mloperand\u2464\u2468"}],"title":"7.9.5. argMin/argMax operations"},{"refs":[{"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"},{"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":"7.9.6. batchNormalization"},{"refs":[{"id":"ref-for-mloperand\u2466\u2461"},{"id":"ref-for-mloperand\u2466\u2462"},{"id":"ref-for-mloperand\u2466\u2463"},{"id":"ref-for-mloperand\u2466\u2464"}],"title":"7.9.7. cast"},{"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":"7.9.8. clamp"},{"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"}],"title":"7.9.9. concat"},{"refs":[{"id":"ref-for-mloperand\u2467\u2463"},{"id":"ref-for-mloperand\u2467\u2464"},{"id":"ref-for-mloperand\u2467\u2465"},{"id":"ref-for-mloperand\u2467\u2466"},{"id":"ref-for-mloperand\u2467\u2467"},{"id":"ref-for-mloperand\u2467\u2468"},{"id":"ref-for-mloperand\u2468\u24ea"},{"id":"ref-for-mloperand\u2468\u2460"}],"title":"7.9.10. conv2d"},{"refs":[{"id":"ref-for-mloperand\u2468\u2461"},{"id":"ref-for-mloperand\u2468\u2462"},{"id":"ref-for-mloperand\u2468\u2463"},{"id":"ref-for-mloperand\u2468\u2464"},{"id":"ref-for-mloperand\u2468\u2465"},{"id":"ref-for-mloperand\u2468\u2466"},{"id":"ref-for-mloperand\u2468\u2467"},{"id":"ref-for-mloperand\u2468\u2468"}],"title":"7.9.11. convTranspose2d"},{"refs":[{"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"},{"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"}],"title":"7.9.12. 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Element-wise unary operations"},{"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":"7.9.15. elu"},{"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"}],"title":"7.9.16. expand"},{"refs":[{"id":"ref-for-mloperand\u2460\u2467\u2464"},{"id":"ref-for-mloperand\u2460\u2467\u2465"},{"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"}],"title":"7.9.17. gather"},{"refs":[{"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":"7.9.18. gelu"},{"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"}],"title":"7.9.19. gemm"},{"refs":[{"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"},{"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"}],"title":"7.9.20. gru"},{"refs":[{"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"},{"id":"ref-for-mloperand\u2461\u2461\u2463"},{"id":"ref-for-mloperand\u2461\u2461\u2464"},{"id":"ref-for-mloperand\u2461\u2461\u2465"},{"id":"ref-for-mloperand\u2461\u2461\u2466"},{"id":"ref-for-mloperand\u2461\u2461\u2467"},{"id":"ref-for-mloperand\u2461\u2461\u2468"},{"id":"ref-for-mloperand\u2461\u2462\u24ea"}],"title":"7.9.21. gruCell"},{"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"}],"title":"7.9.22. hardSigmoid"},{"refs":[{"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":"7.9.23. hardSwish"},{"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":"7.9.24. instanceNormalization"},{"refs":[{"id":"ref-for-mloperand\u2461\u2463\u2466"},{"id":"ref-for-mloperand\u2461\u2463\u2467"},{"id":"ref-for-mloperand\u2461\u2463\u2468"},{"id":"ref-for-mloperand\u2461\u2464\u24ea"},{"id":"ref-for-mloperand\u2461\u2464\u2460"},{"id":"ref-for-mloperand\u2461\u2464\u2461"},{"id":"ref-for-mloperand\u2461\u2464\u2462"},{"id":"ref-for-mloperand\u2461\u2464\u2463"}],"title":"7.9.25. layerNormalization"},{"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"}],"title":"7.9.26. leakyRelu"},{"refs":[{"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"}],"title":"7.9.27. linear"},{"refs":[{"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"},{"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"}],"title":"7.9.28. lstm"},{"refs":[{"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"},{"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"},{"id":"ref-for-mloperand\u2461\u2468\u2467"}],"title":"7.9.29. 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pad"},{"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"},{"id":"ref-for-mloperand\u2462\u2460\u2466"},{"id":"ref-for-mloperand\u2462\u2460\u2467"},{"id":"ref-for-mloperand\u2462\u2460\u2468"}],"title":"7.9.32. Pooling operations"},{"refs":[{"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"}],"title":"7.9.33. prelu"},{"refs":[{"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"},{"id":"ref-for-mloperand\u2462\u2463\u24ea"},{"id":"ref-for-mloperand\u2462\u2463\u2460"},{"id":"ref-for-mloperand\u2462\u2463\u2461"},{"id":"ref-for-mloperand\u2462\u2463\u2462"},{"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":"7.9.34. 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Casting"}],"url":"#mloperand"},
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@@ -14369,9 +14356,9 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 "#dom-mlgraphbuilder-concat-inputs-axis-options-axis": {"displayText":"axis","export":true,"for_":["MLGraphBuilder/concat(inputs, axis, options)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"axis","type":"argument","url":"#dom-mlgraphbuilder-concat-inputs-axis-options-axis"},
 "#dom-mlgraphbuilder-concat-inputs-axis-options-inputs": {"displayText":"inputs","export":true,"for_":["MLGraphBuilder/concat(inputs, axis, options)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"inputs","type":"argument","url":"#dom-mlgraphbuilder-concat-inputs-axis-options-inputs"},
 "#dom-mlgraphbuilder-concat-inputs-axis-options-options": {"displayText":"options","export":true,"for_":["MLGraphBuilder/concat(inputs, axis, options)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"options","type":"argument","url":"#dom-mlgraphbuilder-concat-inputs-axis-options-options"},
-"#dom-mlgraphbuilder-constant": {"displayText":"constant(descriptor, bufferView)","export":true,"for_":["MLGraphBuilder"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"constant(descriptor, bufferView)","type":"method","url":"#dom-mlgraphbuilder-constant"},
-"#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview": {"displayText":"bufferView","export":true,"for_":["MLGraphBuilder/constant(descriptor, bufferView)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"bufferView","type":"argument","url":"#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"},
-"#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor": {"displayText":"descriptor","export":true,"for_":["MLGraphBuilder/constant(descriptor, bufferView)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"descriptor","type":"argument","url":"#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"},
+"#dom-mlgraphbuilder-constant": {"displayText":"constant(descriptor, buffer)","export":true,"for_":["MLGraphBuilder"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"constant(descriptor, buffer)","type":"method","url":"#dom-mlgraphbuilder-constant"},
+"#dom-mlgraphbuilder-constant-descriptor-buffer-buffer": {"displayText":"buffer","export":true,"for_":["MLGraphBuilder/constant(descriptor, buffer)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"buffer","type":"argument","url":"#dom-mlgraphbuilder-constant-descriptor-buffer-buffer"},
+"#dom-mlgraphbuilder-constant-descriptor-buffer-descriptor": {"displayText":"descriptor","export":true,"for_":["MLGraphBuilder/constant(descriptor, buffer)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"descriptor","type":"argument","url":"#dom-mlgraphbuilder-constant-descriptor-buffer-descriptor"},
 "#dom-mlgraphbuilder-constant-type-value": {"displayText":"constant(type, value)","export":true,"for_":["MLGraphBuilder"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"constant(type, value)","type":"method","url":"#dom-mlgraphbuilder-constant-type-value"},
 "#dom-mlgraphbuilder-constant-type-value-type": {"displayText":"type","export":true,"for_":["MLGraphBuilder/constant(type, value)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"type","type":"argument","url":"#dom-mlgraphbuilder-constant-type-value-type"},
 "#dom-mlgraphbuilder-constant-type-value-value": {"displayText":"value","export":true,"for_":["MLGraphBuilder/constant(type, value)"],"level":"","normative":true,"shortname":"webnn","spec":"webnn","status":"local","text":"value","type":"argument","url":"#dom-mlgraphbuilder-constant-type-value-value"},