diff --git a/index.html b/index.html
index 07a34861..be54daf5 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 82ce88815, updated Thu Sep 7 16:33:55 2023 -0700" name="generator">
   <link href="https://www.w3.org/TR/webnn/" rel="canonical">
-  <meta content="479ce17ca89b5d3b6f602269a19e7a846699fc74" name="document-revision">
+  <meta content="7ec2e1877ed9163cd6b23262b5ecd1af51d97559" name="document-revision">
 <style>
 /* Make <dl> blocks more distinct from their surroundings. */
 main dl:not(.switch) {
@@ -965,7 +965,7 @@
   <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="2024-01-27">27 January 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="2024-02-03">3 February 2024</time></p>
    <details open>
     <summary>More details about this document</summary>
     <div data-fill-with="spec-metadata">
@@ -1560,18 +1560,19 @@ <h3 class="heading settled" data-level="6.1" id="programming-model-overview"><sp
 semantics, with no side effects.
 Each operation invocation conceptually returns a distinct new value, without
 changing the value of any other <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③">MLOperand</a></code>.</p>
-   <p>The runtime values (of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④">MLOperand</a></code>s) are tensors, which are essentially multidimensional
+   <p>Internally, the <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥">MLGraphBuilder</a></code> methods such as <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-gemm" id="ref-for-dom-mlgraphbuilder-gemm①">gemm()</a></code> create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined">implementation-defined</a> <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="platform-operator">platform operator</dfn> which is held by the <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④">MLOperand</a></code> or <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation">MLActivation</a></code>, which performs the actual operation on the input data when the computation is run. An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤">MLOperand</a></code> also holds an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①">implementation-defined</a> <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="platform-operand">platform operand</dfn>, which references the operand in the underlying computational graph, and is connected to <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator">platform operators</a> as input and/or output.</p>
+   <p>The runtime values (of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥">MLOperand</a></code>s) are tensors, which are essentially multidimensional
 arrays. The representation of the tensors is implementation dependent, but it typically
 includes the array data stored in some buffer (memory) and some metadata describing the
 array data (such as its shape).</p>
-   <p>As mentioned above, the operations have a functional semantics. This allows the implementation
+   <p>As mentioned above, the operations have functional semantics. This allows the implementation
 to potentially share the array data between multiple tensors. For example, the implementation
 of operations such as reshape, or slice may return a view of its input tensor
 that shares the same buffer as the input tensor. (In the case of reshape,
 the entire data is shared, while in the case of slice, a part of the input data is shared.)
 The implementation may use views, as above, for intermediate values.</p>
    <p>Before the execution, the computation graph that is used to compute one or more specified outputs needs to be compiled and optimized. The key purpose of the compilation step is to enable optimizations that span two or more operations, such as operation or loop fusion.</p>
-   <p>There are multiple ways by which the graph may be compiled. The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build" id="ref-for-dom-mlgraphbuilder-build">build()</a></code> method compiles the graph in the background without blocking the calling thread, and returns a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-promise" id="ref-for-idl-promise">Promise</a></code> that resolves to an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②">MLGraph</a></code>. The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑦">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync">buildSync()</a></code> method compiles the graph immediately on the calling thread, which must be a worker thread running on CPU or GPU device, and returns an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph③">MLGraph</a></code>. Both compilation methods produce an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph④">MLGraph</a></code> that represents a compiled graph for optimal execution.</p>
+   <p>There are multiple ways by which the graph may be compiled. The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑦">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build" id="ref-for-dom-mlgraphbuilder-build">build()</a></code> method compiles the graph in the background without blocking the calling thread, and returns a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-promise" id="ref-for-idl-promise">Promise</a></code> that resolves to an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②">MLGraph</a></code>. The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑧">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync">buildSync()</a></code> method compiles the graph immediately on the calling thread, which must be a worker thread running on CPU or GPU device, and returns an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph③">MLGraph</a></code>. Both compilation methods produce an <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph④">MLGraph</a></code> that represents a compiled graph for optimal execution.</p>
    <p>Once the <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph⑤">MLGraph</a></code> is constructed, there are multiple ways by which the graph may be executed. The <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③">MLContext</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlcontext-computesync" id="ref-for-dom-mlcontext-computesync">computeSync()</a></code> method represents a way the execution of the graph is carried out immediately
 on the calling thread, which must also be a worker thread, either on a CPU or GPU device. The execution
 produces the results of the computation from all the inputs bound to the graph.</p>
@@ -1583,8 +1584,8 @@ <h3 class="heading settled" data-level="6.1" id="programming-model-overview"><sp
 time the operation is successfully completed on the offloaded timeline at which time the calling thread is
 signaled. This type of execution supports both the CPU and GPU device.</p>
    <p>In both the <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext⑤">MLContext</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlcontext-compute" id="ref-for-dom-mlcontext-compute③">compute()</a></code> and <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext⑥">MLContext</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlcontext-computesync" id="ref-for-dom-mlcontext-computesync①">computeSync()</a></code> execution methods, the caller supplies
-the input values using <code class="idl"><a data-link-type="idl" href="#typedefdef-mlnamedarraybufferviews" id="ref-for-typedefdef-mlnamedarraybufferviews">MLNamedArrayBufferViews</a></code>, binding the input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤">MLOperand</a></code>s to their values. The caller
-then supplies pre-allocated buffers for output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥">MLOperand</a></code>s using <code class="idl"><a data-link-type="idl" href="#typedefdef-mlnamedarraybufferviews" id="ref-for-typedefdef-mlnamedarraybufferviews①">MLNamedArrayBufferViews</a></code>.</p>
+the input values using <code class="idl"><a data-link-type="idl" href="#typedefdef-mlnamedarraybufferviews" id="ref-for-typedefdef-mlnamedarraybufferviews">MLNamedArrayBufferViews</a></code>, binding the input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦">MLOperand</a></code>s to their values. The caller
+then supplies pre-allocated buffers for output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑧">MLOperand</a></code>s using <code class="idl"><a data-link-type="idl" href="#typedefdef-mlnamedarraybufferviews" id="ref-for-typedefdef-mlnamedarraybufferviews①">MLNamedArrayBufferViews</a></code>.</p>
    <p>The <code class="idl"><a data-link-type="idl" href="#mlcommandencoder" id="ref-for-mlcommandencoder①">MLCommandEncoder</a></code> interface created by the <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext⑦">MLContext</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlcontext-createcommandencoder" id="ref-for-dom-mlcontext-createcommandencoder">createCommandEncoder()</a></code> method supports
 a graph execution method that provides the maximum flexibility to callers that also utilize WebGPU in their
 application. It does this by placing the workload required to initialize and compute the results of the
@@ -1752,38 +1753,38 @@ <h4 class="heading settled" data-level="7.2.3" id="api-ml-createcontextsync"><sp
      </ol>
     </div>
    </details>
-   <h3 class="heading settled" data-level="7.3" id="api-mlactivation"><span class="secno">7.3. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation">MLActivation</a></code> interface</span><a class="self-link" href="#api-mlactivation"></a></h3>
-   <p>Objects implementing the <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①">MLActivation</a></code> interface represent activation function types.</p>
+   <h3 class="heading settled" data-level="7.3" id="api-mlactivation"><span class="secno">7.3. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①">MLActivation</a></code> interface</span><a class="self-link" href="#api-mlactivation"></a></h3>
+   <p>Objects implementing the <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②">MLActivation</a></code> interface represent activation function types.</p>
 <pre class="idl highlight def">[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#SecureContext" id="ref-for-SecureContext②"><c- g>SecureContext</c-></a>, <a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#Exposed" id="ref-for-Exposed③"><c- g>Exposed</c-></a>=(<c- n>Window</c->, <c- n>DedicatedWorker</c->)]
 <c- b>interface</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="interface" data-export id="mlactivation"><code><c- g>MLActivation</c-></code></dfn> {
 };
 </pre>
    <div class="internal-slots">
-     <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②">MLActivation</a></code> has the following internal slots: 
+     <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③">MLActivation</a></code> has the following internal slots: 
     <dl>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-name-slot"><code>[[name]]</code></dfn> of type <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string">string</a>
      <dd data-md>
-      <p>The <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③">MLActivation</a></code>'s name.</p>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-builder-slot"><code>[[builder]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑧">MLGraphBuilder</a></code>
+      <p>The <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④">MLActivation</a></code>'s name.</p>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-builder-slot"><code>[[builder]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑨">MLGraphBuilder</a></code>
      <dd data-md>
-      <p>The graph builder object this <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④">MLActivation</a></code> belongs to.</p>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-options-slot"><code>[[options]]</code></dfn> of type <a data-link-type="dfn" href="https://w3c.github.io/FileAPI/#blob-url-entry-object" id="ref-for-blob-url-entry-object">object</a>
+      <p>The graph builder object this <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤">MLActivation</a></code> belongs to.</p>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-options-slot"><code>[[options]]</code></dfn> of type <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#ordered-map" id="ref-for-ordered-map">ordered map</a>
      <dd data-md>
-      <p>A dictionary containing <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤">MLActivation</a></code> options.</p>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-operator-slot"><code>[[operator]]</code></dfn> of type <a data-link-type="dfn" href="https://w3c.github.io/FileAPI/#blob-url-entry-object" id="ref-for-blob-url-entry-object①">object</a>
+      <p>A dictionary containing <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑥">MLActivation</a></code> options.</p>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLActivation" data-dfn-type="attribute" data-export id="dom-mlactivation-operator-slot"><code>[[operator]]</code></dfn> of type <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①">platform operator</a>
      <dd data-md>
-      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑥">MLActivation</a></code>'s corresponding <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined">implementation-defined</a> platform operator object.</p>
+      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑦">MLActivation</a></code>'s corresponding <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②">platform operator</a>.</p>
     </dl>
    </div>
    <div class="note" role="note"> These activations function types are used to create other operations. One such use of this interface is for when an activation function is fused into another operation such as <a href="#api-mlgraphbuilder-conv2d">§ 7.7.9 conv2d</a> or <a href="#api-mlgraphbuilder-batchnorm">§ 7.7.3 batchNormalization</a> during a graph construction session. Such fused activation functions can provide a significant performance improvement when supported natively by the underlying implementation. This is intended as an optimization opportunity for implementers. </div>
-   <h4 class="heading settled" data-level="7.3.1" id="api-mlactivation-create"><span class="secno">7.3.1. </span><span class="content">Creating <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑦">MLActivation</a></code></span><a class="self-link" href="#api-mlactivation-create"></a></h4>
-   <div class="note" role="note"> The <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑧">MLActivation</a></code> objects (including the ones passed as input to methods) are created by the methods of <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑨">MLGraphBuilder</a></code> and are identified by their name. The <var>options</var> dictionary is defined by those methods. The actual creation of the activation function e.g. a <a href="#api-mlgraphbuilder-sigmoid-method">§ 7.7.37 sigmoid</a> or <a href="#api-mlgraphbuilder-relu-method">§ 7.7.34 relu</a> can then be deferred until when the rest of the graph is ready to connect with it such as during the construction of <a href="#api-mlgraphbuilder-conv2d">§ 7.7.9 conv2d</a> for example. </div>
+   <h4 class="heading settled" data-level="7.3.1" id="api-mlactivation-create"><span class="secno">7.3.1. </span><span class="content">Creating <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑧">MLActivation</a></code></span><a class="self-link" href="#api-mlactivation-create"></a></h4>
+   <div class="note" role="note"> The <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑨">MLActivation</a></code> objects (including the ones passed as input to methods) are created by the methods of <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⓪">MLGraphBuilder</a></code> and are identified by their name. The <var>options</var> dictionary is defined by those methods. The actual creation of the activation function e.g. a <a href="#api-mlgraphbuilder-sigmoid-method">§ 7.7.37 sigmoid</a> or <a href="#api-mlgraphbuilder-relu-method">§ 7.7.34 relu</a> can then be deferred until when the rest of the graph is ready to connect with it such as during the construction of <a href="#api-mlgraphbuilder-conv2d">§ 7.7.9 conv2d</a> for example. </div>
    <details class="algorithm" data-algorithm="create an MLActivation" open>
     <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="create-an-mlactivation">create an MLActivation</dfn> given <var>builder</var>, <var>name</var>, optional <var>options</var> and optional <var>init-steps</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
-       <p>Let <var>activation</var> be a new <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑨">MLActivation</a></code>.</p>
+       <p>Let <var>activation</var> be a new <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①⓪">MLActivation</a></code>.</p>
       <li data-md>
        <p>Set <var>activation</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlactivation-builder-slot" id="ref-for-dom-mlactivation-builder-slot">[[builder]]</a></code> to <var>builder</var>.</p>
       <li data-md>
@@ -1797,7 +1798,7 @@ <h4 class="heading settled" data-level="7.3.1" id="api-mlactivation-create"><spa
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①">implementation-defined</a> platform operator <var>opImpl</var> for the given <var>name</var> operation.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③">platform operator</a> <var>opImpl</var> for the given <var>name</var> operation.</p>
           <li data-md>
            <p>Set <var>activation</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlactivation-operator-slot" id="ref-for-dom-mlactivation-operator-slot">[[operator]]</a></code> to <var>opImpl</var>.</p>
          </ol>
@@ -2301,18 +2302,18 @@ <h3 class="heading settled" data-level="7.6" id="api-mlgraph"><span class="secno
       <p>The context of type <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③③">MLContext</a></code> associated with this <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②②">MLGraph</a></code>.</p>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraph" data-dfn-type="attribute" data-export id="dom-mlgraph-inputdescriptors-slot"><code>[[inputDescriptors]]</code></dfn> of type <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-record" id="ref-for-idl-record②">record</a>&lt;<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMString" id="ref-for-idl-DOMString②">DOMString</a></code>, <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor">MLOperandDescriptor</a></code>>
      <dd data-md>
-      <p>Maps the name of an input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦">MLOperand</a></code> to its <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①">MLOperandDescriptor</a></code> for all input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑧">MLOperand</a></code>s of this <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②③">MLGraph</a></code>.</p>
+      <p>Maps the name of an input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨">MLOperand</a></code> to its <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①">MLOperandDescriptor</a></code> for all input <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⓪">MLOperand</a></code>s of this <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②③">MLGraph</a></code>.</p>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraph" data-dfn-type="attribute" data-export id="dom-mlgraph-outputdescriptors-slot"><code>[[outputDescriptors]]</code></dfn> of type <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-record" id="ref-for-idl-record③">record</a>&lt;<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMString" id="ref-for-idl-DOMString③">DOMString</a></code>, <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②">MLOperandDescriptor</a></code>>
      <dd data-md>
-      <p>Maps the name of an output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨">MLOperand</a></code> to its <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③">MLOperandDescriptor</a></code> for all output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⓪">MLOperand</a></code>s of this <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②④">MLGraph</a></code>.</p>
+      <p>Maps the name of an output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①">MLOperand</a></code> to its <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor③">MLOperandDescriptor</a></code> for all output <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①②">MLOperand</a></code>s of this <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②④">MLGraph</a></code>.</p>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraph" data-dfn-type="attribute" data-export id="dom-mlgraph-implementation-slot"><code>[[implementation]]</code></dfn>
      <dd data-md>
       <p>The underlying implementation provided by the User Agent.</p>
     </dl>
    </div>
-   <h3 class="heading settled" data-level="7.7" id="api-mlgraphbuilder"><span class="secno">7.7. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⓪">MLGraphBuilder</a></code> interface</span><a class="self-link" href="#api-mlgraphbuilder"></a></h3>
-   <p>The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①①">MLGraphBuilder</a></code> interface defines a set of operations as identified by the <a href="#usecases">§ 2 Use cases</a> that can be composed into a computational graph. It also represents the intermediate state of a graph building session.</p>
-<pre class="idl highlight def"><c- b>typedef</c-> <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-record" id="ref-for-idl-record④"><c- b>record</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-DOMString" id="ref-for-idl-DOMString④"><c- b>DOMString</c-></a>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①"><c- n>MLOperand</c-></a>> <dfn class="dfn-paneled idl-code" data-dfn-type="typedef" data-export id="typedefdef-mlnamedoperands"><code><c- g>MLNamedOperands</c-></code></dfn>;
+   <h3 class="heading settled" data-level="7.7" id="api-mlgraphbuilder"><span class="secno">7.7. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①①">MLGraphBuilder</a></code> interface</span><a class="self-link" href="#api-mlgraphbuilder"></a></h3>
+   <p>The <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①②">MLGraphBuilder</a></code> interface defines a set of operations as identified by the <a href="#usecases">§ 2 Use cases</a> that can be composed into a computational graph. It also represents the intermediate state of a graph building session.</p>
+<pre class="idl highlight def"><c- b>typedef</c-> <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-record" id="ref-for-idl-record④"><c- b>record</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-DOMString" id="ref-for-idl-DOMString④"><c- b>DOMString</c-></a>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③"><c- n>MLOperand</c-></a>> <dfn class="dfn-paneled idl-code" data-dfn-type="typedef" data-export id="typedefdef-mlnamedoperands"><code><c- g>MLNamedOperands</c-></code></dfn>;
 
 <c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlbufferresourceview"><code><c- g>MLBufferResourceView</c-></code></dfn> {
   <c- b>required</c-> <a data-link-type="idl-name" href="https://gpuweb.github.io/gpuweb/#gpubuffer" id="ref-for-gpubuffer④"><c- n>GPUBuffer</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="GPUBuffer " href="#dom-mlbufferresourceview-resource" id="ref-for-dom-mlbufferresourceview-resource"><c- g>resource</c-></a>;
@@ -2328,13 +2329,13 @@ <h3 class="heading settled" data-level="7.7" id="api-mlgraphbuilder"><span class
   <a class="idl-code" data-link-type="constructor" href="#dom-mlgraphbuilder-mlgraphbuilder" id="ref-for-dom-mlgraphbuilder-mlgraphbuilder"><c- g>constructor</c-></a>(<a data-link-type="idl-name" href="#mlcontext" id="ref-for-mlcontext③④"><c- n>MLContext</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/MLGraphBuilder(context), MLGraphBuilder/constructor(context)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-mlgraphbuilder-context-context"><code><c- g>context</c-></code><a class="self-link" href="#dom-mlgraphbuilder-mlgraphbuilder-context-context"></a></dfn>);
 
   // Create an operand for a graph input.
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②"><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-DOMString" id="ref-for-idl-DOMString⑤"><c- b>DOMString</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/input(name, descriptor)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-input-name-descriptor-name"><code><c- g>name</c-></code><a class="self-link" href="#dom-mlgraphbuilder-input-name-descriptor-name"></a></dfn>, <a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor④"><c- n>MLOperandDescriptor</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/input(name, descriptor)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-input-name-descriptor-descriptor"><code><c- g>descriptor</c-></code><a class="self-link" href="#dom-mlgraphbuilder-input-name-descriptor-descriptor"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④"><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-DOMString" id="ref-for-idl-DOMString⑤"><c- b>DOMString</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/input(name, descriptor)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-input-name-descriptor-name"><code><c- g>name</c-></code><a class="self-link" href="#dom-mlgraphbuilder-input-name-descriptor-name"></a></dfn>, <a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor④"><c- n>MLOperandDescriptor</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/input(name, descriptor)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-input-name-descriptor-descriptor"><code><c- g>descriptor</c-></code><a class="self-link" href="#dom-mlgraphbuilder-input-name-descriptor-descriptor"></a></dfn>);
 
   // Create an operand for a graph constant.
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant①"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor⑤"><c- n>MLOperandDescriptor</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"><code><c- g>descriptor</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"></a></dfn>, <a data-link-type="idl-name" href="#typedefdef-mlbufferview" id="ref-for-typedefdef-mlbufferview"><c- n>MLBufferView</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"><code><c- g>bufferView</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant" id="ref-for-dom-mlgraphbuilder-constant①"><c- g>constant</c-></a>(<a data-link-type="idl-name" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor⑤"><c- n>MLOperandDescriptor</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"><code><c- g>descriptor</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-descriptor"></a></dfn>, <a data-link-type="idl-name" href="#typedefdef-mlbufferview" id="ref-for-typedefdef-mlbufferview"><c- n>MLBufferView</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(descriptor, bufferView)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"><code><c- g>bufferView</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-descriptor-bufferview-bufferview"></a></dfn>);
 
   // Create a single-value operand from the specified number of the specified type.
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant-value-type" id="ref-for-dom-mlgraphbuilder-constant-value-type"><c- g>constant</c-></a>(<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-double" id="ref-for-idl-double"><c- b>double</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(value, type), MLGraphBuilder/constant(value)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-value-type-value"><code><c- g>value</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-value-type-value"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype①"><c- n>MLOperandDataType</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(value, type), MLGraphBuilder/constant(value)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-value-type-type"><code><c- g>type</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-value-type-type"></a></dfn> = "float32");
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-constant-value-type" id="ref-for-dom-mlgraphbuilder-constant-value-type"><c- g>constant</c-></a>(<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-double" id="ref-for-idl-double"><c- b>double</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(value, type), MLGraphBuilder/constant(value)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-value-type-value"><code><c- g>value</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-value-type-value"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype①"><c- n>MLOperandDataType</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/constant(value, type), MLGraphBuilder/constant(value)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-constant-value-type-type"><code><c- g>type</c-></code><a class="self-link" href="#dom-mlgraphbuilder-constant-value-type-type"></a></dfn> = "float32");
 
   // Compile the graph up to the specified output operands asynchronously.
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-promise" id="ref-for-idl-promise④"><c- b>Promise</c-></a>&lt;<a data-link-type="idl-name" href="#mlgraph" id="ref-for-mlgraph②⑤"><c- n>MLGraph</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-build" id="ref-for-dom-mlgraphbuilder-build①"><c- g>build</c-></a>(<a data-link-type="idl-name" href="#typedefdef-mlnamedoperands" id="ref-for-typedefdef-mlnamedoperands"><c- n>MLNamedOperands</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/build(outputs)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-build-outputs-outputs"><code><c- g>outputs</c-></code><a class="self-link" href="#dom-mlgraphbuilder-build-outputs-outputs"></a></dfn>);
@@ -2344,7 +2345,7 @@ <h3 class="heading settled" data-level="7.7" id="api-mlgraphbuilder"><span class
   <a data-link-type="idl-name" href="#mlgraph" id="ref-for-mlgraph②⑥"><c- n>MLGraph</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync①"><c- g>buildSync</c-></a>(<a data-link-type="idl-name" href="#typedefdef-mlnamedoperands" id="ref-for-typedefdef-mlnamedoperands①"><c- n>MLNamedOperands</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/buildSync(outputs)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-buildsync-outputs-outputs"><code><c- g>outputs</c-></code><a class="self-link" href="#dom-mlgraphbuilder-buildsync-outputs-outputs"></a></dfn>);
 };
 </pre>
-   <div class="note" role="note"> Both <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①②">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build" id="ref-for-dom-mlgraphbuilder-build②">build()</a></code> and <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①③">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync②">buildSync()</a></code> methods compile the graph builder state up to the specified output operands into a compiled graph according to the type of <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑤">MLContext</a></code> that creates it. Since this operation can be costly in some machine configurations, the calling thread of the <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①④">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync③">buildSync()</a></code> method must only be a worker thread to avoid potential disruption of the user experience. When the <code class="idl"><a data-link-type="idl" href="#dom-mlcontext-contexttype-slot" id="ref-for-dom-mlcontext-contexttype-slot⑥">[[contextType]]</a></code> of the <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑥">MLContext</a></code> is set to "<a data-link-type="dfn" href="#context-type-default" id="ref-for-context-type-default④">default</a>", the compiled graph is initialized right before the <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②⑦">MLGraph</a></code> is returned. This graph initialization stage is important for optimal performance of the subsequent graph executions. See <a href="#api-mlcommandencoder-graph-initialization">§ 7.4.1 Graph Initialization</a> for more detail. </div>
+   <div class="note" role="note"> Both <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①③">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-build" id="ref-for-dom-mlgraphbuilder-build②">build()</a></code> and <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①④">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync②">buildSync()</a></code> methods compile the graph builder state up to the specified output operands into a compiled graph according to the type of <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑤">MLContext</a></code> that creates it. Since this operation can be costly in some machine configurations, the calling thread of the <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑤">MLGraphBuilder</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-buildsync" id="ref-for-dom-mlgraphbuilder-buildsync③">buildSync()</a></code> method must only be a worker thread to avoid potential disruption of the user experience. When the <code class="idl"><a data-link-type="idl" href="#dom-mlcontext-contexttype-slot" id="ref-for-dom-mlcontext-contexttype-slot⑥">[[contextType]]</a></code> of the <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑥">MLContext</a></code> is set to "<a data-link-type="dfn" href="#context-type-default" id="ref-for-context-type-default④">default</a>", the compiled graph is initialized right before the <code class="idl"><a data-link-type="idl" href="#mlgraph" id="ref-for-mlgraph②⑦">MLGraph</a></code> is returned. This graph initialization stage is important for optimal performance of the subsequent graph executions. See <a href="#api-mlcommandencoder-graph-initialization">§ 7.4.1 Graph Initialization</a> for more detail. </div>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlbufferresourceview" id="ref-for-dictdef-mlbufferresourceview①">MLBufferResourceView</a></code> has the following members:</p>
    <dl>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBufferResourceView" data-dfn-type="dict-member" data-export id="dom-mlbufferresourceview-resource"><code>resource</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://gpuweb.github.io/gpuweb/#gpubuffer" id="ref-for-gpubuffer⑤">GPUBuffer</a></span>
@@ -2358,14 +2359,14 @@ <h3 class="heading settled" data-level="7.7" id="api-mlgraphbuilder"><span class
      <p>Specifies the <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long-long" id="ref-for-idl-unsigned-long-long⑤">unsigned long long</a></code> size of the buffer view.</p>
    </dl>
    <div class="internal-slots">
-     <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑤">MLGraphBuilder</a></code> has the following internal slots: 
+     <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑥">MLGraphBuilder</a></code> has the following internal slots: 
     <dl>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="attribute" data-export id="dom-mlgraphbuilder-context-slot"><code>[[context]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑦">MLContext</a></code>
      <dd data-md>
-      <p>The context of type <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑧">MLContext</a></code> associated with this <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑥">MLGraphBuilder</a></code>.</p>
+      <p>The context of type <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑧">MLContext</a></code> associated with this <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑦">MLGraphBuilder</a></code>.</p>
     </dl>
    </div>
-   <h4 class="heading settled" data-level="7.7.1" id="api-mlgraphbuilder-constructor"><span class="secno">7.7.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑦">MLGraphBuilder</a></code> constructor</span><a class="self-link" href="#api-mlgraphbuilder-constructor"></a></h4>
+   <h4 class="heading settled" data-level="7.7.1" id="api-mlgraphbuilder-constructor"><span class="secno">7.7.1. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑧">MLGraphBuilder</a></code> constructor</span><a class="self-link" href="#api-mlgraphbuilder-constructor"></a></h4>
    <details class="algorithm" data-algorithm="MLGraphBuilder(context)" data-algorithm-for="MLGraphBuilder" open>
     <summary> The <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#new" id="ref-for-new">new</a> <dfn class="dfn-paneled idl-code" data-dfn-for="MLGraphBuilder" data-dfn-type="constructor" data-export data-lt="MLGraphBuilder(context)|constructor(context)" id="dom-mlgraphbuilder-mlgraphbuilder"><code>MLGraphBuilder(<var>context</var>)</code></dfn> constructor steps are: </summary>
     <div class="algorithm-steps">
@@ -2385,9 +2386,9 @@ <h4 class="heading settled" data-level="7.7.2" id="api-mlgraphbuilder-argminmax"
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mlargminmaxoptions-selectlastindex" id="ref-for-dom-mlargminmaxoptions-selectlastindex"><c- g>selectLastIndex</c-></a> = <c- b>false</c->;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-argmin" id="ref-for-dom-mlgraphbuilder-argmin"><c- g>argMin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMin(input, options), MLGraphBuilder/argMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmin-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmin-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions"><c- n>MLArgMinMaxOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMin(input, options), MLGraphBuilder/argMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmin-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmin-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-argmax" id="ref-for-dom-mlgraphbuilder-argmax"><c- g>argMax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMax(input, options), MLGraphBuilder/argMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmax-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmax-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions①"><c- n>MLArgMinMaxOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMax(input, options), MLGraphBuilder/argMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmax-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmax-input-options-options"></a></dfn> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑨"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-argmin" id="ref-for-dom-mlgraphbuilder-argmin"><c- g>argMin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMin(input, options), MLGraphBuilder/argMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmin-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmin-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions"><c- n>MLArgMinMaxOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMin(input, options), MLGraphBuilder/argMin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmin-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmin-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-argmax" id="ref-for-dom-mlgraphbuilder-argmax"><c- g>argMax</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMax(input, options), MLGraphBuilder/argMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmax-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmax-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions①"><c- n>MLArgMinMaxOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/argMax(input, options), MLGraphBuilder/argMax(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-argmax-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-argmax-input-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions②">MLArgMinMaxOptions</a></code> has the following members:</p>
@@ -2406,14 +2407,14 @@ <h4 class="heading settled" data-level="7.7.2" id="api-mlgraphbuilder-argminmax"
      <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-mloperand①⑨">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-mloperand②①">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-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions③">MLArgMinMaxOptions</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-mloperand②⓪">MLOperand</a></code>. The N-D tensor of the reduced shape. The values must be of type <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int64" id="ref-for-dom-mloperanddatatype-int64">"int64"</a></code> in the range [0, N-1] where N is the corresponding size of each of the input dimensions specified by options.axes.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②">MLOperand</a></code>. The N-D tensor of the reduced shape. The values must be of type <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int64" id="ref-for-dom-mloperanddatatype-int64">"int64"</a></code> in the range [0, N-1] where N is the corresponding size of each of the input dimensions specified by options.axes.</p>
    </div>
    <details class="algorithm" data-algorithm="argminmax-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="argminmax-op" data-noexport id="mlgraphbuilder-argminmax-op">create argMin/argMax operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions④">MLArgMinMaxOptions</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="argminmax-op" data-noexport id="mlgraphbuilder-argminmax-op">create argMin/argMax operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③">MLOperand</a></code> <var>input</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mlargminmaxoptions" id="ref-for-dictdef-mlargminmaxoptions④">MLArgMinMaxOptions</a></code> <var>options</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -2437,11 +2438,11 @@ <h4 class="heading settled" data-level="7.7.2" id="api-mlgraphbuilder-argminmax"
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③">implementation-defined</a> platform operator for the <var>op</var> argMin or argMax operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④">platform operator</a> for the <var>op</var> argMin or argMax 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-slot">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -2487,49 +2488,49 @@ <h4 class="heading settled" data-level="7.7.2" id="api-mlgraphbuilder-argminmax"
    <h4 class="heading settled" data-level="7.7.3" id="api-mlgraphbuilder-batchnorm"><span class="secno">7.7.3. </span><span class="content">batchNormalization</span><a class="self-link" href="#api-mlgraphbuilder-batchnorm"></a></h4>
     Normalize the values of the input tensor using <a data-link-type="biblio" href="#biblio-batch-normalization" title="Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift">[Batch-Normalization]</a>. For each input feature, the mean and variance values of that feature are computed across all the samples in the batch dimension while the model is trained. These mean and variance values are then subsequently given to this operation during model inference. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlbatchnormalizationoptions"><code><c- g>MLBatchNormalizationOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlbatchnormalizationoptions-scale" id="ref-for-dom-mlbatchnormalizationoptions-scale"><c- g>scale</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlbatchnormalizationoptions-bias" id="ref-for-dom-mlbatchnormalizationoptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlbatchnormalizationoptions-scale" id="ref-for-dom-mlbatchnormalizationoptions-scale"><c- g>scale</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlbatchnormalizationoptions-bias" id="ref-for-dom-mlbatchnormalizationoptions-bias"><c- g>bias</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②"><c- b>unsigned</c-> <c- b>long</c-></a> <a class="idl-code" data-default="1" data-link-type="dict-member" data-type="unsigned long " href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis"><c- g>axis</c-></a> = 1;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float"><c- b>float</c-></a> <a class="idl-code" data-default="1e-5" data-link-type="dict-member" data-type="float " href="#dom-mlbatchnormalizationoptions-epsilon" id="ref-for-dom-mlbatchnormalizationoptions-epsilon"><c- g>epsilon</c-></a> = 1e-5;
-  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⓪"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlbatchnormalizationoptions-activation" id="ref-for-dom-mlbatchnormalizationoptions-activation"><c- g>activation</c-></a>;
+  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①①"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlbatchnormalizationoptions-activation" id="ref-for-dom-mlbatchnormalizationoptions-activation"><c- g>activation</c-></a>;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder①⑨"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-batchnormalization" id="ref-for-dom-mlgraphbuilder-batchnormalization"><c- g>batchNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-mean"><code><c- g>mean</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-mean"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-variance"><code><c- g>variance</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-variance"></a></dfn>,
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⓪"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-batchnormalization" id="ref-for-dom-mlgraphbuilder-batchnormalization"><c- g>batchNormalization</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-mean"><code><c- g>mean</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-mean"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-variance"><code><c- g>variance</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-variance"></a></dfn>,
                              <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlbatchnormalizationoptions" id="ref-for-dictdef-mlbatchnormalizationoptions"><c- n>MLBatchNormalizationOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/batchNormalization(input, mean, variance, options), MLGraphBuilder/batchNormalization(input, mean, variance)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-batchnormalization-input-mean-variance-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlbatchnormalizationoptions" id="ref-for-dictdef-mlbatchnormalizationoptions①">MLBatchNormalizationOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-scale"><code>scale</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨">MLOperand</a></code>. Specifies the 1-D tensor of the scaling values whose is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis①">axis</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①">MLOperand</a></code>. Specifies the 1-D tensor of the scaling values whose is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis①">axis</a></code>.</p>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③②">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③①">MLOperand</a></code>. Specifies 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-size①">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis②">axis</a></code>.</p>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③">MLOperand</a></code>. Specifies 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-size①">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis②">axis</a></code>.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-axis"><code>axis</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③">unsigned long</a>, defaulting to <code>1</code></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④">unsigned long</a></code> scalar. Specifies the index to the feature count dimension of the input shape for which the mean and variance values are. Its value must be in the range [0, N-1] where N is the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank②">rank</a> of the input tensor. The default value is 1, corresponding to the channel (<em>"c"</em>) dimension in the <code class="idl"><a data-link-type="idl" href="#dom-mlinputoperandlayout-nchw" id="ref-for-dom-mlinputoperandlayout-nchw">"nchw"</a></code> data layout.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-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-float①">float</a>, defaulting to <code>1e-5</code></span>
     <dd data-md>
      <p>A <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float②">float</a></code> scalar. Specifies A small value to prevent computational error due to divide-by-zero.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①①">MLActivation</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLBatchNormalizationOptions" data-dfn-type="dict-member" data-export id="dom-mlbatchnormalizationoptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①②">MLActivation</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①②">MLActivation</a></code> object. Specifies the optional activation function that immediately follows the normalization operation.</p>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①③">MLActivation</a></code> object. Specifies the optional activation function that immediately follows the normalization operation.</p>
    </dl>
    <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-mloperand③②">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-mloperand③④">MLOperand</a></code>. The input N-D tensor.</p>
      <li data-md>
-      <p><em>mean</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③">MLOperand</a></code>. Specifies the 1-D tensor of the mean values of the input features across the batch. Its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis③">axis</a></code>.</p>
+      <p><em>mean</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑤">MLOperand</a></code>. Specifies the 1-D tensor of the mean values of the input features across the batch. Its <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size②">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis③">axis</a></code>.</p>
      <li data-md>
-      <p><em>variance</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③④">MLOperand</a></code>. The 1-D tensor of the variance values of the input features across the batch whose <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis④">axis</a></code>.</p>
+      <p><em>variance</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑥">MLOperand</a></code>. The 1-D tensor of the variance values of the input features across the batch whose <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size③">size</a> is equal to the size of the input dimension denoted by <code class="idl"><a data-link-type="idl" href="#dom-mlbatchnormalizationoptions-axis" id="ref-for-dom-mlbatchnormalizationoptions-axis④">axis</a></code>.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlbatchnormalizationoptions" id="ref-for-dictdef-mlbatchnormalizationoptions②">MLBatchNormalizationOptions</a></code>. Specifies 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-mloperand③⑤">MLOperand</a></code>. The batch-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-mloperand③⑦">MLOperand</a></code>. The batch-normalized N-D tensor of the same shape as <em>input</em>.</p>
    </div>
    <details class="algorithm" data-algorithm="batchNormalization(input, mean, variance, 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="batchNormalization(input, mean, variance, options)|batchNormalization(input, mean, variance)" id="dom-mlgraphbuilder-batchnormalization"><code>batchNormalization(<var>input</var>, <var>mean</var>, <var>variance</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -2570,7 +2571,7 @@ <h4 class="heading settled" data-level="7.7.3" id="api-mlgraphbuilder-batchnorm"
          <p>Make a request to the underlying platform to initialize the batch normalization:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤">implementation-defined</a> platform operator <var>batchNormImpl</var> for this method, given <var>input</var>, <var>mean</var>, <var>variance</var> and <var>options</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator⑤">platform operator</a> <var>batchNormImpl</var> for this method, given <var>input</var>, <var>mean</var>, <var>variance</var> and <var>options</var>.</p>
           <li data-md>
            <p>If <var>options</var>.activation <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists⑧">exists</a>,register it as activation to <var>batchNormImpl</var>.</p>
           <li data-md>
@@ -2646,7 +2647,7 @@ <h5 class="heading settled" data-level="7.7.4.2" id="api-mlgraphbuilder-buildsyn
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Connect <var>graph</var> to a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥">implementation-defined</a> graph implementation <var>graphImpl</var> given <var>graph</var>.</p>
+           <p>Connect <var>graph</var> to a new <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③">implementation-defined</a> graph implementation <var>graphImpl</var> given <var>graph</var>.</p>
           <li data-md>
            <p>Set <var>graph</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgraph-implementation-slot" id="ref-for-dom-mlgraph-implementation-slot⑦">[[implementation]]</a></code> to <var>graphImpl</var>.</p>
          </ol>
@@ -2686,19 +2687,19 @@ <h5 class="heading settled" data-level="7.7.4.2" id="api-mlgraphbuilder-buildsyn
    </details>
    <h4 class="heading settled" data-level="7.7.5" id="api-mlgraphbuilder-cast"><span class="secno">7.7.5. </span><span class="content">cast</span><a class="self-link" href="#api-mlgraphbuilder-cast"></a></h4>
     Cast each element in the input tensor to the target data type. 
-<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-mlgraphbuilder②⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-cast" id="ref-for-dom-mlgraphbuilder-cast"><c- g>cast</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cast(input, type)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cast-input-type-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cast-input-type-input"></a></dfn>, <a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype②"><c- n>MLOperandDataType</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cast(input, type)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cast-input-type-type"><code><c- g>type</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cast-input-type-type"></a></dfn>);
+<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-mlgraphbuilder②①"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-cast" id="ref-for-dom-mlgraphbuilder-cast"><c- g>cast</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cast(input, type)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cast-input-type-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cast-input-type-input"></a></dfn>, <a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype②"><c- n>MLOperandDataType</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cast(input, type)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cast-input-type-type"><code><c- g>type</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cast-input-type-type"></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-mloperand③⑧">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-mloperand④⓪">MLOperand</a></code>. The input N-D tensor.</p>
      <li data-md>
       <p><em>type</em>: an <code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype③">MLOperandDataType</a></code>. The target data type.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨">MLOperand</a></code>. The N-D tensor of the same shape as <em>input</em> with each element casted to the target data type.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①">MLOperand</a></code>. The N-D tensor of the same shape as <em>input</em> with each element casted to the target data type.</p>
    </div>
    <details class="algorithm" data-algorithm="cast(input, type)" 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-cast"><code>cast(<var>input</var>, <var>type</var>)</code></dfn> method steps are: </summary>
@@ -2715,11 +2716,11 @@ <h4 class="heading settled" data-level="7.7.5" id="api-mlgraphbuilder-cast"><spa
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦">implementation-defined</a> platform operator <var>castImpl</var> for this method, given <var>type</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator⑥">platform operator</a> <var>castImpl</var> for this method, given <var>type</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-slot②">[[operator]]</a></code> to <var>castImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧">implementation-defined</a> platform operand <var>outputImpl</var> to represent an output, given <var>output</var> and <var>castImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①">platform operand</a> <var>outputImpl</var> to represent an output, given <var>output</var> and <var>castImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot④">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -2740,9 +2741,9 @@ <h4 class="heading settled" data-level="7.7.6" id="api-mlgraphbuilder-clamp"><sp
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float④"><c- b>float</c-></a> <dfn class="dfn-paneled idl-code" data-dfn-for="MLClampOptions" data-dfn-type="dict-member" data-export data-type="float " id="dom-mlclampoptions-maxvalue"><code><c- g>maxValue</c-></code></dfn>;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②①"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-clamp" id="ref-for-dom-mlgraphbuilder-clamp"><c- g>clamp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(input, options), MLGraphBuilder/clamp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlclampoptions" id="ref-for-dictdef-mlclampoptions"><c- n>MLClampOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(input, options), MLGraphBuilder/clamp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①③"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-clamp-options" id="ref-for-dom-mlgraphbuilder-clamp-options"><c- g>clamp</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlclampoptions" id="ref-for-dictdef-mlclampoptions①"><c- n>MLClampOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(options), MLGraphBuilder/clamp()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-options-options"></a></dfn> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②②"><c- g>MLGraphBuilder</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-clamp" id="ref-for-dom-mlgraphbuilder-clamp"><c- g>clamp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(input, options), MLGraphBuilder/clamp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlclampoptions" id="ref-for-dictdef-mlclampoptions"><c- n>MLClampOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(input, options), MLGraphBuilder/clamp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①④"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-clamp-options" id="ref-for-dom-mlgraphbuilder-clamp-options"><c- g>clamp</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlclampoptions" id="ref-for-dictdef-mlclampoptions①"><c- n>MLClampOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/clamp(options), MLGraphBuilder/clamp()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-clamp-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-clamp-options-options"></a></dfn> = {});
 };
 </pre>
    <div class="note" role="note">
@@ -2785,7 +2786,7 @@ <h5 class="heading settled" data-level="7.7.6.1" id="api-mlgraphbuilder-clamp-op
      <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-mloperand④②">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-mloperand④④">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-mlclampoptions" id="ref-for-dictdef-mlclampoptions②">MLClampOptions</a></code>. The optional parameters of the operation.</p>
       <ul>
@@ -2798,7 +2799,7 @@ <h5 class="heading settled" data-level="7.7.6.1" id="api-mlgraphbuilder-clamp-op
      <strong>Returns:</strong> 
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④③">MLOperand</a></code>. The output tensor of the same shape as <em>operand</em>.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑤">MLOperand</a></code>. The output tensor of the same shape as <em>operand</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="clamp(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -2816,11 +2817,11 @@ <h5 class="heading settled" data-level="7.7.6.1" id="api-mlgraphbuilder-clamp-op
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑨">implementation-defined</a> platform operator <var>clampImpl</var> for this method, given <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlclampoptions-minvalue" id="ref-for-dom-mlclampoptions-minvalue①">minValue</a></code> and <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlclampoptions-maxvalue" id="ref-for-dom-mlclampoptions-maxvalue①">maxValue</a></code>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator⑦">platform operator</a> <var>clampImpl</var> for this method, given <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlclampoptions-minvalue" id="ref-for-dom-mlclampoptions-minvalue①">minValue</a></code> and <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlclampoptions-maxvalue" id="ref-for-dom-mlclampoptions-maxvalue①">maxValue</a></code>.</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-slot③">[[operator]]</a></code> to <var>clampImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⓪">implementation-defined</a> platform operand <var>outputImpl</var> to represent clamp output, given <var>output</var> and <var>clampImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②">platform operand</a> <var>outputImpl</var> to represent clamp output, given <var>output</var> and <var>clampImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -2850,7 +2851,7 @@ <h5 class="heading settled" data-level="7.7.6.2" id="api-mlgraphbuilder-clamp-op
      <strong>Returns:</strong> 
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①④">MLActivation</a></code>. The operator representing the clamp operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①⑤">MLActivation</a></code>. The operator representing the clamp operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="clamp(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -2872,20 +2873,20 @@ <h5 class="heading settled" data-level="7.7.6.2" id="api-mlgraphbuilder-clamp-op
    </details>
    <h4 class="heading settled" data-level="7.7.7" id="api-mlgraphbuilder-concat"><span class="secno">7.7.7. </span><span class="content">concat</span><a class="self-link" href="#api-mlgraphbuilder-concat"></a></h4>
     Concatenates the input tensors along a given axis. 
-<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-mlgraphbuilder②②"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-concat" id="ref-for-dom-mlgraphbuilder-concat"><c- g>concat</c-></a>(<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④⑤"><c- n>MLOperand</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/concat(inputs, axis)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-concat-inputs-axis-inputs"><code><c- g>inputs</c-></code><a class="self-link" href="#dom-mlgraphbuilder-concat-inputs-axis-inputs"></a></dfn>, <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/concat(inputs, axis)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-concat-inputs-axis-axis"><code><c- g>axis</c-></code><a class="self-link" href="#dom-mlgraphbuilder-concat-inputs-axis-axis"></a></dfn>);
+<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-mlgraphbuilder②③"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-concat" id="ref-for-dom-mlgraphbuilder-concat"><c- g>concat</c-></a>(<a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand④⑦"><c- n>MLOperand</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/concat(inputs, axis)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-concat-inputs-axis-inputs"><code><c- g>inputs</c-></code><a class="self-link" href="#dom-mlgraphbuilder-concat-inputs-axis-inputs"></a></dfn>, <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/concat(inputs, axis)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-concat-inputs-axis-axis"><code><c- g>axis</c-></code><a class="self-link" href="#dom-mlgraphbuilder-concat-inputs-axis-axis"></a></dfn>);
 };
 </pre>
    <div>
      <strong>Arguments:</strong> 
     <ul>
      <li data-md>
-      <p><em>inputs</em>: a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑥">MLOperand</a></code>. All input tensors must have the
+      <p><em>inputs</em>: a sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑧">MLOperand</a></code>. All input tensors must have the
 same shape, except for the size of the dimension to concatenate on.</p>
      <li data-md>
       <p><em>axis</em>: an <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥">unsigned long</a></code> scalar. The axis that the inputs concatenate along. Its value must be in the range [0, N-1] where N is the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank④">rank</a> of the input tensors.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑦">MLOperand</a></code>. The concatenated tensor of all the inputs along
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑨">MLOperand</a></code>. The concatenated tensor of all the inputs along
     the <em>axis</em>. The output tensor has the same shape except on the dimension
     that all the inputs concatenated along. The size of that dimension is
     computed as the sum of all the input sizes of the same dimension.</p>
@@ -2937,11 +2938,11 @@ <h4 class="heading settled" data-level="7.7.7" id="api-mlgraphbuilder-concat"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①①">implementation-defined</a> platform operator <var>concatImpl</var> for this method, given <var>inputs</var> and <var>axis</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator⑧">platform operator</a> <var>concatImpl</var> for this method, given <var>inputs</var> and <var>axis</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-slot④">[[operator]]</a></code> to <var>concatImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①②">implementation-defined</a> platform operand <var>outputImpl</var> to represent output,given <var>output</var> and <var>concatImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③">platform operand</a> <var>outputImpl</var> to represent output,given <var>output</var> and <var>concatImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⓪">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -2956,9 +2957,9 @@ <h4 class="heading settled" data-level="7.7.7" id="api-mlgraphbuilder-concat"><s
     </div>
    </details>
    <h4 class="heading settled" data-level="7.7.8" id="api-mlgraphbuilder-constant"><span class="secno">7.7.8. </span><span class="content">constant</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-mloperand④⑧">MLOperand</a></code> that can be used in <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②③">MLGraphBuilder</a></code> methods. 
+    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤⓪">MLOperand</a></code> that can be used in <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②④">MLGraphBuilder</a></code> methods. 
    <h5 class="heading settled" data-level="7.7.8.1" id="api-mlgraphbuilder-constant-bufferview"><span class="secno">7.7.8.1. </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>
-    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⑨">MLOperand</a></code> of the specified data type and shape that contains the initializing data. 
+    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤①">MLOperand</a></code> of the specified data type and shape that contains the initializing data. 
    <div>
      <strong>Arguments:</strong> 
     <ul>
@@ -2967,7 +2968,7 @@ <h5 class="heading settled" data-level="7.7.8.1" id="api-mlgraphbuilder-constant
      <li data-md>
       <p><em>bufferView</em>: an <code class="idl"><a data-link-type="idl" href="#typedefdef-mlbufferview" id="ref-for-typedefdef-mlbufferview①">MLBufferView</a></code>. The view of 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-mloperand⑤⓪">MLOperand</a></code>. The constant output tensor. 
+     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤②">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>
@@ -2991,7 +2992,7 @@ <h5 class="heading settled" data-level="7.7.8.1" id="api-mlgraphbuilder-constant
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①③">implementation-defined</a> platform operand <var>constantImpl</var> to represent a constant, given <var>descriptor</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④">platform operand</a> <var>constantImpl</var> to represent a constant, given <var>descriptor</var>.</p>
           <li data-md>
            <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①②">[[operand]]</a></code> to <var>constantImpl</var>.</p>
           <li data-md>
@@ -3004,7 +3005,7 @@ <h5 class="heading settled" data-level="7.7.8.1" id="api-mlgraphbuilder-constant
     </div>
    </details>
    <h5 class="heading settled" data-level="7.7.8.2" id="api-mlgraphbuilder-constant-value-type"><span class="secno">7.7.8.2. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant-value-type" id="ref-for-dom-mlgraphbuilder-constant-value-type①">constant(value, type)</a></code></span><a class="self-link" href="#api-mlgraphbuilder-constant-value-type"></a></h5>
-    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤①">MLOperand</a></code> of the specified value and data type. 
+    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤③">MLOperand</a></code> of the specified value and data type. 
    <div class="note" role="note"> Data truncation will occur when the specified value exceeds the range of the specified output data type e.g. when a float value is assigned to an <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int8" id="ref-for-dom-mloperanddatatype-int8">"int8"</a></code> data type, etc. </div>
    <div>
      <strong>Arguments:</strong> 
@@ -3014,7 +3015,7 @@ <h5 class="heading settled" data-level="7.7.8.2" id="api-mlgraphbuilder-constant
      <li data-md>
       <p><em>type</em>: an optional <code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype④">MLOperandDataType</a></code>. If not specified, it is assumed to be <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-float32" id="ref-for-dom-mloperanddatatype-float32">"float32"</a></code>.</p>
     </ul>
-     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤②">MLOperand</a></code>. The constant output. 
+     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤④">MLOperand</a></code>. The constant output. 
    </div>
    <details class="algorithm" data-algorithm="constant(value, type)" 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="constant(value, type)|constant(value)" id="dom-mlgraphbuilder-constant-value-type"><code>constant(<var>value</var>, <var>type</var>)</code></dfn> method steps are: </summary>
@@ -3039,7 +3040,7 @@ <h5 class="heading settled" data-level="7.7.8.2" id="api-mlgraphbuilder-constant
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①④">implementation-defined</a> platform operand <var>constantImpl</var> to represent a constant, given <var>descriptor</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand⑤">platform operand</a> <var>constantImpl</var> to represent a constant, given <var>descriptor</var>.</p>
           <li data-md>
            <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①③">[[operand]]</a></code> to <var>constantImpl</var>.</p>
           <li data-md>
@@ -3052,7 +3053,7 @@ <h5 class="heading settled" data-level="7.7.8.2" id="api-mlgraphbuilder-constant
     </div>
    </details>
    <h5 class="heading settled" data-level="7.7.8.3" id="api-mlgraphbuilder-constant-range"><span class="secno">7.7.8.3. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-constant-start-end-step-type" id="ref-for-dom-mlgraphbuilder-constant-start-end-step-type">constant(start, end, step, type)</a></code></span><a class="self-link" href="#api-mlgraphbuilder-constant-range"></a></h5>
-    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤③">MLOperand</a></code> of the specified data type and shape that contains the data as specified by the range. 
+    Create a constant <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤⑤">MLOperand</a></code> of the specified data type and shape that contains the data as specified by the range. 
    <div class="note" role="note"> Data truncation will occur when the values in the range exceed the range of the specified output data type e.g. when a float value is assigned to an <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int8" id="ref-for-dom-mloperanddatatype-int8①">"int8"</a></code> data type, etc. </div>
    <div>
      <strong>Arguments:</strong> 
@@ -3066,7 +3067,7 @@ <h5 class="heading settled" data-level="7.7.8.3" id="api-mlgraphbuilder-constant
      <li data-md>
       <p><em>type</em>: an optional <code class="idl"><a data-link-type="idl" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype⑤">MLOperandDataType</a></code>. If not specified, it is assumed to be <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-float32" id="ref-for-dom-mloperanddatatype-float32①">"float32"</a></code>.</p>
     </ul>
-     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤④">MLOperand</a></code>. The constant 1-D output tensor of size <code>max(0, ceil((end - start)/step))</code>. 
+     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑤⑥">MLOperand</a></code>. The constant 1-D output tensor of size <code>max(0, ceil((end - start)/step))</code>. 
    </div>
    <details class="algorithm" data-algorithm="constant(start, end, step, type)" 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-start-end-step-type"><code>constant(<var>start</var>, <var>end</var>, <var>step</var>, <var>type</var>)</code></dfn> method steps are: </summary>
@@ -3092,7 +3093,7 @@ <h5 class="heading settled" data-level="7.7.8.3" id="api-mlgraphbuilder-constant
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⑤">implementation-defined</a> platform memory buffer the size of <var>size</var> multiplied by sizeof(<var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype⑧">dataType</a></code>).</p>
+           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④">implementation-defined</a> platform memory buffer the size of <var>size</var> multiplied by sizeof(<var>descriptor</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-datatype" id="ref-for-dom-mloperanddescriptor-datatype⑧">dataType</a></code>).</p>
           <li data-md>
            <p>Store the beginning address to that memory buffer as a pointer <var>buffer</var> of the corresponding data type.</p>
          </ol>
@@ -3106,7 +3107,7 @@ <h5 class="heading settled" data-level="7.7.8.3" id="api-mlgraphbuilder-constant
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⑥">implementation-defined</a> platform operand <var>constantImpl</var> to represent a constant operand, given <var>descriptor</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand⑥">platform operand</a> <var>constantImpl</var> to represent a constant operand, given <var>descriptor</var>.</p>
           <li data-md>
            <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①④">[[operand]]</a></code> to <var>constantImpl</var>.</p>
           <li data-md>
@@ -3141,12 +3142,12 @@ <h4 class="heading settled" data-level="7.7.9" id="api-mlgraphbuilder-conv2d"><s
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long①⓪"><c- b>unsigned</c-> <c- b>long</c-></a> <a class="idl-code" data-default="1" data-link-type="dict-member" data-type="unsigned long " href="#dom-mlconv2doptions-groups" id="ref-for-dom-mlconv2doptions-groups"><c- g>groups</c-></a> = 1;
   <a data-link-type="idl-name" href="#enumdef-mlinputoperandlayout" id="ref-for-enumdef-mlinputoperandlayout"><c- n>MLInputOperandLayout</c-></a> <a class="idl-code" data-default="&quot;nchw&quot;" data-link-type="dict-member" data-type="MLInputOperandLayout " href="#dom-mlconv2doptions-inputlayout" id="ref-for-dom-mlconv2doptions-inputlayout"><c- g>inputLayout</c-></a> = "nchw";
   <a data-link-type="idl-name" href="#enumdef-mlconv2dfilteroperandlayout" id="ref-for-enumdef-mlconv2dfilteroperandlayout"><c- n>MLConv2dFilterOperandLayout</c-></a> <a class="idl-code" data-default="&quot;oihw&quot;" data-link-type="dict-member" data-type="MLConv2dFilterOperandLayout " href="#dom-mlconv2doptions-filterlayout" id="ref-for-dom-mlconv2doptions-filterlayout"><c- g>filterLayout</c-></a> = "oihw";
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlconv2doptions-bias" id="ref-for-dom-mlconv2doptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑤"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlconv2doptions-activation" id="ref-for-dom-mlconv2doptions-activation"><c- g>activation</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlconv2doptions-bias" id="ref-for-dom-mlconv2doptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑥"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlconv2doptions-activation" id="ref-for-dom-mlconv2doptions-activation"><c- g>activation</c-></a>;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②④"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-conv2d" id="ref-for-dom-mlgraphbuilder-conv2d"><c- g>conv2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-filter"><code><c- g>filter</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-filter"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlconv2doptions" id="ref-for-dictdef-mlconv2doptions"><c- n>MLConv2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-options"></a></dfn> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⑤"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-conv2d" id="ref-for-dom-mlgraphbuilder-conv2d"><c- g>conv2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-filter"><code><c- g>filter</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-filter"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlconv2doptions" id="ref-for-dictdef-mlconv2doptions"><c- n>MLConv2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/conv2d(input, filter, options), MLGraphBuilder/conv2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-conv2d-input-filter-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-conv2d-input-filter-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlconv2doptions" id="ref-for-dictdef-mlconv2doptions①">MLConv2dOptions</a></code> has the following members:</p>
@@ -3216,28 +3217,28 @@ <h4 class="heading settled" data-level="7.7.9" id="api-mlgraphbuilder-conv2d"><s
        <p><code class="idl"><a data-link-type="idl" href="#dom-mlconv2dfilteroperandlayout-ihwo" id="ref-for-dom-mlconv2dfilteroperandlayout-ihwo">"ihwo"</a></code>: <em>[inputChannels/groups, height, width, outputChannels]</em></p>
      </ul>
       The default value is <code class="idl"><a data-link-type="idl" href="#dom-mlconv2dfilteroperandlayout-oihw" id="ref-for-dom-mlconv2dfilteroperandlayout-oihw①">"oihw"</a></code>. 
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConv2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconv2doptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑤⑨">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConv2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconv2doptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥①">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥⓪">MLOperand</a></code> object.
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥②">MLOperand</a></code> object.
   Specifies the additional 1-D tensor with the shape of <em>[outputChannels]</em> whose values are to be added to the convolution result.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConv2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconv2doptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑥">MLActivation</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConv2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconv2doptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑦">MLActivation</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①⑦">MLActivation</a></code> object.
+     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation①⑧">MLActivation</a></code> object.
     Specifies the optional activation function that immediately follows the convolution operation.</p>
    </dl>
    <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-mloperand⑥①">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-mloperand⑥③">MLOperand</a></code>. The input 4-D tensor. The logical shape
 is interpreted according to the value of <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-inputlayout" id="ref-for-dom-mlconv2doptions-inputlayout①">inputLayout</a></code>.</p>
      <li data-md>
-      <p><em>filter</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥②">MLOperand</a></code>. The filter 4-D tensor. The logical shape is
+      <p><em>filter</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥④">MLOperand</a></code>. The filter 4-D tensor. The logical shape is
 interpreted according to the value of <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-filterlayout" id="ref-for-dom-mlconv2doptions-filterlayout①">filterLayout</a></code> and <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-groups" id="ref-for-dom-mlconv2doptions-groups①">groups</a></code>.</p>
      <li data-md>
       <p><em>options</em>: an <code class="idl"><a data-link-type="idl" href="#dictdef-mlconv2doptions" id="ref-for-dictdef-mlconv2doptions②">MLConv2dOptions</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-mloperand⑥③">MLOperand</a></code>. The output 4-D tensor that contains the convolution result. The output shape is interpreted according to the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-inputlayout" id="ref-for-dom-mlconv2doptions-inputlayout②">inputLayout</a></code> value. More specifically, the spatial dimensions or the sizes of the last two dimensions of the output tensor for the <em>nchw</em> input layout can be calculated as follow:</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥⑤">MLOperand</a></code>. The output 4-D tensor that contains the convolution result. The output shape is interpreted according to the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-inputlayout" id="ref-for-dom-mlconv2doptions-inputlayout②">inputLayout</a></code> value. More specifically, the spatial dimensions or the sizes of the last two dimensions of the output tensor for the <em>nchw</em> input layout can be calculated as follow:</p>
     <p><em>outputSize = 1 + (inputSize - (filterSize - 1) *</em> <em>dilation - 1 + beginningPadding + endingPadding) / stride</em></p>
    </div>
    <div class="note" role="note"> A <em>depthwise</em> conv2d operation is a variant of grouped convolution, used in models like the MobileNet, where the <em>options.groups</em> = inputChannels = outputChannels and the shape of filter tensor is [options.groups, 1, height, width]
@@ -3305,7 +3306,7 @@ <h4 class="heading settled" data-level="7.7.9" id="api-mlgraphbuilder-conv2d"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⑦">implementation-defined</a> platform operator <var>conv2dImpl</var> for this method, given <var>options</var> and <var>filter</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator⑨">platform operator</a> <var>conv2dImpl</var> for this method, given <var>options</var> and <var>filter</var>.</p>
            <ol>
             <li data-md>
              <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlconv2doptions-activation" id="ref-for-dom-mlconv2doptions-activation①">activation</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists①④">exists</a>,register it as activation to <var>conv2dImpl</var>.</p>
@@ -3313,7 +3314,7 @@ <h4 class="heading settled" data-level="7.7.9" id="api-mlgraphbuilder-conv2d"><s
           <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-slot⑤">[[operator]]</a></code> to <var>conv2dImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⑧">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>conv2dImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand⑦">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>conv2dImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -3345,12 +3346,12 @@ <h4 class="heading settled" data-level="7.7.10" id="api-mlgraphbuilder-convtrans
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②①"><c- b>unsigned</c-> <c- b>long</c-></a> <a class="idl-code" data-default="1" data-link-type="dict-member" data-type="unsigned long " href="#dom-mlconvtranspose2doptions-groups" id="ref-for-dom-mlconvtranspose2doptions-groups"><c- g>groups</c-></a> = 1;
   <a data-link-type="idl-name" href="#enumdef-mlinputoperandlayout" id="ref-for-enumdef-mlinputoperandlayout③"><c- n>MLInputOperandLayout</c-></a> <a class="idl-code" data-default="&quot;nchw&quot;" data-link-type="dict-member" data-type="MLInputOperandLayout " href="#dom-mlconvtranspose2doptions-inputlayout" id="ref-for-dom-mlconvtranspose2doptions-inputlayout"><c- g>inputLayout</c-></a> = "nchw";
   <a data-link-type="idl-name" href="#enumdef-mlconvtranspose2dfilteroperandlayout" id="ref-for-enumdef-mlconvtranspose2dfilteroperandlayout"><c- n>MLConvTranspose2dFilterOperandLayout</c-></a> <a class="idl-code" data-default="&quot;iohw&quot;" data-link-type="dict-member" data-type="MLConvTranspose2dFilterOperandLayout " href="#dom-mlconvtranspose2doptions-filterlayout" id="ref-for-dom-mlconvtranspose2doptions-filterlayout"><c- g>filterLayout</c-></a> = "iohw";
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlconvtranspose2doptions-bias" id="ref-for-dom-mlconvtranspose2doptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑧"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlconvtranspose2doptions-activation" id="ref-for-dom-mlconvtranspose2doptions-activation"><c- g>activation</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlconvtranspose2doptions-bias" id="ref-for-dom-mlconvtranspose2doptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑨"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLActivation " href="#dom-mlconvtranspose2doptions-activation" id="ref-for-dom-mlconvtranspose2doptions-activation"><c- g>activation</c-></a>;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⑤"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-convtranspose2d" id="ref-for-dom-mlgraphbuilder-convtranspose2d"><c- g>convTranspose2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options), MLGraphBuilder/convTranspose2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-convtranspose2d-input-filter-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-convtranspose2d-input-filter-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options), MLGraphBuilder/convTranspose2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-convtranspose2d-input-filter-options-filter"><code><c- g>filter</c-></code><a class="self-link" href="#dom-mlgraphbuilder-convtranspose2d-input-filter-options-filter"></a></dfn>,
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⑥"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-convtranspose2d" id="ref-for-dom-mlgraphbuilder-convtranspose2d"><c- g>convTranspose2d</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options), MLGraphBuilder/convTranspose2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-convtranspose2d-input-filter-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-convtranspose2d-input-filter-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options), MLGraphBuilder/convTranspose2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-convtranspose2d-input-filter-options-filter"><code><c- g>filter</c-></code><a class="self-link" href="#dom-mlgraphbuilder-convtranspose2d-input-filter-options-filter"></a></dfn>,
                             <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlconvtranspose2doptions" id="ref-for-dictdef-mlconvtranspose2doptions"><c- n>MLConvTranspose2dOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/convTranspose2d(input, filter, options), MLGraphBuilder/convTranspose2d(input, filter)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-convtranspose2d-input-filter-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-convtranspose2d-input-filter-options-options"></a></dfn> = {});
 };
 </pre>
@@ -3430,28 +3431,28 @@ <h4 class="heading settled" data-level="7.7.10" id="api-mlgraphbuilder-convtrans
        <p><code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2dfilteroperandlayout-ohwi" id="ref-for-dom-mlconvtranspose2dfilteroperandlayout-ohwi">"ohwi"</a></code>: [outputChannels/groups, height, width, inputChannels]</p>
      </ul>
      The default value is <code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2dfilteroperandlayout-iohw" id="ref-for-dom-mlconvtranspose2dfilteroperandlayout-iohw①">"iohw"</a></code>. 
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConvTranspose2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconvtranspose2doptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑥⑧">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConvTranspose2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconvtranspose2doptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⓪">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑥⑨">MLOperand</a></code> object.
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦①">MLOperand</a></code> object.
 Specifies the additional 1-D tensor with the shape of <em>[outputChannels]</em> whose values are to be added to the convolution result.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConvTranspose2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconvtranspose2doptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation①⑨">MLActivation</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLConvTranspose2dOptions" data-dfn-type="dict-member" data-export id="dom-mlconvtranspose2doptions-activation"><code>activation</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⓪">MLActivation</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②⓪">MLActivation</a></code> object.
+     <p>An <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②①">MLActivation</a></code> object.
 Specifies the optional activation function that immediately follows the convolution operation.</p>
    </dl>
    <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-mloperand⑦⓪">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-mloperand⑦②">MLOperand</a></code>. The input 4-D tensor. The logical shape
 is interpreted according to the value of <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-inputlayout" id="ref-for-dom-mlconvtranspose2doptions-inputlayout①">inputLayout</a></code>.</p>
      <li data-md>
-      <p><em>filter</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦①">MLOperand</a></code>. The filter 4-D tensor. The logical shape is
+      <p><em>filter</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦③">MLOperand</a></code>. The filter 4-D tensor. The logical shape is
 interpreted according to the value of <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-filterlayout" id="ref-for-dom-mlconvtranspose2doptions-filterlayout①">filterLayout</a></code> and <code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-groups" id="ref-for-dom-mlconvtranspose2doptions-groups①">groups</a></code>.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlconvtranspose2doptions" id="ref-for-dictdef-mlconvtranspose2doptions②">MLConvTranspose2dOptions</a></code>.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦②">MLOperand</a></code>. The output 4-D tensor that contains the transposed convolution result. The output shape is interpreted according to the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-inputlayout" id="ref-for-dom-mlconvtranspose2doptions-inputlayout②">inputLayout</a></code> value. More specifically, unless the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-outputsizes" id="ref-for-dom-mlconvtranspose2doptions-outputsizes①">outputSizes</a></code> values are explicitly specified, the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-outputpadding" id="ref-for-dom-mlconvtranspose2doptions-outputpadding②">outputPadding</a></code> may be needed to compute the spatial dimension values of the output tensor as follow:</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑦④">MLOperand</a></code>. The output 4-D tensor that contains the transposed convolution result. The output shape is interpreted according to the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-inputlayout" id="ref-for-dom-mlconvtranspose2doptions-inputlayout②">inputLayout</a></code> value. More specifically, unless the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-outputsizes" id="ref-for-dom-mlconvtranspose2doptions-outputsizes①">outputSizes</a></code> values are explicitly specified, the <em>options</em>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-outputpadding" id="ref-for-dom-mlconvtranspose2doptions-outputpadding②">outputPadding</a></code> may be needed to compute the spatial dimension values of the output tensor as follow:</p>
     <p><em>outputSize = (inputSize - 1) *</em> <em>stride + (filterSize - 1) *</em> <em>dilation + 1 - beginningPadding - endingPadding + outputPadding</em></p>
    </div>
    <details class="algorithm" data-algorithm="convTranspose2d(input, filter, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -3525,7 +3526,7 @@ <h4 class="heading settled" data-level="7.7.10" id="api-mlgraphbuilder-convtrans
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined①⑨">implementation-defined</a> platform operator <var>convTranspose2dImpl</var> for this method, given <var>options</var> and <var>filter</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⓪">platform operator</a> <var>convTranspose2dImpl</var> for this method, given <var>options</var> and <var>filter</var>.</p>
            <ol>
             <li data-md>
              <p>If <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlconvtranspose2doptions-activation" id="ref-for-dom-mlconvtranspose2doptions-activation①">activation</a></code> <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#map-exists" id="ref-for-map-exists②①">exists</a>,register it as activation to <var>convTranspose2dImpl</var>.</p>
@@ -3533,7 +3534,7 @@ <h4 class="heading settled" data-level="7.7.10" id="api-mlgraphbuilder-convtrans
           <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-slot⑥">[[operator]]</a></code> to <var>convTranspose2dImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⓪">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>convTranspose2dImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand⑧">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>convTranspose2dImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⑧">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -3551,25 +3552,25 @@ <h4 class="heading settled" data-level="7.7.11" id="api-mlgraphbuilder-binary"><
     Compute the element-wise binary addition, subtraction, multiplication, division, power, maximum and minimum of the two input tensors. 
    <p>The element-wise binary operations 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>. The <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑧">rank</a> of the output tensor is the maximum <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑨">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-mlgraphbuilder②⑥"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-add" id="ref-for-dom-mlgraphbuilder-add"><c- g>add</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/add(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-add-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-add-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/add(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-add-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-add-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sub" id="ref-for-dom-mlgraphbuilder-sub"><c- g>sub</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sub(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sub-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sub-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sub(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sub-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sub-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-mul" id="ref-for-dom-mlgraphbuilder-mul"><c- g>mul</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/mul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-mul-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-mul-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/mul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-mul-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-mul-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-div" id="ref-for-dom-mlgraphbuilder-div"><c- g>div</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/div(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-div-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-div-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/div(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-div-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-div-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-max" id="ref-for-dom-mlgraphbuilder-max"><c- g>max</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/max(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-max-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-max-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/max(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-max-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-max-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-min" id="ref-for-dom-mlgraphbuilder-min"><c- g>min</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/min(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-min-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-min-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/min(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-min-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-min-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-pow" id="ref-for-dom-mlgraphbuilder-pow"><c- g>pow</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pow(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pow-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pow-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pow(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pow-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pow-a-b-b"></a></dfn>);
+<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-mlgraphbuilder②⑦"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-add" id="ref-for-dom-mlgraphbuilder-add"><c- g>add</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/add(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-add-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-add-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/add(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-add-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-add-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sub" id="ref-for-dom-mlgraphbuilder-sub"><c- g>sub</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑦⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sub(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sub-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sub-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sub(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sub-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sub-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-mul" id="ref-for-dom-mlgraphbuilder-mul"><c- g>mul</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/mul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-mul-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-mul-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/mul(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-mul-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-mul-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-div" id="ref-for-dom-mlgraphbuilder-div"><c- g>div</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/div(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-div-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-div-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/div(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-div-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-div-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-max" id="ref-for-dom-mlgraphbuilder-max"><c- g>max</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/max(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-max-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-max-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑧⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/max(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-max-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-max-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-min" id="ref-for-dom-mlgraphbuilder-min"><c- g>min</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/min(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-min-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-min-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/min(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-min-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-min-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-pow" id="ref-for-dom-mlgraphbuilder-pow"><c- g>pow</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pow(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pow-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pow-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/pow(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-pow-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-pow-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-mloperand⑨④">MLOperand</a></code>. The first input tensor.</p>
+      <p><em>a</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑥">MLOperand</a></code>. The first input tensor.</p>
      <li data-md>
-      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑤">MLOperand</a></code>. The second input tensor.</p>
+      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑦">MLOperand</a></code>. The second input tensor.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑥">MLOperand</a></code>. The output tensor that contains the result of
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑧">MLOperand</a></code>. The output tensor that contains the result of
     element-wise binary operation of the two input tensors.</p>
    </div>
    <div>
@@ -3592,7 +3593,7 @@ <h4 class="heading settled" data-level="7.7.11" id="api-mlgraphbuilder-binary"><
     </ul>
    </div>
    <details class="algorithm" data-algorithm="element-wise-binary-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-binary-op" data-noexport id="mlgraphbuilder-element-wise-binary-op">create element-wise binary operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑦">MLOperand</a></code> <var>a</var> and <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑧">MLOperand</a></code> <var>b</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-binary-op" data-noexport id="mlgraphbuilder-element-wise-binary-op">create element-wise binary operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand⑨⑨">MLOperand</a></code> <var>a</var> and <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⓪⓪">MLOperand</a></code> <var>b</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -3618,11 +3619,11 @@ <h4 class="heading settled" data-level="7.7.11" id="api-mlgraphbuilder-binary"><
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②①">implementation-defined</a> platform operator for the binary operation <var>op</var>, given <var>a</var> and <var>b</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①①">platform operator</a> for the binary operation <var>op</var>, given <var>a</var> and <var>b</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-slot⑦">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②②">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand⑨">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot②①">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -3643,7 +3644,7 @@ <h4 class="heading settled" data-level="7.7.11" id="api-mlgraphbuilder-binary"><
       <li data-md>
        <p><a data-link-type="dfn" href="https://infra.spec.whatwg.org/#assert" id="ref-for-assert④">Assert</a>: The type of <var>shape1</var> and <var>shape2</var> is <code>sequence of unsigned long</code>.</p>
       <li data-md>
-       <p>Let <var>output</var> be the result of invoking the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②③">implementation-defined</a> shape broadcast on <var>shape1</var> and <var>shape2</var>.</p>
+       <p>Let <var>output</var> be the result of invoking the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤">implementation-defined</a> shape broadcast on <var>shape1</var> and <var>shape2</var>.</p>
        <ol>
         <li data-md>
          <p>If that fails, then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw⑨⑤">throw</a> a "<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#dataerror" id="ref-for-dataerror③⓪">DataError</a></code>" <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMException" id="ref-for-idl-DOMException⑥①">DOMException</a></code>.</p>
@@ -3753,24 +3754,24 @@ <h4 class="heading settled" data-level="7.7.11" id="api-mlgraphbuilder-binary"><
    <h4 class="heading settled" data-level="7.7.12" id="api-mlgraphbuilder-logical"><span class="secno">7.7.12. </span><span class="content">Element-wise logical operations</span><a class="self-link" href="#api-mlgraphbuilder-logical"></a></h4>
     Compare input tensors element-wise and return a uint8 tensor of values 0 or 1 for the comparisons. For single-operand operations, return the logical results of the operation. 
    <p>The input tensor 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>. The <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⓪">rank</a> of the output tensor is the maximum <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①①">rank</a> 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-mlgraphbuilder②⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand⑨⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-equal" id="ref-for-dom-mlgraphbuilder-equal"><c- g>equal</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/equal(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-equal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-equal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/equal(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-equal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-equal-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-greater" id="ref-for-dom-mlgraphbuilder-greater"><c- g>greater</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greater(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greater-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greater-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greater(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greater-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greater-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-greaterorequal" id="ref-for-dom-mlgraphbuilder-greaterorequal"><c- g>greaterOrEqual</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greaterOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greaterorequal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greaterorequal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greaterOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greaterorequal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greaterorequal-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lesser" id="ref-for-dom-mlgraphbuilder-lesser"><c- g>lesser</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesser(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesser-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesser-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesser(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesser-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesser-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lesserorequal" id="ref-for-dom-mlgraphbuilder-lesserorequal"><c- g>lesserOrEqual</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesserOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesserorequal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesserorequal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesserOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesserorequal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesserorequal-a-b-b"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-not" id="ref-for-dom-mlgraphbuilder-not"><c- g>not</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/not(a)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-not-a-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-not-a-a"></a></dfn>);
+<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-mlgraphbuilder②⑧"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-equal" id="ref-for-dom-mlgraphbuilder-equal"><c- g>equal</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/equal(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-equal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-equal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/equal(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-equal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-equal-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-greater" id="ref-for-dom-mlgraphbuilder-greater"><c- g>greater</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greater(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greater-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greater-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greater(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greater-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greater-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-greaterorequal" id="ref-for-dom-mlgraphbuilder-greaterorequal"><c- g>greaterOrEqual</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greaterOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greaterorequal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greaterorequal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⓪⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/greaterOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-greaterorequal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-greaterorequal-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lesser" id="ref-for-dom-mlgraphbuilder-lesser"><c- g>lesser</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesser(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesser-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesser-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesser(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesser-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesser-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-lesserorequal" id="ref-for-dom-mlgraphbuilder-lesserorequal"><c- g>lesserOrEqual</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesserOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesserorequal-a-b-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesserorequal-a-b-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/lesserOrEqual(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-lesserorequal-a-b-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-lesserorequal-a-b-b"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-not" id="ref-for-dom-mlgraphbuilder-not"><c- g>not</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①①⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/not(a)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-not-a-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-not-a-a"></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-mloperand①①⑥">MLOperand</a></code>. The first input tensor.</p>
+      <p><em>a</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①⑧">MLOperand</a></code>. The first input tensor.</p>
      <li data-md>
-      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①⑦">MLOperand</a></code>. The second input tensor when specified.</p>
+      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①⑨">MLOperand</a></code>. The second input tensor when specified.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①⑧">MLOperand</a></code>. The output tensor that contains the result of element-wise comparison of the two input tensors.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①②⓪">MLOperand</a></code>. The output tensor that contains the result of element-wise comparison of the two input tensors.</p>
    </div>
    <div>
      <strong>Operation types:</strong> 
@@ -3791,7 +3792,7 @@ <h4 class="heading settled" data-level="7.7.12" id="api-mlgraphbuilder-logical">
    </div>
    <div class="note" role="note"> Although operations <em>greaterOrEqual</em> and <em>lesserOrEqual</em> can each be implemented in terms of operations <em>not</em>, <em>lesser</em>, and <em>greater</em> in other words <code>greater-or-equal(a, b)</code> is <code>not(lesser(a, b))</code>, they are specifically defined to handle NaN cases and for performance reason to avoid double comparisons. </div>
    <details class="algorithm" data-algorithm="element-wise-logical-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-logical-op" data-noexport id="mlgraphbuilder-element-wise-logical-op">create element-wise logical operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①①⑨">MLOperand</a></code> <var>a</var> and an optional <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①②⓪">MLOperand</a></code> <var>b</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-logical-op" data-noexport id="mlgraphbuilder-element-wise-logical-op">create element-wise logical operation</dfn> given <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①②①">MLOperand</a></code> <var>a</var> and an optional <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①②②">MLOperand</a></code> <var>b</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -3827,11 +3828,11 @@ <h4 class="heading settled" data-level="7.7.12" id="api-mlgraphbuilder-logical">
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②④">implementation-defined</a> platform operator for the binary operation <var>op</var>, given <var>a</var> and <var>b</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①②">platform operator</a> for the binary operation <var>op</var>, given <var>a</var> and <var>b</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-slot⑧">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⓪">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot②⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -3930,29 +3931,29 @@ <h4 class="heading settled" data-level="7.7.12" id="api-mlgraphbuilder-logical">
    </details>
    <h4 class="heading settled" data-level="7.7.13" id="api-mlgraphbuilder-unary"><span class="secno">7.7.13. </span><span class="content">Element-wise unary operations</span><a class="self-link" href="#api-mlgraphbuilder-unary"></a></h4>
     Compute the element-wise unary operation for 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-mlgraphbuilder②⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-abs" id="ref-for-dom-mlgraphbuilder-abs"><c- g>abs</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/abs(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-abs-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-abs-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-ceil" id="ref-for-dom-mlgraphbuilder-ceil"><c- g>ceil</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/ceil(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-ceil-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-ceil-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-cos" id="ref-for-dom-mlgraphbuilder-cos"><c- g>cos</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cos(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cos-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cos-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-erf" id="ref-for-dom-mlgraphbuilder-erf"><c- g>erf</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/erf(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-erf-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-erf-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-exp" id="ref-for-dom-mlgraphbuilder-exp"><c- g>exp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/exp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-exp-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-exp-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-floor" id="ref-for-dom-mlgraphbuilder-floor"><c- g>floor</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/floor(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-floor-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-floor-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-identity" id="ref-for-dom-mlgraphbuilder-identity"><c- g>identity</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/identity(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-identity-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-identity-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-log" id="ref-for-dom-mlgraphbuilder-log"><c- g>log</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/log(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-log-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-log-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-neg" id="ref-for-dom-mlgraphbuilder-neg"><c- g>neg</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/neg(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-neg-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-neg-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reciprocal" id="ref-for-dom-mlgraphbuilder-reciprocal"><c- g>reciprocal</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reciprocal(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reciprocal-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reciprocal-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sin" id="ref-for-dom-mlgraphbuilder-sin"><c- g>sin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sin-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sin-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sqrt" id="ref-for-dom-mlgraphbuilder-sqrt"><c- g>sqrt</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sqrt(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sqrt-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sqrt-input-input"></a></dfn>);
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-tan" id="ref-for-dom-mlgraphbuilder-tan"><c- g>tan</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/tan(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-tan-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-tan-input-input"></a></dfn>);
+<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-mlgraphbuilder②⑨"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-abs" id="ref-for-dom-mlgraphbuilder-abs"><c- g>abs</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/abs(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-abs-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-abs-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-ceil" id="ref-for-dom-mlgraphbuilder-ceil"><c- g>ceil</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/ceil(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-ceil-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-ceil-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-cos" id="ref-for-dom-mlgraphbuilder-cos"><c- g>cos</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/cos(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-cos-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-cos-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①②⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-erf" id="ref-for-dom-mlgraphbuilder-erf"><c- g>erf</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/erf(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-erf-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-erf-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-exp" id="ref-for-dom-mlgraphbuilder-exp"><c- g>exp</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/exp(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-exp-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-exp-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-floor" id="ref-for-dom-mlgraphbuilder-floor"><c- g>floor</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/floor(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-floor-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-floor-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-identity" id="ref-for-dom-mlgraphbuilder-identity"><c- g>identity</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/identity(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-identity-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-identity-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-log" id="ref-for-dom-mlgraphbuilder-log"><c- g>log</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/log(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-log-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-log-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①③⑨"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-neg" id="ref-for-dom-mlgraphbuilder-neg"><c- g>neg</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/neg(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-neg-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-neg-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④①"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-reciprocal" id="ref-for-dom-mlgraphbuilder-reciprocal"><c- g>reciprocal</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/reciprocal(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-reciprocal-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-reciprocal-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sin" id="ref-for-dom-mlgraphbuilder-sin"><c- g>sin</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④④"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sin(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sin-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sin-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-sqrt" id="ref-for-dom-mlgraphbuilder-sqrt"><c- g>sqrt</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/sqrt(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-sqrt-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-sqrt-input-input"></a></dfn>);
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-tan" id="ref-for-dom-mlgraphbuilder-tan"><c- g>tan</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①④⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/tan(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-tan-input-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-tan-input-input"></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-mloperand①④⑦">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-mloperand①④⑨">MLOperand</a></code>. The input tensor.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①④⑧">MLOperand</a></code>. The output tensor that contains the result of
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤⓪">MLOperand</a></code>. The output tensor that contains the result of
     element-wise unary operation of the input tensor. The shape of the output
     tensor is the same as the shape of input tensor.</p>
    </div>
@@ -3988,7 +3989,7 @@ <h4 class="heading settled" data-level="7.7.13" id="api-mlgraphbuilder-unary"><s
     </ul>
    </div>
    <details class="algorithm" data-algorithm="element-wise-unary-op" data-algorithm-for="MLGraphBuilder" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-unary-op" data-noexport id="mlgraphbuilder-element-wise-unary-op">create element-wise unary operation</dfn> given <var>op</var> and <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①④⑨">MLOperand</a></code> <var>input</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLGraphBuilder" data-dfn-type="dfn" data-lt="element-wise-unary-op" data-noexport id="mlgraphbuilder-element-wise-unary-op">create element-wise unary operation</dfn> given <var>op</var> and <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤①">MLOperand</a></code> <var>input</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -4002,11 +4003,11 @@ <h4 class="heading settled" data-level="7.7.13" id="api-mlgraphbuilder-unary"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⑥">implementation-defined</a> platform operator for the unary operation <var>op</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①③">platform operator</a> for the unary operation <var>op</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-slot⑨">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①①">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot②⑨">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -4200,9 +4201,9 @@ <h4 class="heading settled" data-level="7.7.14" id="api-mlgraphbuilder-elu"><spa
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float①③"><c- b>float</c-></a> <dfn class="idl-code" data-default="1" data-dfn-for="MLEluOptions" data-dfn-type="dict-member" data-export data-type="float " id="dom-mleluoptions-alpha"><code><c- g>alpha</c-></code><a class="self-link" href="#dom-mleluoptions-alpha"></a></dfn> = 1;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder②⑨"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-elu" id="ref-for-dom-mlgraphbuilder-elu"><c- g>elu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(input, options), MLGraphBuilder/elu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mleluoptions" id="ref-for-dictdef-mleluoptions"><c- n>MLEluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(input, options), MLGraphBuilder/elu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-input-options-options"></a></dfn> = {});
-  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②①"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-elu-options" id="ref-for-dom-mlgraphbuilder-elu-options"><c- g>elu</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mleluoptions" id="ref-for-dictdef-mleluoptions①"><c- n>MLEluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(options), MLGraphBuilder/elu()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-options-options"></a></dfn> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③⓪"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤②"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-elu" id="ref-for-dom-mlgraphbuilder-elu"><c- g>elu</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤③"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(input, options), MLGraphBuilder/elu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-input-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-input-options-input"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mleluoptions" id="ref-for-dictdef-mleluoptions"><c- n>MLEluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(input, options), MLGraphBuilder/elu(input)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-input-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-input-options-options"></a></dfn> = {});
+  <a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②②"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-elu-options" id="ref-for-dom-mlgraphbuilder-elu-options"><c- g>elu</c-></a>(<c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mleluoptions" id="ref-for-dictdef-mleluoptions①"><c- n>MLEluOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/elu(options), MLGraphBuilder/elu()" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-elu-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-elu-options-options"></a></dfn> = {});
 };
 </pre>
    <div class="note" role="note">
@@ -4226,7 +4227,7 @@ <h5 class="heading settled" data-level="7.7.14.1" id="api-mlgraphbuilder-elu-inp
      <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-mloperand①⑤②">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-mloperand①⑤④">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-mleluoptions" id="ref-for-dictdef-mleluoptions②">MLEluOptions</a></code>. The optional parameters of the operation.</p>
       <ul>
@@ -4237,7 +4238,7 @@ <h5 class="heading settled" data-level="7.7.14.1" id="api-mlgraphbuilder-elu-inp
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤③">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-mloperand①⑤⑤">MLOperand</a></code>. The output tensor of the same shape as <em>input</em>.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="elu(input, options)" data-algorithm-for="MLGraphBuilder" open>
@@ -4253,11 +4254,11 @@ <h5 class="heading settled" data-level="7.7.14.1" id="api-mlgraphbuilder-elu-inp
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⑧">implementation-defined</a> platform operator for the ELU operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①④">platform operator</a> for the ELU 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-slot①⓪">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined②⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①②">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot③②">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -4285,7 +4286,7 @@ <h5 class="heading settled" data-level="7.7.14.2" id="api-mlgraphbuilder-elu-opt
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②②">MLActivation</a></code>. The activation function representing the elu operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②③">MLActivation</a></code>. The activation function representing the elu operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="elu(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -4301,19 +4302,19 @@ <h5 class="heading settled" data-level="7.7.14.2" id="api-mlgraphbuilder-elu-opt
    </details>
    <h4 class="heading settled" data-level="7.7.15" id="api-mlgraphbuilder-expand"><span class="secno">7.7.15. </span><span class="content">expand</span><a class="self-link" href="#api-mlgraphbuilder-expand"></a></h4>
     Expand any dimension of size 1 of the input tensor to a larger size according to the new shape. The expansion is consistent with <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>. The input dimensions must have the size of 1 or match the sizes of the corresponding output dimensions according to the new shape. 
-<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-mlgraphbuilder③⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-expand" id="ref-for-dom-mlgraphbuilder-expand"><c- g>expand</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⑤"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/expand(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-expand-input-newshape-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-expand-input-newshape-input"></a></dfn>, <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⓪"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②⑨"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/expand(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-expand-input-newshape-newshape"><code><c- g>newShape</c-></code><a class="self-link" href="#dom-mlgraphbuilder-expand-input-newshape-newshape"></a></dfn>);
+<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-mlgraphbuilder③①"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-expand" id="ref-for-dom-mlgraphbuilder-expand"><c- g>expand</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/expand(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-expand-input-newshape-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-expand-input-newshape-input"></a></dfn>, <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⓪"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long②⑨"><c- b>unsigned</c-> <c- b>long</c-></a>> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/expand(input, newShape)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-expand-input-newshape-newshape"><code><c- g>newShape</c-></code><a class="self-link" href="#dom-mlgraphbuilder-expand-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-mloperand①⑤⑥">MLOperand</a></code>. An input tensor</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤⑧">MLOperand</a></code>. An 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-long③⓪">unsigned long</a></code>. The new shape the input tensor is expanded to.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤⑦">MLOperand</a></code>. The tensor with expanded size dimensions.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑤⑨">MLOperand</a></code>. The tensor with expanded size dimensions.</p>
    </div>
    <details class="algorithm" data-algorithm="expand(input, newShape)" 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-expand"><code>expand(<var>input</var>, <var>newShape</var>)</code></dfn> method steps are: </summary>
@@ -4349,11 +4350,11 @@ <h4 class="heading settled" data-level="7.7.15" id="api-mlgraphbuilder-expand"><
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⓪">implementation-defined</a> platform operator <var>expandImpl</var> for this method, given <var>input</var> and <var>newShape</var>.</p>
+           <p>Create <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⑤">platform operator</a> <var>expandImpl</var> for this method, given <var>input</var> and <var>newShape</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-slot①①">[[operator]]</a></code> to <var>expandImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③①">implementation-defined</a> platform operand <var>outputImpl</var> to represent output,given <var>output</var> and <var>expandImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①③">platform operand</a> <var>outputImpl</var> to represent output,given <var>output</var> and <var>expandImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot③⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -4373,8 +4374,8 @@ <h4 class="heading settled" data-level="7.7.16" id="api-mlgraphbuilder-gather"><
   <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> <a class="idl-code" data-default="0" data-link-type="dict-member" data-type="unsigned long " href="#dom-mlgatheroptions-axis" id="ref-for-dom-mlgatheroptions-axis"><c- g>axis</c-></a> = 0;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③①"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⑧"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gather" id="ref-for-dom-mlgraphbuilder-gather"><c- g>gather</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑤⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-indices"><code><c- g>indices</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-indices"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgatheroptions" id="ref-for-dictdef-mlgatheroptions"><c- n>MLGatherOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-options"></a></dfn> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③②"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⓪"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gather" id="ref-for-dom-mlgraphbuilder-gather"><c- g>gather</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥②"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-indices"><code><c- g>indices</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-indices"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgatheroptions" id="ref-for-dictdef-mlgatheroptions"><c- n>MLGatherOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gather(input, indices, options), MLGraphBuilder/gather(input, indices)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gather-input-indices-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gather-input-indices-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlgatheroptions" id="ref-for-dictdef-mlgatheroptions①">MLGatherOptions</a></code> has the following members:</p>
@@ -4387,13 +4388,13 @@ <h4 class="heading settled" data-level="7.7.16" id="api-mlgraphbuilder-gather"><
      <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-mloperand①⑥①">MLOperand</a></code>. The input N-D tensor from which the values are gathered.</p>
+      <p><em>input</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑥③">MLOperand</a></code>. The input N-D tensor from which the values are gathered.</p>
      <li data-md>
-      <p><em>indices</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑥②">MLOperand</a></code>. The indices N-D tensor of the input values to gather. The values must be of type <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-uint32" id="ref-for-dom-mloperanddatatype-uint32">"uint32"</a></code> or <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int64" id="ref-for-dom-mloperanddatatype-int64②">"int64"</a></code> in the range [0, N-1] where N is the size of the input dimension indexed by <em>options.axis</em>.</p>
+      <p><em>indices</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑥④">MLOperand</a></code>. The indices N-D tensor of the input values to gather. The values must be of type <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-uint32" id="ref-for-dom-mloperanddatatype-uint32">"uint32"</a></code> or <code class="idl"><a data-link-type="idl" href="#dom-mloperanddatatype-int64" id="ref-for-dom-mloperanddatatype-int64②">"int64"</a></code> in the range [0, N-1] where N is the size of the input dimension indexed by <em>options.axis</em>.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlgatheroptions" id="ref-for-dictdef-mlgatheroptions②">MLGatherOptions</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-mloperand①⑥③">MLOperand</a></code>. The output N-D tensor of <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑤">rank</a> equal to the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑥">rank</a> of <em>input</em> + the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑦">rank</a> of <em>indices</em> - 1.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑥⑤">MLOperand</a></code>. The output N-D tensor of <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑤">rank</a> equal to the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑥">rank</a> of <em>input</em> + the <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank①⑦">rank</a> of <em>indices</em> - 1.</p>
    </div>
    <details class="algorithm" data-algorithm="gather(input, indices, 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="gather(input, indices, options)|gather(input, indices)" id="dom-mlgraphbuilder-gather"><code>gather(<var>input</var>, <var>indices</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -4474,11 +4475,11 @@ <h4 class="heading settled" data-level="7.7.16" id="api-mlgraphbuilder-gather"><
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③②">implementation-defined</a> platform operator for the Gather operation, given <var>input</var>, <var>indices</var>, and <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⑥">platform operator</a> for the Gather operation, given <var>input</var>, <var>indices</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-slot①②">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③③">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①④">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot③⑦">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -4547,22 +4548,22 @@ <h4 class="heading settled" data-level="7.7.16" id="api-mlgraphbuilder-gather"><
    <h4 class="heading settled" data-level="7.7.17" id="api-mlgraphbuilder-gemm"><span class="secno">7.7.17. </span><span class="content">gemm</span><a class="self-link" href="#api-mlgraphbuilder-gemm"></a></h4>
     Calculate the <a href="https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3">general matrix multiplication of the Basic Linear Algebra Subprograms</a>. The calculation follows the expression <code>alpha * A * B + beta * C</code>, where <code>A</code> is a 2-D tensor with shape [M, K] or [K, M], <code>B</code> is a 2-D tensor with shape [K, N] or [N, K], and <code>C</code> is broadcastable to the shape [M, N]. <code>A</code> and <code>B</code> may optionally be transposed prior to the calculation. 
 <pre class="idl highlight def"><c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlgemmoptions"><code><c- g>MLGemmOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgemmoptions-c" id="ref-for-dom-mlgemmoptions-c"><c- g>c</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgemmoptions-c" id="ref-for-dom-mlgemmoptions-c"><c- g>c</c-></a>;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float①⑥"><c- b>float</c-></a> <a class="idl-code" data-default="1.0" data-link-type="dict-member" data-type="float " href="#dom-mlgemmoptions-alpha" id="ref-for-dom-mlgemmoptions-alpha"><c- g>alpha</c-></a> = 1.0;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float①⑦"><c- b>float</c-></a> <a class="idl-code" data-default="1.0" data-link-type="dict-member" data-type="float " href="#dom-mlgemmoptions-beta" id="ref-for-dom-mlgemmoptions-beta"><c- g>beta</c-></a> = 1.0;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean⑧"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mlgemmoptions-atranspose" id="ref-for-dom-mlgemmoptions-atranspose"><c- g>aTranspose</c-></a> = <c- b>false</c->;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean⑨"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mlgemmoptions-btranspose" id="ref-for-dom-mlgemmoptions-btranspose"><c- g>bTranspose</c-></a> = <c- b>false</c->;
 };
 
-<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder③②"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gemm" id="ref-for-dom-mlgraphbuilder-gemm①"><c- g>gemm</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑥"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-b"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgemmoptions" id="ref-for-dictdef-mlgemmoptions"><c- n>MLGemmOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-options"></a></dfn> = {});
+<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-mloperand①⑥⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gemm" id="ref-for-dom-mlgraphbuilder-gemm②"><c- g>gemm</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-a"><code><c- g>a</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-a"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-b"><code><c- g>b</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-b"></a></dfn>, <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgemmoptions" id="ref-for-dictdef-mlgemmoptions"><c- n>MLGemmOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gemm(a, b, options), MLGraphBuilder/gemm(a, b)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gemm-a-b-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gemm-a-b-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlgemmoptions" id="ref-for-dictdef-mlgemmoptions①">MLGemmOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGemmOptions" data-dfn-type="dict-member" data-export id="dom-mlgemmoptions-c"><code>c</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑥⑧">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGemmOptions" data-dfn-type="dict-member" data-export id="dom-mlgemmoptions-c"><code>c</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⓪">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑥⑨">MLOperand</a></code>. Specifies the third input tensor. It is either a scalar, or of the shape that is unidirectionally broadcastable to the shape [M, N] according to <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>. When it is not specified, the computation is done as if <em>c</em> is a scalar 0.0.</p>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑦①">MLOperand</a></code>. Specifies the third input tensor. It is either a scalar, or of the shape that is unidirectionally broadcastable to the shape [M, N] according to <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>. When it is not specified, the computation is done as if <em>c</em> is a scalar 0.0.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGemmOptions" data-dfn-type="dict-member" data-export id="dom-mlgemmoptions-alpha"><code>alpha</code></dfn>, <span> of type <a data-link-type="idl-name" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float①⑧">float</a>, defaulting to <code>1.0</code></span>
     <dd data-md>
      <p>A <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float①⑨">float</a></code> scalar multiplier for the first input.</p>
@@ -4580,13 +4581,13 @@ <h4 class="heading settled" data-level="7.7.17" id="api-mlgraphbuilder-gemm"><sp
      <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-mloperand①⑦⓪">MLOperand</a></code>. The first input 2-D tensor with shape [M, K] if <em>aTranspose</em> is false, or [K, M] if <em>aTranspose</em> is true.</p>
+      <p><em>a</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑦②">MLOperand</a></code>. The first input 2-D tensor with shape [M, K] if <em>aTranspose</em> is false, or [K, M] if <em>aTranspose</em> is true.</p>
      <li data-md>
-      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑦①">MLOperand</a></code>. The second input 2-D tensor with shape [K, N] if <em>bTranspose</em> is false, or [N, K] if <em>bTranspose</em> is true.</p>
+      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑦③">MLOperand</a></code>. The second input 2-D tensor with shape [K, N] if <em>bTranspose</em> is false, or [N, K] if <em>bTranspose</em> is true.</p>
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlgemmoptions" id="ref-for-dictdef-mlgemmoptions②">MLGemmOptions</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-mloperand①⑦②">MLOperand</a></code>. The output 2-D tensor of shape [M, N] that contains the calculated product of all the inputs.</p>
+    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑦④">MLOperand</a></code>. The output 2-D tensor of shape [M, N] that contains the calculated product of all the inputs.</p>
    </div>
    <details class="algorithm" data-algorithm="gemm(a, b, 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="gemm(a, b, options)|gemm(a, b)" id="dom-mlgraphbuilder-gemm"><code>gemm(<var>a</var>, <var>b</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -4622,11 +4623,11 @@ <h4 class="heading settled" data-level="7.7.17" id="api-mlgraphbuilder-gemm"><sp
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③④">implementation-defined</a> platform operator for the GEMM operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⑦">platform operator</a> for the GEMM 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-slot①③">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⑤">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot④①">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -4668,33 +4669,33 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
 };
 
 <c- b>dictionary</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="dictionary" data-export id="dictdef-mlgruoptions"><code><c- g>MLGruOptions</c-></code></dfn> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦③"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-bias" id="ref-for-dom-mlgruoptions-bias"><c- g>bias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦④"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-recurrentbias" id="ref-for-dom-mlgruoptions-recurrentbias"><c- g>recurrentBias</c-></a>;
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-initialhiddenstate" id="ref-for-dom-mlgruoptions-initialhiddenstate"><c- g>initialHiddenState</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑤"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-bias" id="ref-for-dom-mlgruoptions-bias"><c- g>bias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑥"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-recurrentbias" id="ref-for-dom-mlgruoptions-recurrentbias"><c- g>recurrentBias</c-></a>;
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑦"><c- n>MLOperand</c-></a> <a class="idl-code" data-link-type="dict-member" data-type="MLOperand " href="#dom-mlgruoptions-initialhiddenstate" id="ref-for-dom-mlgruoptions-initialhiddenstate"><c- g>initialHiddenState</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-mlgruoptions-resetafter" id="ref-for-dom-mlgruoptions-resetafter"><c- g>resetAfter</c-></a> = <c- b>true</c->;
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean①⑤"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mlgruoptions-returnsequence" id="ref-for-dom-mlgruoptions-returnsequence"><c- g>returnSequence</c-></a> = <c- b>false</c->;
   <a data-link-type="idl-name" href="#enumdef-mlrecurrentnetworkdirection" id="ref-for-enumdef-mlrecurrentnetworkdirection"><c- n>MLRecurrentNetworkDirection</c-></a> <a class="idl-code" data-default="&quot;forward&quot;" data-link-type="dict-member" data-type="MLRecurrentNetworkDirection " href="#dom-mlgruoptions-direction" id="ref-for-dom-mlgruoptions-direction"><c- g>direction</c-></a> = "forward";
   <a data-link-type="idl-name" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout"><c- n>MLGruWeightLayout</c-></a> <a class="idl-code" data-default="&quot;zrn&quot;" data-link-type="dict-member" data-type="MLGruWeightLayout " href="#dom-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout"><c- g>layout</c-></a> = "zrn";
-  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①①"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②③"><c- n>MLActivation</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<MLActivation> " href="#dom-mlgruoptions-activations" id="ref-for-dom-mlgruoptions-activations"><c- g>activations</c-></a>;
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①①"><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-mlgruoptions-activations" id="ref-for-dom-mlgruoptions-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="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①②"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑥"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gru" id="ref-for-dom-mlgraphbuilder-gru"><c- g>gru</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑦"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑧"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-weight"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"></a></dfn>,
+<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="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①②"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑧"><c- n>MLOperand</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mlgraphbuilder-gru" id="ref-for-dom-mlgraphbuilder-gru"><c- g>gru</c-></a>(<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑦⑨"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-input"><code><c- g>input</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-input"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧⓪"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-weight"><code><c- g>weight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-weight"></a></dfn>, <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧①"><c- n>MLOperand</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"><code><c- g>recurrentWeight</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-recurrentweight"></a></dfn>,
                           <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long③④"><c- b>unsigned</c-> <c- b>long</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-steps"><code><c- g>steps</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-steps"></a></dfn>, <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/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-hiddensize"><code><c- g>hiddenSize</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-hiddensize"></a></dfn>,
                           <c- b>optional</c-> <a data-link-type="idl-name" href="#dictdef-mlgruoptions" id="ref-for-dictdef-mlgruoptions"><c- n>MLGruOptions</c-></a> <dfn class="idl-code" data-dfn-for="MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize, options), MLGraphBuilder/gru(input, weight, recurrentWeight, steps, hiddenSize)" data-dfn-type="argument" data-export id="dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-options"><code><c- g>options</c-></code><a class="self-link" href="#dom-mlgraphbuilder-gru-input-weight-recurrentweight-steps-hiddensize-options-options"></a></dfn> = {});
 };
 </pre>
    <p><code class="idl"><a data-link-type="idl" href="#dictdef-mlgruoptions" id="ref-for-dictdef-mlgruoptions①">MLGruOptions</a></code> has the following members:</p>
    <dl>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧⓪">MLOperand</a></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-bias"><code>bias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧②">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧①">MLOperand</a></code>. Specifies the 2-D input bias tensor of shape [numDirections, 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-layout①">layout</a></code> argument.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧②">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧③">MLOperand</a></code>. Specifies the 2-D input bias tensor of shape [numDirections, 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-layout①">layout</a></code> argument.</p>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-recurrentbias"><code>recurrentBias</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧④">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧③">MLOperand</a></code>. Specifies the 2-D recurrent bias tensor of shape [numDirections, 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-layout②">layout</a></code> argument.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-initialhiddenstate"><code>initialHiddenState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧④">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧⑤">MLOperand</a></code>. Specifies the 2-D recurrent bias tensor of shape [numDirections, 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-layout②">layout</a></code> argument.</p>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-initialhiddenstate"><code>initialHiddenState</code></dfn>, <span> of type <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑧⑥">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧⑤">MLOperand</a></code>. The 3-D initial hidden state tensor of shape [numDirections, batchSize, hiddenSize].
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧⑦">MLOperand</a></code>. 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="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-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-boolean①⑥">boolean</a>, defaulting to <code>true</code></span>
     <dd data-md>
@@ -4709,19 +4710,19 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-layout"><code>layout</code></dfn>, <span> of type <a data-link-type="idl-name" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout①">MLGruWeightLayout</a>, defaulting to <code>"zrn"</code></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout②">MLGruWeightLayout</a></code>. The ordering of the weight and bias vectors for the internal gates of GRU, specifically the <code>update (z)</code>, <code>reset (r)</code>, and <code>new (n)</code> gate, as indicated in the second dimension of the weight and bias tensor shape. When not specified, the default layout is <code class="idl"><a data-link-type="idl" href="#dom-mlgruweightlayout-zrn" id="ref-for-dom-mlgruweightlayout-zrn">"zrn"</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②④">MLActivation</a>></span>
+    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruOptions" data-dfn-type="dict-member" data-export id="dom-mlgruoptions-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑤">MLActivation</a>></span>
     <dd data-md>
-     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②⑤">MLActivation</a></code>. Specifies a pair of activation functions with the first function used for the update and reset gate, and the second used for the new gate. When not specified, implementations SHOULD use the the pair of sigmoid ("sigmoid") and the hyperbolic tangent ("tanh") functions, respectively.</p>
+     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②⑥">MLActivation</a></code>. Specifies a pair of activation functions with the first function used for the update and reset gate, and the second used for the new gate. When not specified, implementations SHOULD use the the pair of sigmoid ("sigmoid") and the hyperbolic tangent ("tanh") functions, respectively.</p>
    </dl>
    <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-mloperand①⑧⑥">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-mloperand①⑧⑧">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-mloperand①⑧⑦">MLOperand</a></code>. The 3-D input weight tensor of shape [numDirections, 3 * 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-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout③">layout</a></code> argument.</p>
+      <p><em>weight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧⑨">MLOperand</a></code>. The 3-D input weight tensor of shape [numDirections, 3 * 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-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout③">layout</a></code> argument.</p>
      <li data-md>
-      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑧⑧">MLOperand</a></code>. The 3-D recurrent weight tensor of shape [numDirections, 3 * 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-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-layout④">layout</a></code> argument.</p>
+      <p><em>recurrentWeight</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑨⓪">MLOperand</a></code>. The 3-D recurrent weight tensor of shape [numDirections, 3 * 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-mlgruoptions-layout" id="ref-for-dom-mlgruoptions-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-long③⑥">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>
@@ -4729,7 +4730,7 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
      <li data-md>
       <p><em>options</em>: an optional <code class="idl"><a data-link-type="idl" href="#dictdef-mlgruoptions" id="ref-for-dictdef-mlgruoptions②">MLGruOptions</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-mloperand①⑧⑨">MLOperand</a></code>. The first element of the sequence is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the cell output from the last time step of the network. Additionally, if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-returnsequence" id="ref-for-dom-mlgruoptions-returnsequence①">returnSequence</a></code> is set to true, the second element is the 4-D output tensor of shape [steps, numDirections, batchSize, hiddenSize] containing every cell outputs 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-mloperand①⑨①">MLOperand</a></code>. The first element of the sequence is a 3-D tensor of shape [numDirections, batchSize, hiddenSize], the cell output from the last time step of the network. Additionally, if <var>options</var>.<code class="idl"><a data-link-type="idl" href="#dom-mlgruoptions-returnsequence" id="ref-for-dom-mlgruoptions-returnsequence①">returnSequence</a></code> is set to true, the second element is the 4-D output tensor of shape [steps, numDirections, batchSize, hiddenSize] containing every cell outputs from each time step in the temporal sequence.</p>
    </div>
    <details class="algorithm" data-algorithm="gru(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="gru(input, weight, recurrentWeight, steps, hiddenSize, options)|gru(input, weight, recurrentWeight, steps, hiddenSize)" id="dom-mlgraphbuilder-gru"><code>gru(<var>input</var>, <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var>, <var>options</var>)</code></dfn> method steps are: </summary>
@@ -4760,7 +4761,7 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
       <li data-md>
        <p>If <var>steps</var> is not equal to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤④">[[descriptor]]</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions④⑨">dimensions</a></code>[0], then <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑦②">throw</a> a <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#exceptiondef-typeerror" id="ref-for-exceptiondef-typeerror③①">TypeError</a></code>.</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-mloperand①⑨⓪">MLOperand</a></code> objects.</p>
+       <p>Let <var>output</var> be an empty sequence of <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑨②">MLOperand</a></code> objects.</p>
       <li data-md>
        <p>If any of the following sub-steps fail, <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-throw" id="ref-for-dfn-throw①⑦③">throw</a> an "<code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#operationerror" id="ref-for-operationerror②⑧">OperationError</a></code>" <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-DOMException" id="ref-for-idl-DOMException⑧③">DOMException</a></code>.</p>
        <ol>
@@ -4768,7 +4769,7 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⑥">implementation-defined</a> platform operator for "gru", given <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var> and <var>options</var> as parameters.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⑧">platform operator</a> for "gru", given <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var> and <var>options</var> as parameters.</p>
          </ol>
         <li data-md>
          <p>Connect <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot④⑤">[[operand]]</a></code> as input to <var>opImpl</var>.</p>
@@ -4848,54 +4849,54 @@ <h4 class="heading settled" data-level="7.7.18" id="api-mlgraphbuilder-gru"><spa
    <h4 class="heading settled" data-level="7.7.19" id="api-mlgraphbuilder-grucell"><span class="secno">7.7.19. </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-mloperand①⑨①"><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-mloperand①⑨②"><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-mloperand①⑨③"><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-mloperand①⑨④"><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-boolean②⓪"><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-mlgruweightlayout③"><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-sequence①③"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑥"><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>;
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①③"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑦"><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-mlgraphbuilder③④"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand①⑨③"><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-mloperand①⑨④"><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-mloperand①⑨⑤"><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-mloperand①⑨⑥"><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-mloperand①⑨⑦"><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="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/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>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-mloperand①⑨⑤"><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-mloperand①⑨⑥"><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-mloperand①⑨⑦"><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-mloperand①⑨⑧"><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-mloperand①⑨⑨"><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="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/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-mloperand①⑨⑧">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-mloperand②⓪⓪">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand①⑨⑨">MLOperand</a></code>. Specifies 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-mloperand②⓪⓪">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⓪①">MLOperand</a></code>. Specifies 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-mloperand②⓪②">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⓪①">MLOperand</a></code>. Specifies 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>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⓪③">MLOperand</a></code>. Specifies 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-boolean②①">boolean</a>, defaulting to <code>true</code></span>
     <dd data-md>
      <p>A <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean②②">boolean</a></code> indicating whether to apply the reset gate after or before matrix multiplication. The default value is true.</p>
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-layout"><code>layout</code></dfn>, <span> of type <a data-link-type="idl-name" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout④">MLGruWeightLayout</a>, defaulting to <code>"zrn"</code></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="#enumdef-mlgruweightlayout" id="ref-for-enumdef-mlgruweightlayout⑤">MLGruWeightLayout</a></code>. The ordering of the weight and bias vectors for the internal gates of GRU, specifically the <code>update (z)</code>, <code>reset (r)</code>, and <code>new (n)</code> gate, as indicated in the second dimension of the weight and bias tensor shape. When not specified, the default layout is <code class="idl"><a data-link-type="idl" href="#dom-mlgruweightlayout-zrn" id="ref-for-dom-mlgruweightlayout-zrn①">"zrn"</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLGruCellOptions" data-dfn-type="dict-member" data-export id="dom-mlgrucelloptions-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑦">MLActivation</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-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation②⑧">MLActivation</a>></span>
     <dd data-md>
-     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②⑧">MLActivation</a></code>. Specifies a pair of activation functions with the first function used for the update and reset gate, and the second used for the new gate. When not specified, implementations SHOULD use the the pair of sigmoid ("sigmoid") and the hyperbolic tangent ("tanh") functions, respectively.</p>
+     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation②⑨">MLActivation</a></code>. Specifies a pair of activation functions with the first function used for the update and reset gate, and the second used for the new gate. When not specified, implementations SHOULD use the the pair of sigmoid ("sigmoid") and the hyperbolic tangent ("tanh") functions, respectively.</p>
    </dl>
    <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-mloperand②⓪②">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-mloperand②⓪④">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-mloperand②⓪③">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-mloperand②⓪⑤">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-mloperand②⓪④">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-mloperand②⓪⑥">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-mloperand②⓪⑤">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-mloperand②⓪⑦">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-long③⑨">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-mloperand②⓪⑥">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-mloperand②⓪⑧">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>
@@ -4936,11 +4937,11 @@ <h4 class="heading settled" data-level="7.7.19" id="api-mlgraphbuilder-grucell">
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⑦">implementation-defined</a> platform operator for "gruCell", given <var>weight</var>, <var>recurrentWeight</var>, <var>hiddenState</var>, <var>hiddenSize</var> and <var>options</var> as parameters.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator①⑨">platform 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-slot①④">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⑧">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⑥">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot④⑥">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5058,9 +5059,9 @@ <h4 class="heading settled" data-level="7.7.20" id="api-mlgraphbuilder-hard-sigm
   <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="0.5" data-link-type="dict-member" data-type="float " href="#dom-mlhardsigmoidoptions-beta" id="ref-for-dom-mlhardsigmoidoptions-beta"><c- g>beta</c-></a> = 0.5;
 };
 
-<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-mloperand②⓪⑦"><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-mloperand②⓪⑧"><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-mlactivation②⑨"><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> = {});
+<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-mloperand②⓪⑨"><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-mloperand②①⓪"><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-mlactivation③⓪"><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>
    <div class="note" role="note">
@@ -5095,14 +5096,14 @@ <h5 class="heading settled" data-level="7.7.20.1" 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-mloperand②⓪⑨">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-mloperand②①①">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-mloperand②①⓪">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-mloperand②①②">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>
@@ -5118,11 +5119,11 @@ <h5 class="heading settled" data-level="7.7.20.1" id="api-mlgraphbuilder-hardsig
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined③⑨">implementation-defined</a> platform operator for the hard sigmoid operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⓪">platform 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-slot①⑤">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⓪">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⑦">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot④⑨">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5146,7 +5147,7 @@ <h5 class="heading settled" data-level="7.7.20.2" id="api-mlgraphbuilder-hardsig
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③⓪">MLActivation</a></code>. The activation function representing the hard sigmoid operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③①">MLActivation</a></code>. The activation function representing the hard sigmoid operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="hardSigmoid(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -5166,9 +5167,9 @@ <h5 class="heading settled" data-level="7.7.20.2" id="api-mlgraphbuilder-hardsig
    </details>
    <h4 class="heading settled" data-level="7.7.21" id="api-mlgraphbuilder-hard-swish"><span class="secno">7.7.21. </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-mlgraphbuilder③⑥"><c- g>MLGraphBuilder</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="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-mloperand②①②"><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-mlactivation③①"><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 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-mlgraphbuilder③⑦"><c- g>MLGraphBuilder</c-></a> {
+  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①③"><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-mloperand②①④"><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-mlactivation③②"><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>
    <div class="note" role="note">
@@ -5194,12 +5195,12 @@ <h5 class="heading settled" data-level="7.7.21.1" 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-mloperand②①③">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-mloperand②①⑤">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-mloperand②①④">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-mloperand②①⑥">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>
@@ -5215,11 +5216,11 @@ <h5 class="heading settled" data-level="7.7.21.1" id="api-mlgraphbuilder-hardswi
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④①">implementation-defined</a> platform operator for the hard-swish operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②①">platform 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-slot①⑥">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④②">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⑧">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑤②">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5243,7 +5244,7 @@ <h5 class="heading settled" data-level="7.7.21.2" id="api-mlgraphbuilder-hardswi
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③②">MLActivation</a></code>. The activation function representing the hard-swish operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③③">MLActivation</a></code>. The activation function representing the hard-swish operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="hardSwish()" data-algorithm-for="MLGraphBuilder" open>
@@ -5262,7 +5263,7 @@ <h5 class="heading settled" data-level="7.7.21.2" id="api-mlgraphbuilder-hardswi
     </div>
    </details>
    <h4 class="heading settled" data-level="7.7.22" id="api-mlgraphbuilder-input"><span class="secno">7.7.22. </span><span class="content">input</span><a class="self-link" href="#api-mlgraphbuilder-input"></a></h4>
-    Create a named <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑤">MLOperand</a></code> based on a descriptor, that can be used as an input. 
+    Create a named <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑦">MLOperand</a></code> based on a descriptor, that can be used as an input. 
    <div>
      <strong>Arguments:</strong> 
     <ul>
@@ -5271,7 +5272,7 @@ <h4 class="heading settled" data-level="7.7.22" id="api-mlgraphbuilder-input"><s
      <li data-md>
       <p><em>descriptor</em>: an <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor①⑦">MLOperandDescriptor</a></code> object.</p>
     </ul>
-     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑥">MLOperand</a></code> object. 
+     <strong>Returns:</strong>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②①⑧">MLOperand</a></code> object. 
    </div>
    <details class="algorithm" data-algorithm="input(name, descriptor)" 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-input"><code>input(<var>name</var>, <var>descriptor</var>)</code></dfn> method steps are: </summary>
@@ -5301,7 +5302,7 @@ <h4 class="heading settled" data-level="7.7.22" id="api-mlgraphbuilder-input"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④③">implementation-defined</a> platform input operand <var>operandImpl</var> given <var>descriptor</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥">implementation-defined</a> platform input operand <var>operandImpl</var> given <var>descriptor</var>.</p>
           <li data-md>
            <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑤⑤">[[operand]]</a></code> to <var>operandImpl</var>.</p>
           <li data-md>
@@ -5316,25 +5317,25 @@ <h4 class="heading settled" data-level="7.7.22" id="api-mlgraphbuilder-input"><s
    <h4 class="heading settled" data-level="7.7.23" id="api-mlgraphbuilder-instancenorm"><span class="secno">7.7.23. </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 <a href="#api-mlgraphbuilder-batchnorm">§ 7.7.3 batchNormalization</a> 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-mloperand②①⑦"><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-mloperand②①⑧"><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-mloperand②①⑨"><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-mloperand②②⓪"><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-float②⑧"><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-mlinputoperandlayout⑥"><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-mlgraphbuilder③⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②①⑨"><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-mloperand②②⓪"><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>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-mloperand②②①"><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-mloperand②②②"><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>The <code class="idl"><a data-link-type="idl" href="#dictdef-mlinstancenormalizationoptions" id="ref-for-dictdef-mlinstancenormalizationoptions①">MLInstanceNormalizationOptions</a></code> members are:</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-mloperand②②①">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-mloperand②②③">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②②">MLOperand</a></code>. Specifies 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-size②⑦">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-size②⑧">size</a> is equal to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤⑧">[[descriptor]]</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions⑤⑦">dimensions</a></code>[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-mloperand②②③">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②④">MLOperand</a></code>. Specifies 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-size②⑦">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-size②⑧">size</a> is equal to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤⑧">[[descriptor]]</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions⑤⑦">dimensions</a></code>[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-mloperand②②⑤">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②④">MLOperand</a></code>. Specifies 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-size②⑨">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-size③⓪">size</a> is equal to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤⑨">[[descriptor]]</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions⑤⑧">dimensions</a></code>[1].</p>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②②⑥">MLOperand</a></code>. Specifies 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-size②⑨">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-size③⓪">size</a> is equal to <var>input</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-descriptor-slot" id="ref-for-dom-mloperand-descriptor-slot⑤⑨">[[descriptor]]</a></code>.<code class="idl"><a data-link-type="idl" href="#dom-mloperanddescriptor-dimensions" id="ref-for-dom-mloperanddescriptor-dimensions⑤⑧">dimensions</a></code>[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-float②⑨">float</a>, defaulting to <code>1e-5</code></span>
     <dd data-md>
      <p>A <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float③⓪">float</a></code> scalar. Specifies a small value to prevent computational error due to divide-by-zero.</p>
@@ -5346,11 +5347,11 @@ <h4 class="heading settled" data-level="7.7.23" 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-mloperand②②⑤">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-mloperand②②⑦">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-mloperand②②⑥">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-mloperand②②⑧">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>
@@ -5371,11 +5372,11 @@ <h4 class="heading settled" data-level="7.7.23" id="api-mlgraphbuilder-instancen
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④④">implementation-defined</a> platform operator for the instance normalization operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②②">platform 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-slot①⑦">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand①⑨">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑤⑥">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5424,24 +5425,24 @@ <h4 class="heading settled" data-level="7.7.23" id="api-mlgraphbuilder-instancen
    <h4 class="heading settled" data-level="7.7.24" id="api-mlgraphbuilder-layernorm"><span class="secno">7.7.24. </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 <a href="#api-mlgraphbuilder-batchnorm">§ 7.7.3 batchNormalization</a> where the mean and variance values are computed across all the samples in the batch dimension while the model is trained, and in <a href="#api-mlgraphbuilder-instancenorm">§ 7.7.23 instanceNormalization</a> 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-mloperand②②⑦"><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-mloperand②②⑧"><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-mloperand②②⑨"><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-mloperand②③⓪"><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-sequence①④"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⓪"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<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-float③①"><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-mlgraphbuilder③⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②②⑨"><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-mloperand②③⓪"><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> = {});
+<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-mloperand②③①"><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-mloperand②③②"><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>The <code class="idl"><a data-link-type="idl" href="#dictdef-mllayernormalizationoptions" id="ref-for-dictdef-mllayernormalizationoptions①">MLLayerNormalizationOptions</a></code> members are:</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-mloperand②③①">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-mloperand②③③">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③②">MLOperand</a></code>. Specifies 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-mloperand②③③">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③④">MLOperand</a></code>. Specifies 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-mloperand②③⑤">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③④">MLOperand</a></code>. Specifies 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>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②③⑥">MLOperand</a></code>. Specifies 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;unsigned long></code></span>
     <dd data-md>
      <p>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-long④①">unsigned long</a></code>. The indices to the input dimensions to reduce. When this member is not present, it is assumed to be [1,2,3] that is, the reduction for the mean and variance values are calculated across all the input features for each individual sample in the batch.</p>
@@ -5453,11 +5454,11 @@ <h4 class="heading settled" data-level="7.7.24" 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-mloperand②③⑤">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-mloperand②③⑦">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-mloperand②③⑥">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-mloperand②③⑧">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>
@@ -5490,11 +5491,11 @@ <h4 class="heading settled" data-level="7.7.24" id="api-mlgraphbuilder-layernorm
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⑥">implementation-defined</a> platform operator for the instance normalization operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②③">platform 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>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⓪">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑤⑨">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5545,9 +5546,9 @@ <h4 class="heading settled" data-level="7.7.25" id="api-mlgraphbuilder-leakyrelu
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float③④"><c- b>float</c-></a> <a class="idl-code" data-default="0.01" data-link-type="dict-member" data-type="float " href="#dom-mlleakyreluoptions-alpha" id="ref-for-dom-mlleakyreluoptions-alpha"><c- g>alpha</c-></a> = 0.01;
 };
 
-<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-mloperand②③⑦"><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-mloperand②③⑧"><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-mlactivation③③"><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> = {});
+<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="idl-name" href="#mloperand" id="ref-for-mloperand②③⑨"><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-mloperand②④⓪"><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-mlactivation③④"><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>
    <div class="note" role="note">
@@ -5573,14 +5574,14 @@ <h5 class="heading settled" data-level="7.7.25.1" 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-mloperand②③⑨">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-mloperand②④①">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-mloperand②④⓪">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-mloperand②④②">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>
@@ -5596,11 +5597,11 @@ <h5 class="heading settled" data-level="7.7.25.1" id="api-mlgraphbuilder-leaky-r
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⑧">implementation-defined</a> platform operator for the Leaky RELU operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②④">platform 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-slot①⑨">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined④⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②①">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑥②">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5624,7 +5625,7 @@ <h5 class="heading settled" data-level="7.7.25.2" id="api-mlgraphbuilder-leaky-r
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③④">MLActivation</a></code>. The activation function representing the leaky relu operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③⑤">MLActivation</a></code>. The activation function representing the leaky relu operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="leakyRelu(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -5649,9 +5650,9 @@ <h4 class="heading settled" data-level="7.7.26" id="api-mlgraphbuilder-linear"><
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float③⑧"><c- b>float</c-></a> <a class="idl-code" data-default="0" data-link-type="dict-member" data-type="float " href="#dom-mllinearoptions-beta" id="ref-for-dom-mllinearoptions-beta"><c- g>beta</c-></a> = 0;
 };
 
-<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="idl-name" href="#mloperand" id="ref-for-mloperand②④①"><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-mloperand②④②"><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-mlactivation③⑤"><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> = {});
+<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="idl-name" href="#mloperand" id="ref-for-mloperand②④③"><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-mloperand②④④"><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-mlactivation③⑥"><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>
    <div class="note" role="note">
@@ -5682,14 +5683,14 @@ <h5 class="heading settled" data-level="7.7.26.1" 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-mloperand②④③">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-mloperand②④⑤">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-mloperand②④④">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-mloperand②④⑥">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>
@@ -5705,11 +5706,11 @@ <h5 class="heading settled" data-level="7.7.26.1" id="api-mlgraphbuilder-linear-
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⓪">implementation-defined</a> platform operator for the linear operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⑤">platform 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-slot②⓪">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤①">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②②">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑥⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -5733,7 +5734,7 @@ <h5 class="heading settled" data-level="7.7.26.2" id="api-mlgraphbuilder-linear-
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③⑥">MLActivation</a></code>. The activation function representing the linear operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③⑦">MLActivation</a></code>. The activation function representing the linear operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="linear(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -5759,40 +5760,40 @@ <h4 class="heading settled" data-level="7.7.27" 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-mloperand②④⑤"><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-mloperand②④⑥"><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-mloperand②④⑦"><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-mloperand②④⑧"><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-mloperand②④⑨"><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-mloperand②④⑦"><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-mloperand②④⑧"><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-mloperand②④⑨"><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-mloperand②⑤⓪"><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-mloperand②⑤①"><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-boolean②③"><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-mlrecurrentnetworkdirection③"><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";
-  <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-mlactivation③⑦"><c- n>MLActivation</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<MLActivation> " href="#dom-mllstmoptions-activations" id="ref-for-dom-mllstmoptions-activations"><c- g>activations</c-></a>;
+  <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-mlactivation③⑧"><c- n>MLActivation</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<MLActivation> " href="#dom-mllstmoptions-activations" id="ref-for-dom-mllstmoptions-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-sequence①⑥"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑤⓪"><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-mloperand②⑤①"><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-mloperand②⑤②"><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-mloperand②⑤③"><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>,
+<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="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑥"><c- b>sequence</c-></a>&lt;<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-lstm" id="ref-for-dom-mlgraphbuilder-lstm"><c- g>lstm</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/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-mloperand②⑤④"><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-mloperand②⑤⑤"><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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④②"><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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④③"><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> = {});
 };
 </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-mloperand②⑤④">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-mloperand②⑤⑥">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑤">MLOperand</a></code>. Specifies 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-mloperand②⑤⑥">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑦">MLOperand</a></code>. Specifies 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-mloperand②⑤⑧">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑦">MLOperand</a></code>. Specifies 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-mloperand②⑤⑧">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑨">MLOperand</a></code>. Specifies 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-mloperand②⑥⓪">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑤⑨">MLOperand</a></code>. Specifies 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-mloperand②⑥⓪">MLOperand</a></span>
-    <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑥①">MLOperand</a></code>. Specifies 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-mloperand②⑥②">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑥①">MLOperand</a></code>. Specifies 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-mloperand②⑥②">MLOperand</a></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑥③">MLOperand</a></code>. Specifies 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-mloperand②⑥④">MLOperand</a></span>
+    <dd data-md>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑥⑤">MLOperand</a></code>. Specifies 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-boolean②④">boolean</a>, defaulting to <code>false</code></span>
     <dd data-md>
      <p>A <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean②⑤">boolean</a></code> indicating whether to also return the entire sequence with every output from each time step in it in addition to the output of the last time step.</p>
@@ -5802,19 +5803,19 @@ <h4 class="heading settled" data-level="7.7.27" id="api-mlgraphbuilder-lstm"><sp
     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmOptions" data-dfn-type="dict-member" data-export id="dom-mllstmoptions-layout"><code>layout</code></dfn>, <span> of type <a data-link-type="idl-name" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout①">MLLstmWeightLayout</a>, defaulting to <code>"iofg"</code></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout②">MLLstmWeightLayout</a></code>. The ordering of the weight and bias vectors for the internal gates of LSTM, specifically the <code>input (i)</code>, <code>output (o)</code>, <code>forget (f)</code>, and <code>cell (g)</code> gate, as indicated in the first dimension of the weight and bias tensor shapes. When not specified, the default layout is <code class="idl"><a data-link-type="idl" href="#dom-mllstmweightlayout-iofg" id="ref-for-dom-mllstmweightlayout-iofg">"iofg"</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-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③⑧">MLActivation</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-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation③⑨">MLActivation</a>></span>
     <dd data-md>
-     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation③⑨">MLActivation</a></code>. A sequence of three activation functions, the first one is used for the <code>input (i)</code>, <code>forget (f)</code>, and <code>output (o)</code> gate, the second one is used for the <code>cell (g)</code> gate, and the last used for filtering the output cell state before combining it with the result of the output gate to form the output hidden state. When not specified, implementations SHOULD use the sequence of the sigmoid function ("sigmoid") followed by two hyperbolic tangent functions ("tanh") respectively.</p>
+     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⓪">MLActivation</a></code>. A sequence of three activation functions, the first one is used for the <code>input (i)</code>, <code>forget (f)</code>, and <code>output (o)</code> gate, the second one is used for the <code>cell (g)</code> gate, and the last used for filtering the output cell state before combining it with the result of the output gate to form the output hidden state. When not specified, implementations SHOULD use the sequence of the sigmoid function ("sigmoid") followed by two hyperbolic tangent functions ("tanh") respectively.</p>
    </dl>
    <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-mloperand②⑥④">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-mloperand②⑥⑥">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-mloperand②⑥⑤">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-mloperand②⑥⑦">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-mloperand②⑥⑥">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-mloperand②⑥⑧">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-long④④">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>
@@ -5822,7 +5823,7 @@ <h4 class="heading settled" data-level="7.7.27" 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-mloperand②⑥⑦">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-mloperand②⑥⑨">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>
@@ -5924,11 +5925,11 @@ <h4 class="heading settled" data-level="7.7.27" id="api-mlgraphbuilder-lstm"><sp
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤②">implementation-defined</a> platform operator for the LSTM operation, given <var>weight</var>, <var>recurrentWeight</var>, <var>steps</var>, <var>hiddenSize</var> and <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⑥">platform 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>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operator-slot" id="ref-for-dom-mloperand-operator-slot②①">[[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-slot②②">[[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-slot②③">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤③">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②③">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑥⑧">[[operand]]</a></code>, <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑥⑨">[[operand]]</a></code> and <var>output2</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦⓪">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6026,56 +6027,56 @@ <h4 class="heading settled" data-level="7.7.27" id="api-mlgraphbuilder-lstm"><sp
    <h4 class="heading settled" data-level="7.7.28" id="api-mlgraphbuilder-lstmcell"><span class="secno">7.7.28. </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-mloperand②⑥⑧"><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-mloperand②⑥⑨"><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-mloperand②⑦⓪"><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-mloperand②⑦⓪"><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-mloperand②⑦①"><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-mloperand②⑦②"><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-mllstmweightlayout③"><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-sequence①⑦"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④⓪"><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>;
+  <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence①⑦"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④①"><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-mlgraphbuilder④②"><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-mloperand②⑦①"><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-mloperand②⑦②"><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-mloperand②⑦③"><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-mloperand②⑦④"><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-mloperand②⑦⑤"><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-mloperand②⑦⑥"><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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⑥"><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>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="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-mloperand②⑦③"><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-mloperand②⑦④"><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-mloperand②⑦⑤"><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-mloperand②⑦⑥"><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-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-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-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-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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⑥"><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-mloperand②⑦⑦">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-mloperand②⑦⑨">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑦⑧">MLOperand</a></code>. 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-mloperand②⑦⑨">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⓪">MLOperand</a></code>. 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-mloperand②⑧①">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧⓪">MLOperand</a></code>. 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-mloperand②⑧①">MLOperand</a></span>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧②">MLOperand</a></code>. 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-mloperand②⑧③">MLOperand</a></span>
     <dd data-md>
-     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧②">MLOperand</a></code>. 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>
+     <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑧④">MLOperand</a></code>. 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-mllstmweightlayout④">MLLstmWeightLayout</a>, defaulting to <code>"iofg"</code></span>
     <dd data-md>
      <p>An <code class="idl"><a data-link-type="idl" href="#enumdef-mllstmweightlayout" id="ref-for-enumdef-mllstmweightlayout⑤">MLLstmWeightLayout</a></code>. The ordering of the weight and bias vectors for the internal gates of LSTM, specifically the <code>input (i)</code>, <code>output (o)</code>, <code>forget (f)</code>, and <code>cell (g)</code> gate, as indicated in the first dimension of the weight and bias tensor shapes. When not specified, the default layout is <code class="idl"><a data-link-type="idl" href="#dom-mllstmweightlayout-iofg" id="ref-for-dom-mllstmweightlayout-iofg①">"iofg"</a></code>.</p>
-    <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLLstmCellOptions" data-dfn-type="dict-member" data-export id="dom-mllstmcelloptions-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④①">MLActivation</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-activations"><code>activations</code></dfn>, <span> of type sequence&lt;<a data-link-type="idl-name" href="#mlactivation" id="ref-for-mlactivation④②">MLActivation</a>></span>
     <dd data-md>
-     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④②">MLActivation</a></code>. A sequence of three activation functions, the first one is used for the <code>input (i)</code>, <code>forget (f)</code>, and <code>output (o)</code> gate, the second one is used for the <code>cell (g)</code> gate, and the last used for filtering the output cell state before combining it with the result of the output gate to form the output hidden state. When not specified, they are assumed to be of the sigmoid function ("sigmoid") followed by two hyperbolic tangent functions ("tanh") respectively.</p>
+     <p>A sequence of <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④③">MLActivation</a></code>. A sequence of three activation functions, the first one is used for the <code>input (i)</code>, <code>forget (f)</code>, and <code>output (o)</code> gate, the second one is used for the <code>cell (g)</code> gate, and the last used for filtering the output cell state before combining it with the result of the output gate to form the output hidden state. When not specified, they are assumed to be of the sigmoid function ("sigmoid") followed by two hyperbolic tangent functions ("tanh") respectively.</p>
    </dl>
    <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-mloperand②⑧③">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-mloperand②⑧④">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-mloperand②⑧⑤">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-mloperand②⑧⑥">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-mloperand②⑧⑦">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-long④⑦">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-mloperand②⑧⑧">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>
@@ -6134,11 +6135,11 @@ <h4 class="heading settled" data-level="7.7.28" id="api-mlgraphbuilder-lstmcell"
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤④">implementation-defined</a> platform operator 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>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⑦">platform 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-slot②④">[[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-slot②⑤">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②④">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output0</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦②">[[operand]]</a></code> and <var>output1</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦③">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6267,19 +6268,19 @@ <h4 class="heading settled" data-level="7.7.28" id="api-mlgraphbuilder-lstmcell"
    </div>
    <h4 class="heading settled" data-level="7.7.29" id="api-mlgraphbuilder-matmul"><span class="secno">7.7.29. </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-mlgraphbuilder④③"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand②⑧⑨"><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-mloperand②⑨⓪"><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-mloperand②⑨①"><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 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-mlgraphbuilder④④"><c- g>MLGraphBuilder</c-></a> {
+  <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-mloperand②⑨②">MLOperand</a></code>. The first N-dimensional input tensor.</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 N-dimensional input tensor.</p>
      <li data-md>
-      <p><em>b</em>: an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨③">MLOperand</a></code>. The second N-dimensional input tensor.</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 N-dimensional input tensor.</p>
     </ul>
-    <p><strong>Returns:</strong> an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand②⑨④">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>
@@ -6350,11 +6351,11 @@ <h4 class="heading settled" data-level="7.7.29" id="api-mlgraphbuilder-matmul"><
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⑥">implementation-defined</a> platform operator for the matrix multiplication operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⑧">platform 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-slot②⑥">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⑤">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6382,8 +6383,8 @@ <h4 class="heading settled" data-level="7.7.30" id="api-mlgraphbuilder-pad"><spa
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float④③"><c- b>float</c-></a> <a class="idl-code" data-default="0" data-link-type="dict-member" data-type="float " href="#dom-mlpadoptions-value" id="ref-for-dom-mlpadoptions-value"><c- g>value</c-></a> = 0;
 };
 
-<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-mloperand②⑨⑤"><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-mloperand②⑨⑥"><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>,
+<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-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-sequence①⑨"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long④⑧"><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-sequence②⓪"><c- b>sequence</c-></a>&lt;<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/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> = {});
@@ -6406,7 +6407,7 @@ <h4 class="heading settled" data-level="7.7.30" 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-mloperand②⑨⑦">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-long⑤⓪">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-rank③⑧">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>
@@ -6414,7 +6415,7 @@ <h4 class="heading settled" data-level="7.7.30" 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-mloperand②⑨⑧">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>
@@ -6455,11 +6456,11 @@ <h4 class="heading settled" data-level="7.7.30" id="api-mlgraphbuilder-pad"><spa
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⑧">implementation-defined</a> platform operator for the padding operation, given <var>beginningPadding</var>, <var>endingPadding</var> and <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator②⑨">platform 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-slot②⑦">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑤⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⑥">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑦⑨">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6532,10 +6533,10 @@ <h4 class="heading settled" data-level="7.7.31" id="api-mlgraphbuilder-pool2d"><
   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②⑤"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑤⑥"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<unsigned long> " href="#dom-mlpool2doptions-outputsizes" id="ref-for-dom-mlpool2doptions-outputsizes"><c- g>outputSizes</c-></a>;
 };
 
-<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-mloperand②⑨⑨"><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-mloperand③⓪⓪"><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-mloperand③⓪①"><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-mloperand③⓪②"><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-mloperand③⓪③"><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-mloperand③⓪④"><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> = {});
+<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-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>
@@ -6604,12 +6605,12 @@ <h4 class="heading settled" data-level="7.7.31" 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-mloperand③⓪⑤">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-mloperand③⓪⑥">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>
@@ -6623,7 +6624,7 @@ <h4 class="heading settled" data-level="7.7.31" id="api-mlgraphbuilder-pool2d"><
 </pre>
    </div>
    <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 <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⓪⑦">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 <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>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -6673,11 +6674,11 @@ <h4 class="heading settled" data-level="7.7.31" id="api-mlgraphbuilder-pool2d"><
           <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-this③⑦">this</a> and <var>desc</var>.</p>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⓪">implementation-defined</a> platform operator for the <var>op</var> pooling operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⓪">platform 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-slot②⑧">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥①">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⑦">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑧②">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6741,22 +6742,22 @@ <h5 class="heading settled" data-level="7.7.31.3" id="api-mlgraphbuilder-pool2d-
     Calculate the maximum value for patches of a feature map, and use it to create a pooled feature map. See <a href="#api-mlgraphbuilder-pool2d">§ 7.7.31 Pooling operations</a> for more detail. 
    <h4 class="heading settled" data-level="7.7.32" id="api-mlgraphbuilder-prelu"><span class="secno">7.7.32. </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-mlgraphbuilder④⑥"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⓪⑧"><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-mloperand③⓪⑨"><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-mloperand③①⓪"><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 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-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-mloperand③①①">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-mloperand③①②">MLOperand</a></code>. The slope tensor. Its shape is either the same as, or unidirectionally broadcastable to the shape of input tensor <em>input</em> according to <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>.</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 unidirectionally broadcastable to the shape of input tensor <em>input</em> according to <a data-link-type="biblio" href="#biblio-numpy-broadcasting-rule" title="General Broadcasting Rules of NumPy">[numpy-broadcasting-rule]</a>.</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-mloperand③①③">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>
@@ -6782,11 +6783,11 @@ <h4 class="heading settled" data-level="7.7.32" id="api-mlgraphbuilder-prelu"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥②">implementation-defined</a> platform operator for the PreLU operation, given <var>slope</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③①">platform 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-slot②⑨">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥③">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⑧">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑧⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -6818,17 +6819,17 @@ <h4 class="heading settled" data-level="7.7.33" id="api-mlgraphbuilder-reduce"><
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean②⑥"><c- b>boolean</c-></a> <a class="idl-code" data-default="false" data-link-type="dict-member" data-type="boolean " href="#dom-mlreduceoptions-keepdimensions" id="ref-for-dom-mlreduceoptions-keepdimensions①"><c- g>keepDimensions</c-></a> = <c- b>false</c->;
 };
 
-<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-mloperand③①④"><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-mloperand③①⑤"><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-mloperand③①⑥"><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-mloperand③①⑦"><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-mloperand③①⑧"><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-mloperand③①⑨"><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-mloperand③②⓪"><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-mloperand③②①"><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-mloperand③②②"><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-mloperand③②③"><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-mloperand③②④"><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-mloperand③②⑤"><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-mloperand③②⑥"><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-mloperand③②⑦"><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-mloperand③②⑧"><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-mloperand③②⑨"><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-mloperand③③⓪"><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-mloperand③③①"><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-mloperand③③②"><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-mloperand③③③"><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> = {});
+<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-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>
@@ -6844,11 +6845,11 @@ <h4 class="heading settled" data-level="7.7.33" 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-mloperand③③④">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-mloperand③③⑤">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> 
@@ -6876,7 +6877,7 @@ <h4 class="heading settled" data-level="7.7.33" id="api-mlgraphbuilder-reduce"><
     </ul>
    </div>
    <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 <var>op</var>, <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③③⑥">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 <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>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -6900,11 +6901,11 @@ <h4 class="heading settled" data-level="7.7.33" id="api-mlgraphbuilder-reduce"><
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥④">implementation-defined</a> platform operator for the <var>op</var> reduce operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③②">platform 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-slot③⓪">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand②⑨">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑧⑧">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7053,9 +7054,9 @@ <h4 class="heading settled" data-level="7.7.33" id="api-mlgraphbuilder-reduce"><
    </details>
    <h4 class="heading settled" data-level="7.7.34" id="api-mlgraphbuilder-relu-method"><span class="secno">7.7.34. </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-mlgraphbuilder④⑧"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③③⑦"><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-mloperand③③⑧"><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-mlactivation④③"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#relu-noargs" id="ref-for-relu-noargs"><c- g>relu</c-></a>();
+<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-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-mlactivation④④"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#relu-noargs" id="ref-for-relu-noargs"><c- g>relu</c-></a>();
 };
 </pre>
    <div class="note" role="note">
@@ -7073,12 +7074,12 @@ <h5 class="heading settled" data-level="7.7.34.1" 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-mloperand③③⑨">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-mloperand③④⓪">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>
@@ -7094,11 +7095,11 @@ <h5 class="heading settled" data-level="7.7.34.1" id="api-mlgraphbuilder-relu-in
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⑥">implementation-defined</a> platform operator for the ReLU operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③③">platform 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-slot③①">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⓪">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑨①">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7122,7 +7123,7 @@ <h5 class="heading settled" data-level="7.7.34.2" id="api-mlgraphbuilder-relu"><
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④④">MLActivation</a></code>. The activation function representing the relu operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⑤">MLActivation</a></code>. The activation function representing the relu operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="relu()" data-algorithm-for="MLGraphBuilder" open>
@@ -7154,19 +7155,19 @@ <h4 class="heading settled" data-level="7.7.35" id="api-mlgraphbuilder-resample2
   <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence②⑨"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥⑤"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<unsigned long> " href="#dom-mlresample2doptions-axes" id="ref-for-dom-mlresample2doptions-axes"><c- g>axes</c-></a>;
 };
 
-<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-mloperand③④①"><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-mloperand③④②"><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> = {});
+<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-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-mloperand③④③">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-mloperand③④④">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>
@@ -7210,7 +7211,7 @@ <h4 class="heading settled" data-level="7.7.35" id="api-mlgraphbuilder-resample2
     </div>
    </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-mloperand③④⑤">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>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -7247,11 +7248,11 @@ <h4 class="heading settled" data-level="7.7.35" id="api-mlgraphbuilder-resample2
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⑧">implementation-defined</a> platform operator for the resample 2D operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③④">platform 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-slot③②">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑥⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③①">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑨④">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7267,21 +7268,21 @@ <h4 class="heading settled" data-level="7.7.35" id="api-mlgraphbuilder-resample2
    </details>
    <h4 class="heading settled" data-level="7.7.36" id="api-mlgraphbuilder-reshape-method"><span class="secno">7.7.36. </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-mlgraphbuilder⑤⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③④⑥"><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-mloperand③④⑦"><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-sequence③⓪"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥⑧"><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 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-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-sequence③⓪"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥⑧"><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-mloperand③④⑧">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 <a data-link-type="dfn" href="https://webidl.spec.whatwg.org/#dfn-nullable-type" id="ref-for-dfn-nullable-type">nullable</a> <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑥⑨">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-mloperand③④⑨">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>
@@ -7318,11 +7319,11 @@ <h4 class="heading settled" data-level="7.7.36" id="api-mlgraphbuilder-reshape-m
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⓪">implementation-defined</a> platform operator for the reshape operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⑤">platform 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-slot③③">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦①">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③②">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot⑨⑦">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7371,9 +7372,9 @@ <h4 class="heading settled" data-level="7.7.36" id="api-mlgraphbuilder-reshape-m
    </div>
    <h4 class="heading settled" data-level="7.7.37" id="api-mlgraphbuilder-sigmoid-method"><span class="secno">7.7.37. </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-mlgraphbuilder⑤①"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⓪"><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-mloperand③⑤①"><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-mlactivation④⑤"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#sigmoid-noargs" id="ref-for-sigmoid-noargs"><c- g>sigmoid</c-></a>();
+<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-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-mlactivation④⑥"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#sigmoid-noargs" id="ref-for-sigmoid-noargs"><c- g>sigmoid</c-></a>();
 };
 </pre>
    <div class="note" role="note">
@@ -7395,12 +7396,12 @@ <h5 class="heading settled" data-level="7.7.37.1" 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-mloperand③⑤②">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-mloperand③⑤③">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>
@@ -7416,11 +7417,11 @@ <h5 class="heading settled" data-level="7.7.37.1" id="api-mlgraphbuilder-sigmoid
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦②">implementation-defined</a> platform operator for the sigmoid operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⑥">platform 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-slot③④">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦③">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③③">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⓪⓪">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7444,7 +7445,7 @@ <h5 class="heading settled" data-level="7.7.37.2" id="api-mlgraphbuilder-sigmoid
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⑥">MLActivation</a></code>. The activation function representing the sigmoid operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⑦">MLActivation</a></code>. The activation function representing the sigmoid operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="sigmoid()" data-algorithm-for="MLGraphBuilder" open>
@@ -7464,21 +7465,21 @@ <h5 class="heading settled" data-level="7.7.37.2" id="api-mlgraphbuilder-sigmoid
    </details>
    <h4 class="heading settled" data-level="7.7.38" id="api-mlgraphbuilder-slice"><span class="secno">7.7.38. </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-mlgraphbuilder⑤②"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤④"><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-mloperand③⑤⑤"><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-sequence③①"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦②"><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-sequence③②"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦③"><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>);
+<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-mlgraphbuilder⑤③"><c- g>MLGraphBuilder</c-></a> {
+  <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-sequence③①"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦②"><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-sequence③②"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦③"><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>);
 };
 </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-mloperand③⑤⑥">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-long⑦④">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-rank④④">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-long⑦⑤">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-rank④⑤">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-mloperand③⑤⑦">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>
@@ -7497,11 +7498,11 @@ <h4 class="heading settled" data-level="7.7.38" id="api-mlgraphbuilder-slice"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦④">implementation-defined</a> platform operator for the slice operation, given <var>starts</var> and <var>sizes</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⑦">platform 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-slot③⑤">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③④">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⓪③">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7518,9 +7519,9 @@ <h4 class="heading settled" data-level="7.7.38" id="api-mlgraphbuilder-slice"><s
    <h4 class="heading settled" data-level="7.7.39" id="api-mlgraphbuilder-softmax-method"><span class="secno">7.7.39. </span><span class="content">softmax</span><a class="self-link" href="#api-mlgraphbuilder-softmax-method"></a></h4>
     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-mlgraphbuilder⑤③"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑤⑧"><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-mloperand③⑤⑨"><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-mlactivation④⑦"><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 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-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-mlactivation④⑧"><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>
    <div class="note" role="note">
@@ -7545,12 +7546,12 @@ <h5 class="heading settled" data-level="7.7.39.1" 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-mloperand③⑥⓪">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-mloperand③⑥①">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>
@@ -7568,11 +7569,11 @@ <h5 class="heading settled" data-level="7.7.39.1" id="api-mlgraphbuilder-softmax
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⑥">implementation-defined</a> platform operator for the softmax operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⑧">platform 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-slot③⑥">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⑤">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⓪⑥">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7596,7 +7597,7 @@ <h5 class="heading settled" data-level="7.7.39.2" id="api-mlgraphbuilder-softmax
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⑧">MLActivation</a></code>. The activation function representing the softmax operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation④⑨">MLActivation</a></code>. The activation function representing the softmax operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softmax()" data-algorithm-for="MLGraphBuilder" open>
@@ -7620,9 +7621,9 @@ <h4 class="heading settled" data-level="7.7.40" id="api-mlgraphbuilder-softplus-
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-float" id="ref-for-idl-float④⑨"><c- b>float</c-></a> <a class="idl-code" data-default="1" data-link-type="dict-member" data-type="float " href="#dom-mlsoftplusoptions-steepness" id="ref-for-dom-mlsoftplusoptions-steepness"><c- g>steepness</c-></a> = 1;
 };
 
-<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-mloperand③⑥②"><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-mloperand③⑥③"><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-mlactivation④⑨"><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> = {});
+<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="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-mlactivation⑤⓪"><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>
    <div class="note" role="note">
@@ -7652,14 +7653,14 @@ <h5 class="heading settled" data-level="7.7.40.1" 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-mloperand③⑥④">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-mloperand③⑥⑤">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>
@@ -7675,11 +7676,11 @@ <h5 class="heading settled" data-level="7.7.40.1" id="api-mlgraphbuilder-softplu
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⑧">implementation-defined</a> platform operator for the softplus operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator③⑨">platform 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-slot③⑦">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑦⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⑥">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①⓪⑨">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7703,7 +7704,7 @@ <h5 class="heading settled" data-level="7.7.40.2" id="api-mlgraphbuilder-softplu
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤⓪">MLActivation</a></code>. The activation function representing the softplus operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤①">MLActivation</a></code>. The activation function representing the softplus operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softplus(options)" data-algorithm-for="MLGraphBuilder" open>
@@ -7723,9 +7724,9 @@ <h5 class="heading settled" data-level="7.7.40.2" id="api-mlgraphbuilder-softplu
    </details>
    <h4 class="heading settled" data-level="7.7.41" id="api-mlgraphbuilder-softsign-method"><span class="secno">7.7.41. </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-mlgraphbuilder⑤⑤"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑥⑥"><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-mloperand③⑥⑦"><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-mlactivation⑤①"><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 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-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-mlactivation⑤②"><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>
    <div class="note" role="note">
@@ -7743,12 +7744,12 @@ <h5 class="heading settled" data-level="7.7.41.1" 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-mloperand③⑥⑧">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-mloperand③⑥⑨">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>
@@ -7764,11 +7765,11 @@ <h5 class="heading settled" data-level="7.7.41.1" id="api-mlgraphbuilder-softsig
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⓪">implementation-defined</a> platform operator for the softsign operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④⓪">platform 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-slot③⑧">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧①">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⑦">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①①②">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7792,7 +7793,7 @@ <h5 class="heading settled" data-level="7.7.41.2" id="api-mlgraphbuilder-softsig
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤②">MLActivation</a></code>. The activation function representing the softsign operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤③">MLActivation</a></code>. The activation function representing the softsign operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="softsign()" data-algorithm-for="MLGraphBuilder" open>
@@ -7816,8 +7817,8 @@ <h4 class="heading settled" data-level="7.7.42" id="api-mlgraphbuilder-split"><s
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦⑥"><c- b>unsigned</c-> <c- b>long</c-></a> <a class="idl-code" data-default="0" data-link-type="dict-member" data-type="unsigned long " href="#dom-mlsplitoptions-axis" id="ref-for-dom-mlsplitoptions-axis"><c- g>axis</c-></a> = 0;
 };
 
-<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="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③③"><c- b>sequence</c-></a>&lt;<a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦⓪"><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-mloperand③⑦①"><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>,
+<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="dfn" href="https://webidl.spec.whatwg.org/#idl-sequence" id="ref-for-idl-sequence③③"><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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦⑦"><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-sequence③④"><c- b>sequence</c-></a>&lt;<a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑦⑧"><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> = {});
 };
@@ -7826,13 +7827,13 @@ <h4 class="heading settled" data-level="7.7.42" 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-mloperand③⑦②">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-long⑦⑨">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-long⑧⓪">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-long⑧①">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-long⑧②">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-mloperand③⑦③">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-long⑧③">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤③">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-long⑧④">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤④">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-size⑤⑤">size</a> of <em>splits</em>. The shape of the i-th output tensor is the same as 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-long⑧③">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤③">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-long⑧④">unsigned long</a></code>, the <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#list-size" id="ref-for-list-size⑤④">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-size⑤⑤">size</a> of <em>splits</em>. The shape of the i-th output tensor is the same as 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>
@@ -7858,11 +7859,11 @@ <h4 class="heading settled" data-level="7.7.42" id="api-mlgraphbuilder-split"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧②">implementation-defined</a> platform operator for the split operation, given <var>splits</var> and <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④①">platform operator</a> for the split operation, given <var>splits</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-slot③⑨">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧③">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⑧">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①①⑤">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7899,9 +7900,9 @@ <h4 class="heading settled" data-level="7.7.42" id="api-mlgraphbuilder-split"><s
    </div>
    <h4 class="heading settled" data-level="7.7.43" id="api-mlgraphbuilder-tanh-method"><span class="secno">7.7.43. </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-mlgraphbuilder⑤⑦"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑦④"><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-mloperand③⑦⑤"><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-mlactivation⑤③"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#tanh-noargs" id="ref-for-tanh-noargs"><c- g>tanh</c-></a>();
+<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-mlgraphbuilder⑤⑧"><c- g>MLGraphBuilder</c-></a> {
+  <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-mlactivation⑤④"><c- n>MLActivation</c-></a> <a class="idl-code" data-link-type="method" href="#tanh-noargs" id="ref-for-tanh-noargs"><c- g>tanh</c-></a>();
 };
 </pre>
    <div class="note" role="note">
@@ -7921,12 +7922,12 @@ <h5 class="heading settled" data-level="7.7.43.1" 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-mloperand③⑦⑥">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-mloperand③⑦⑦">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>
@@ -7942,11 +7943,11 @@ <h5 class="heading settled" data-level="7.7.43.1" id="api-mlgraphbuilder-tanh-in
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧④">implementation-defined</a> platform operator for the hyperbolic tangent operation.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④②">platform 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-slot④⓪">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⑤">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand③⑨">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①①⑧">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -7970,7 +7971,7 @@ <h5 class="heading settled" data-level="7.7.43.2" id="api-mlgraphbuilder-tanh"><
     <p><strong>Returns:</strong></p>
     <ul>
      <li data-md>
-      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤④">MLActivation</a></code>. The activation function representing the tanh operation.</p>
+      <p>an <code class="idl"><a data-link-type="idl" href="#mlactivation" id="ref-for-mlactivation⑤⑤">MLActivation</a></code>. The activation function representing the tanh operation.</p>
     </ul>
    </div>
    <details class="algorithm" data-algorithm="tanh()" data-algorithm-for="MLGraphBuilder" open>
@@ -7994,8 +7995,8 @@ <h4 class="heading settled" data-level="7.7.44" id="api-mlgraphbuilder-transpose
   <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 class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑧⑨"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="dict-member" data-type="sequence<unsigned long> " href="#dom-mltransposeoptions-permutation" id="ref-for-dom-mltransposeoptions-permutation"><c- g>permutation</c-></a>;
 };
 
-<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-mloperand③⑦⑧"><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-mloperand③⑦⑨"><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> = {});
+<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-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>
@@ -8011,11 +8012,11 @@ <h4 class="heading settled" data-level="7.7.44" 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-mloperand③⑧⓪">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-mloperand③⑧①">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>
@@ -8042,11 +8043,11 @@ <h4 class="heading settled" data-level="7.7.44" id="api-mlgraphbuilder-transpose
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⑥">implementation-defined</a> platform operator for the transpose operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④③">platform 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-slot④①">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⑦">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④⓪">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①②①">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -8067,8 +8068,8 @@ <h4 class="heading settled" data-level="7.7.45" id="api-mlgraphbuilder-triangula
   <a class="idl-code" data-link-type="interface" href="https://webidl.spec.whatwg.org/#idl-long" id="ref-for-idl-long"><c- b>long</c-></a> <a class="idl-code" data-default="0" data-link-type="dict-member" data-type="long " href="#dom-mltriangularoptions-diagonal" id="ref-for-dom-mltriangularoptions-diagonal"><c- g>diagonal</c-></a> = 0;
 };
 
-<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-mloperand③⑧②"><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-mloperand③⑧③"><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> = {});
+<c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥⓪"><c- g>MLGraphBuilder</c-></a> {
+  <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>
@@ -8084,11 +8085,11 @@ <h4 class="heading settled" data-level="7.7.45" 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-mloperand③⑧④">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-mloperand③⑧⑥">MLOperand</a></code>. The input 2-D tensor.</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-mloperand③⑧⑤">MLOperand</a></code>. The output 2-D tensor representing a triangular matrix.</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 2-D tensor representing a triangular matrix.</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>
@@ -8103,11 +8104,11 @@ <h4 class="heading settled" data-level="7.7.45" id="api-mlgraphbuilder-triangula
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⑧">implementation-defined</a> platform operator for the triangular operation, given <var>options</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④④">platform 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-slot④②">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑧⑨">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④①">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①②④">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -8174,21 +8175,21 @@ <h4 class="heading settled" data-level="7.7.46" id="api-mlgraphbuilder-where"><s
     Select the values from the input or the other tensor depending on the corresponding <code class="idl"><a data-link-type="idl" href="https://webidl.spec.whatwg.org/#idl-boolean" id="ref-for-idl-boolean③②">boolean</a></code> values of the condition tensor. The condition tensor is often the output of one of the element-wise logical operations. 
    <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-rank⑤③">rank</a> of the output tensor is the maximum <a data-link-type="dfn" href="#mloperand-rank" id="ref-for-mloperand-rank⑤④">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-mlgraphbuilder⑥⓪"><c- g>MLGraphBuilder</c-></a> {
-  <a data-link-type="idl-name" href="#mloperand" id="ref-for-mloperand③⑧⑥"><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-mloperand③⑧⑦"><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-mloperand③⑧⑧"><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-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-other"><code><c- g>other</c-></code><a class="self-link" href="#dom-mlgraphbuilder-where-condition-input-other-other"></a></dfn>);
+<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-mlgraphbuilder⑥①"><c- g>MLGraphBuilder</c-></a> {
+  <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-mloperand③⑨⓪">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-mloperand③⑨①">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-mloperand③⑨②">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-mloperand③⑨③">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>
@@ -8219,11 +8220,11 @@ <h4 class="heading settled" data-level="7.7.46" id="api-mlgraphbuilder-where"><s
          <p>Make a request to the underlying platform to:</p>
          <ol>
           <li data-md>
-           <p>Let <var>opImpl</var> be an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑨⓪">implementation-defined</a> platform operator for the where operation, given <var>condition</var>, <var>input</var> and <var>other</var>.</p>
+           <p>Let <var>opImpl</var> be <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④⑤">platform 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-slot④③">[[operator]]</a></code> to <var>opImpl</var>.</p>
           <li data-md>
-           <p>Create an <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑨①">implementation-defined</a> platform operand <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
+           <p>Create an <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④②">platform operand</a> <var>outputImpl</var> to represent the output, given <var>output</var> and <var>opImpl</var>.</p>
           <li data-md>
            <p>Set <var>output</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-operand-slot" id="ref-for-dom-mloperand-operand-slot①②⑦">[[operand]]</a></code> to <var>outputImpl</var>.</p>
          </ol>
@@ -8251,34 +8252,34 @@ <h4 class="heading settled" data-level="7.7.46" id="api-mlgraphbuilder-where"><s
 </pre>
     </details>
    </div>
-   <h3 class="heading settled" data-level="7.8" id="api-mloperand"><span class="secno">7.8. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨④">MLOperand</a></code> interface</span><a class="self-link" href="#api-mloperand"></a></h3>
-   <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑤">MLOperand</a></code> represents an intermediary graph being constructed as a result of compositing parts of an operation into a fully composed operation.</p>
-   <p>For instance, an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑥">MLOperand</a></code> may represent a constant feeding to an operation or the result from combining multiple constants together into an operation. See also <a href="#programming-model">§ 6 Programming Model</a>.</p>
+   <h3 class="heading settled" data-level="7.8" id="api-mloperand"><span class="secno">7.8. </span><span class="content"><code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑥">MLOperand</a></code> interface</span><a class="self-link" href="#api-mloperand"></a></h3>
+   <p>An <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑦">MLOperand</a></code> represents an intermediary graph being constructed as a result of compositing parts of an operation into a fully composed operation.</p>
+   <p>For instance, an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑧">MLOperand</a></code> may represent a constant feeding to an operation or the result from combining multiple constants together into an operation. See also <a href="#programming-model">§ 6 Programming Model</a>.</p>
 <pre class="idl highlight def">[<a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#SecureContext" id="ref-for-SecureContext⑦"><c- g>SecureContext</c-></a>, <a class="idl-code" data-link-type="extended-attribute" href="https://webidl.spec.whatwg.org/#Exposed" id="ref-for-Exposed①⓪"><c- g>Exposed</c-></a>=(<c- n>Window</c->, <c- n>DedicatedWorker</c->)]
 <c- b>interface</c-> <dfn class="dfn-paneled idl-code" data-dfn-type="interface" data-export id="mloperand"><code><c- g>MLOperand</c-></code></dfn> {};
 </pre>
    <div class="internal-slots">
-     <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑦">MLOperand</a></code> has the following internal slots: 
+     <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑨">MLOperand</a></code> has the following internal slots: 
     <dl>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-builder-slot"><code>[[builder]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥①">MLGraphBuilder</a></code>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-builder-slot"><code>[[builder]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥②">MLGraphBuilder</a></code>
      <dd data-md>
-      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑧">MLOperand</a></code>'s associated builder object.</p>
+      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⓪">MLOperand</a></code>'s associated builder object.</p>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-descriptor-slot"><code>[[descriptor]]</code></dfn> of type <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑤">MLOperandDescriptor</a></code>
      <dd data-md>
-      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand③⑨⑨">MLOperand</a></code>'s descriptor.</p>
+      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪①">MLOperand</a></code>'s descriptor.</p>
      <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-name-slot"><code>[[name]]</code></dfn> of type <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#string" id="ref-for-string①③">string</a>
      <dd data-md>
-      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⓪">MLOperand</a></code>'s name (only for input operands).</p>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-operand-slot"><code>[[operand]]</code></dfn> of type <a data-link-type="dfn" href="https://w3c.github.io/FileAPI/#blob-url-entry-object" id="ref-for-blob-url-entry-object②">object</a>
+      <p>The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪②">MLOperand</a></code>'s name (only for input operands).</p>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-operand-slot"><code>[[operand]]</code></dfn> of type <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④③">platform operand</a>
      <dd data-md>
-      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪①">MLOperand</a></code>'s corresponding <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑨②">implementation-defined</a> platform operand object.</p>
-     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-operator-slot"><code>[[operator]]</code></dfn> of type <a data-link-type="dfn" href="https://w3c.github.io/FileAPI/#blob-url-entry-object" id="ref-for-blob-url-entry-object③">object</a>
+      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪③">MLOperand</a></code>'s corresponding <a data-link-type="dfn" href="#platform-operand" id="ref-for-platform-operand④④">platform operand</a>.</p>
+     <dt data-md><dfn class="dfn-paneled idl-code" data-dfn-for="MLOperand" data-dfn-type="attribute" data-export id="dom-mloperand-operator-slot"><code>[[operator]]</code></dfn> of type <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④⑥">platform operator</a>
      <dd data-md>
-      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪②">MLOperand</a></code>'s corresponding <a data-link-type="dfn" href="https://infra.spec.whatwg.org/#implementation-defined" id="ref-for-implementation-defined⑨③">implementation-defined</a> platform operator object.</p>
+      <p>Reference to <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪④">MLOperand</a></code>'s corresponding <a data-link-type="dfn" href="#platform-operator" id="ref-for-platform-operator④⑦">platform operator</a>.</p>
     </dl>
    </div>
    <div class="algorithm" data-algorithm="rank" data-algorithm-for="MLOperand">
-     To get the <dfn class="dfn-paneled" data-dfn-for="MLOperand" data-dfn-type="dfn" data-noexport id="mloperand-rank">rank</dfn> of an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪③">MLOperand</a></code> <var>operand</var>, run the following steps: 
+     To get the <dfn class="dfn-paneled" data-dfn-for="MLOperand" data-dfn-type="dfn" data-noexport id="mloperand-rank">rank</dfn> of an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑤">MLOperand</a></code> <var>operand</var>, run the following steps: 
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -8286,15 +8287,15 @@ <h3 class="heading settled" data-level="7.8" id="api-mloperand"><span class="sec
      </ol>
     </div>
    </div>
-   <p>Since the <code class="idl"><a data-link-type="idl" href="#dom-mloperand-builder-slot" id="ref-for-dom-mloperand-builder-slot">[[builder]]</a></code> object is bound by the <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-mlgraphbuilder" id="ref-for-dom-mlgraphbuilder-mlgraphbuilder①">MLGraphBuilder()</a></code> constructor to an <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑨">MLContext</a></code> object, an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪④">MLOperand</a></code> is also always bound to the same <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext④⓪">MLContext</a></code> object.</p>
-   <h4 class="heading settled" data-level="7.8.1" id="api-mloperand-create"><span class="secno">7.8.1. </span><span class="content">Creating <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑤">MLOperand</a></code></span><a class="self-link" href="#api-mloperand-create"></a></h4>
-    The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑥">MLOperand</a></code> objects are created by the methods of <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥②">MLGraphBuilder</a></code>, internally using the following algorithms. 
+   <p>Since the <code class="idl"><a data-link-type="idl" href="#dom-mloperand-builder-slot" id="ref-for-dom-mloperand-builder-slot">[[builder]]</a></code> object is bound by the <code class="idl"><a data-link-type="idl" href="#dom-mlgraphbuilder-mlgraphbuilder" id="ref-for-dom-mlgraphbuilder-mlgraphbuilder①">MLGraphBuilder()</a></code> constructor to an <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext③⑨">MLContext</a></code> object, an <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑥">MLOperand</a></code> is also always bound to the same <code class="idl"><a data-link-type="idl" href="#mlcontext" id="ref-for-mlcontext④⓪">MLContext</a></code> object.</p>
+   <h4 class="heading settled" data-level="7.8.1" id="api-mloperand-create"><span class="secno">7.8.1. </span><span class="content">Creating <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑦">MLOperand</a></code></span><a class="self-link" href="#api-mloperand-create"></a></h4>
+    The <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑧">MLOperand</a></code> objects are created by the methods of <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥③">MLGraphBuilder</a></code>, internally using the following algorithms. 
    <details class="algorithm" data-algorithm="create an MLOperand" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="create-an-mloperand">create an MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥③">MLGraphBuilder</a></code> <var>builder</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑥">MLOperandDescriptor</a></code> <var>desc</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="create-an-mloperand">create an MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥④">MLGraphBuilder</a></code> <var>builder</var> and <code class="idl"><a data-link-type="idl" href="#dictdef-mloperanddescriptor" id="ref-for-dictdef-mloperanddescriptor②⑥">MLOperandDescriptor</a></code> <var>desc</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
-       <p>Let <var>operand</var> be a new <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑦">MLOperand</a></code>.</p>
+       <p>Let <var>operand</var> be a new <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑨">MLOperand</a></code>.</p>
       <li data-md>
        <p>Set <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-builder-slot" id="ref-for-dom-mloperand-builder-slot①">[[builder]]</a></code> to <var>builder</var>.</p>
       <li data-md>
@@ -8305,11 +8306,11 @@ <h4 class="heading settled" data-level="7.8.1" id="api-mloperand-create"><span c
     </div>
    </details>
    <details class="algorithm" data-algorithm="copy an MLOperand" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="copy-an-mloperand">copy an MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑧">MLOperand</a></code> <var>operand</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-type="dfn" data-noexport id="copy-an-mloperand">copy an MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①⓪">MLOperand</a></code> <var>operand</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
-       <p>Let <var>result</var> be a new <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④⓪⑨">MLOperand</a></code>.</p>
+       <p>Let <var>result</var> be a new <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①①">MLOperand</a></code>.</p>
       <li data-md>
        <p>Set <var>result</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-builder-slot" id="ref-for-dom-mloperand-builder-slot②">[[builder]]</a></code> to <var>operand</var>.<code class="idl"><a data-link-type="idl" href="#dom-mloperand-builder-slot" id="ref-for-dom-mloperand-builder-slot③">[[builder]]</a></code>.</p>
       <li data-md>
@@ -8337,7 +8338,7 @@ <h4 class="heading settled" data-level="7.8.1" id="api-mloperand-create"><span c
     </div>
    </details>
    <details class="algorithm" data-algorithm="validate MLOperand" data-algorithm-for="MLOperand" open>
-    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLOperand" data-dfn-type="dfn" data-noexport id="mloperand-validate-mloperand">validate MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①⓪">MLOperand</a></code> <var>operand</var> and <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥④">MLGraphBuilder</a></code> <var>builder</var>, run the following steps: </summary>
+    <summary> To <dfn class="dfn-paneled" data-dfn-for="MLOperand" data-dfn-type="dfn" data-noexport id="mloperand-validate-mloperand">validate MLOperand</dfn> given <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①②">MLOperand</a></code> <var>operand</var> and <code class="idl"><a data-link-type="idl" href="#mlgraphbuilder" id="ref-for-mlgraphbuilder⑥⑤">MLGraphBuilder</a></code> <var>builder</var>, run the following steps: </summary>
     <div class="algorithm-steps">
      <ol>
       <li data-md>
@@ -8352,8 +8353,8 @@ <h4 class="heading settled" data-level="7.8.1" id="api-mloperand-create"><span c
     </div>
    </details>
    <h4 class="heading settled" data-level="7.8.2" id="api-mloperand-datatype"><span class="secno">7.8.2. </span><span class="content">dataType</span><a class="self-link" href="#api-mloperand-datatype"></a></h4>
-    Return a data type of the <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①①">MLOperand</a></code>. 
-<pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mloperand" id="ref-for-mloperand④①②"><c- g>MLOperand</c-></a> {
+    Return a data type of the <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①③">MLOperand</a></code>. 
+<pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mloperand" id="ref-for-mloperand④①④"><c- g>MLOperand</c-></a> {
   <a data-link-type="idl-name" href="#enumdef-mloperanddatatype" id="ref-for-enumdef-mloperanddatatype⑥"><c- n>MLOperandDataType</c-></a> <a class="idl-code" data-link-type="method" href="#dom-mloperand-datatype" id="ref-for-dom-mloperand-datatype①"><c- g>dataType</c-></a>();
 };
 </pre>
@@ -8368,8 +8369,8 @@ <h4 class="heading settled" data-level="7.8.2" id="api-mloperand-datatype"><span
     </div>
    </details>
    <h4 class="heading settled" data-level="7.8.3" id="api-mloperand-shape"><span class="secno">7.8.3. </span><span class="content">shape</span><a class="self-link" href="#api-mloperand-shape"></a></h4>
-    Return a shape of the <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①③">MLOperand</a></code>. 
-<pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mloperand" id="ref-for-mloperand④①④"><c- g>MLOperand</c-></a> {
+    Return a shape of the <code class="idl"><a data-link-type="idl" href="#mloperand" id="ref-for-mloperand④①⑤">MLOperand</a></code>. 
+<pre class="idl highlight def"><c- b>partial</c-> <c- b>interface</c-> <a class="idl-code" data-link-type="interface" href="#mloperand" id="ref-for-mloperand④①⑥"><c- g>MLOperand</c-></a> {
   <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="interface" href="https://webidl.spec.whatwg.org/#idl-unsigned-long" id="ref-for-idl-unsigned-long⑨①"><c- b>unsigned</c-> <c- b>long</c-></a>> <a class="idl-code" data-link-type="method" href="#dom-mloperand-shape" id="ref-for-dom-mloperand-shape①"><c- g>shape</c-></a>();
 };
 </pre>
@@ -9023,6 +9024,8 @@ <h3 class="no-num no-ref heading settled" id="index-defined-here"><span class="c
      <li><a href="#dom-mllstmoptions-peepholeweight">dict-member for MLLstmOptions</a><span>, in § 7.7.27</span>
     </ul>
    <li><a href="#dom-mltransposeoptions-permutation">permutation</a><span>, in § 7.7.44</span>
+   <li><a href="#platform-operand">platform operand</a><span>, in § 6.1</span>
+   <li><a href="#platform-operator">platform operator</a><span>, in § 6.1</span>
    <li><a href="#mlgraphbuilder-pooling-op">pooling-op</a><span>, in § 7.7.31</span>
    <li><a href="#dom-mlgraphbuilder-pow">pow(a, b)</a><span>, in § 7.7.11</span>
    <li><a href="#dom-mlcontext-powerpreference-slot">[[powerPreference]]</a><span>, in § 7.5</span>
@@ -9157,11 +9160,6 @@ <h3 class="no-num no-ref heading settled" id="index-defined-elsewhere"><span cla
      <li><span class="dfn-paneled" id="38d90164">element type</span>
      <li><span class="dfn-paneled" id="023f906d">view constructor</span>
     </ul>
-   <li>
-    <a data-link-type="biblio">[FileAPI]</a> defines the following terms:
-    <ul>
-     <li><span class="dfn-paneled" id="ea2e2fcc">object</span>
-    </ul>
    <li>
     <a data-link-type="biblio">[FLOAT16ARRAY]</a> defines the following terms:
     <ul>
@@ -9190,6 +9188,7 @@ <h3 class="no-num no-ref heading settled" id="index-defined-elsewhere"><span cla
      <li><span class="dfn-paneled" id="45209803">for each <small>(for map)</small></span>
      <li><span class="dfn-paneled" id="860300d4">implementation-defined</span>
      <li><span class="dfn-paneled" id="649608b9">list</span>
+     <li><span class="dfn-paneled" id="84b454ff">ordered map</span>
      <li><span class="dfn-paneled" id="0204d188">size</span>
      <li><span class="dfn-paneled" id="0698d556">string</span>
      <li><span class="dfn-paneled" id="febebe0e">the range</span>
@@ -9262,8 +9261,6 @@ <h3 class="no-num no-ref heading settled" id="normative"><span class="content">N
    <dd>Tab Atkins Jr.; Elika Etemad. <a href="https://drafts.csswg.org/css-values-4/"><cite>CSS Values and Units Module Level 4</cite></a>. URL: <a href="https://drafts.csswg.org/css-values-4/">https://drafts.csswg.org/css-values-4/</a>
    <dt id="biblio-ecmascript">[ECMASCRIPT]
    <dd><a href="https://tc39.es/ecma262/multipage/"><cite>ECMAScript Language Specification</cite></a>. URL: <a href="https://tc39.es/ecma262/multipage/">https://tc39.es/ecma262/multipage/</a>
-   <dt id="biblio-fileapi">[FileAPI]
-   <dd>Marijn Kruisselbrink. <a href="https://w3c.github.io/FileAPI/"><cite>File API</cite></a>. URL: <a href="https://w3c.github.io/FileAPI/">https://w3c.github.io/FileAPI/</a>
    <dt id="biblio-html">[HTML]
    <dd>Anne van Kesteren; et al. <a href="https://html.spec.whatwg.org/multipage/"><cite>HTML Standard</cite></a>. Living Standard. URL: <a href="https://html.spec.whatwg.org/multipage/">https://html.spec.whatwg.org/multipage/</a>
    <dt id="biblio-infra">[INFRA]
@@ -10287,7 +10284,6 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['e63ec40a'] = {"dfnID": "e63ec40a", "url": "https://tc39.es/ecma262/#table-the-typedarray-constructors", "dfnText": "element size", "refSections": [{"refs": [{"id": "ref-for-table-the-typedarray-constructors"}, {"id": "ref-for-table-the-typedarray-constructors\u2460"}, {"id": "ref-for-table-the-typedarray-constructors\u2461"}, {"id": "ref-for-table-the-typedarray-constructors\u2462"}], "title": "7.5.1. Synchronous Execution"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2463"}, {"id": "ref-for-table-the-typedarray-constructors\u2464"}], "title": "7.5.2. MLNamedArrayBufferViews transfer algorithm"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2465"}], "title": "7.9. MLOperandDescriptor dictionary"}], "external": true};
 window.dfnpanelData['38d90164'] = {"dfnID": "38d90164", "url": "https://tc39.es/ecma262/#table-the-typedarray-constructors", "dfnText": "element type", "refSections": [{"refs": [{"id": "ref-for-table-the-typedarray-constructors"}, {"id": "ref-for-table-the-typedarray-constructors\u2460"}, {"id": "ref-for-table-the-typedarray-constructors\u2461"}, {"id": "ref-for-table-the-typedarray-constructors\u2462"}], "title": "7.5.1. Synchronous Execution"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2463"}, {"id": "ref-for-table-the-typedarray-constructors\u2464"}], "title": "7.5.2. MLNamedArrayBufferViews transfer algorithm"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2465"}], "title": "7.9. MLOperandDescriptor dictionary"}], "external": true};
 window.dfnpanelData['023f906d'] = {"dfnID": "023f906d", "url": "https://tc39.es/ecma262/#table-the-typedarray-constructors", "dfnText": "view constructor", "refSections": [{"refs": [{"id": "ref-for-table-the-typedarray-constructors"}, {"id": "ref-for-table-the-typedarray-constructors\u2460"}, {"id": "ref-for-table-the-typedarray-constructors\u2461"}, {"id": "ref-for-table-the-typedarray-constructors\u2462"}], "title": "7.5.1. Synchronous Execution"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2463"}, {"id": "ref-for-table-the-typedarray-constructors\u2464"}], "title": "7.5.2. MLNamedArrayBufferViews transfer algorithm"}, {"refs": [{"id": "ref-for-table-the-typedarray-constructors\u2465"}], "title": "7.9. MLOperandDescriptor dictionary"}], "external": true};
-window.dfnpanelData['ea2e2fcc'] = {"dfnID": "ea2e2fcc", "url": "https://w3c.github.io/FileAPI/#blob-url-entry-object", "dfnText": "object", "refSections": [{"refs": [{"id": "ref-for-blob-url-entry-object"}, {"id": "ref-for-blob-url-entry-object\u2460"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-blob-url-entry-object\u2461"}, {"id": "ref-for-blob-url-entry-object\u2462"}], "title": "7.8. MLOperand interface"}], "external": true};
 window.dfnpanelData['7bb7bc47'] = {"dfnID": "7bb7bc47", "url": "https://tc39.es/proposal-float16array/#sec-float16array", "dfnText": "Float16Array", "refSections": [{"refs": [{"id": "ref-for-sec-float16array"}, {"id": "ref-for-sec-float16array\u2460"}], "title": "9.1. MLOperandDataType and ArrayBufferView compatibility"}], "external": true};
 window.dfnpanelData['b8308563'] = {"dfnID": "b8308563", "url": "https://html.spec.whatwg.org/multipage/workers.html#dedicatedworkerglobalscope", "dfnText": "DedicatedWorkerGlobalScope", "refSections": [{"refs": [{"id": "ref-for-dedicatedworkerglobalscope"}], "title": "7.1. The navigator.ml interface"}], "external": true};
 window.dfnpanelData['be0c27b2'] = {"dfnID": "be0c27b2", "url": "https://html.spec.whatwg.org/multipage/system-state.html#navigator", "dfnText": "Navigator", "refSections": [{"refs": [{"id": "ref-for-navigator"}, {"id": "ref-for-navigator\u2460"}], "title": "7.1. The navigator.ml interface"}], "external": true};
@@ -10303,8 +10299,9 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['1243a891'] = {"dfnID": "1243a891", "url": "https://infra.spec.whatwg.org/#map-exists", "dfnText": "exist", "refSections": [{"refs": [{"id": "ref-for-map-exists"}, {"id": "ref-for-map-exists\u2460"}], "title": "7.2.2. createContext"}, {"refs": [{"id": "ref-for-map-exists\u2461"}, {"id": "ref-for-map-exists\u2462"}], "title": "7.4.2. Dispatch Execution Commands"}, {"refs": [{"id": "ref-for-map-exists\u2463"}], "title": "7.5.1. Synchronous Execution"}, {"refs": [{"id": "ref-for-map-exists\u2464"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-map-exists\u2465"}, {"id": "ref-for-map-exists\u2466"}, {"id": "ref-for-map-exists\u2467"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-map-exists\u2468"}, {"id": "ref-for-map-exists\u2460\u24ea"}, {"id": "ref-for-map-exists\u2460\u2460"}, {"id": "ref-for-map-exists\u2460\u2461"}, {"id": "ref-for-map-exists\u2460\u2462"}, {"id": "ref-for-map-exists\u2460\u2463"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-map-exists\u2460\u2464"}, {"id": "ref-for-map-exists\u2460\u2465"}, {"id": "ref-for-map-exists\u2460\u2466"}, {"id": "ref-for-map-exists\u2460\u2467"}, {"id": "ref-for-map-exists\u2460\u2468"}, {"id": "ref-for-map-exists\u2461\u24ea"}, {"id": "ref-for-map-exists\u2461\u2460"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-map-exists\u2461\u2461"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-map-exists\u2461\u2462"}, {"id": "ref-for-map-exists\u2461\u2463"}, {"id": "ref-for-map-exists\u2461\u2464"}, {"id": "ref-for-map-exists\u2461\u2465"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-map-exists\u2461\u2466"}, {"id": "ref-for-map-exists\u2461\u2467"}, {"id": "ref-for-map-exists\u2461\u2468"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-map-exists\u2462\u24ea"}, {"id": "ref-for-map-exists\u2462\u2460"}], "title": "7.7.22. input"}, {"refs": [{"id": "ref-for-map-exists\u2462\u2461"}, {"id": "ref-for-map-exists\u2462\u2462"}, {"id": "ref-for-map-exists\u2462\u2463"}, {"id": "ref-for-map-exists\u2462\u2464"}, {"id": "ref-for-map-exists\u2462\u2465"}, {"id": "ref-for-map-exists\u2462\u2466"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-map-exists\u2462\u2467"}, {"id": "ref-for-map-exists\u2462\u2468"}, {"id": "ref-for-map-exists\u2463\u24ea"}, {"id": "ref-for-map-exists\u2463\u2460"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-map-exists\u2463\u2461"}, {"id": "ref-for-map-exists\u2463\u2462"}, {"id": "ref-for-map-exists\u2463\u2463"}, {"id": "ref-for-map-exists\u2463\u2464"}, {"id": "ref-for-map-exists\u2463\u2465"}, {"id": "ref-for-map-exists\u2463\u2466"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-map-exists\u2463\u2467"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-map-exists\u2463\u2468"}, {"id": "ref-for-map-exists\u2464\u24ea"}, {"id": "ref-for-map-exists\u2464\u2460"}, {"id": "ref-for-map-exists\u2464\u2461"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-map-exists\u2464\u2462"}, {"id": "ref-for-map-exists\u2464\u2463"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-map-exists\u2464\u2464"}, {"id": "ref-for-map-exists\u2464\u2465"}], "title": "7.8.1. Creating MLOperand"}], "external": true};
 window.dfnpanelData['16d07e10'] = {"dfnID": "16d07e10", "url": "https://infra.spec.whatwg.org/#list-iterate", "dfnText": "for each (for list)", "refSections": [{"refs": [{"id": "ref-for-list-iterate"}, {"id": "ref-for-list-iterate\u2460"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-list-iterate\u2461"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-list-iterate\u2462"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-list-iterate\u2463"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-list-iterate\u2464"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-list-iterate\u2465"}], "title": "7.9. MLOperandDescriptor dictionary"}], "external": true};
 window.dfnpanelData['45209803'] = {"dfnID": "45209803", "url": "https://infra.spec.whatwg.org/#map-iterate", "dfnText": "for each (for map)", "refSections": [{"refs": [{"id": "ref-for-map-iterate"}, {"id": "ref-for-map-iterate\u2460"}, {"id": "ref-for-map-iterate\u2461"}, {"id": "ref-for-map-iterate\u2462"}], "title": "7.4.2. Dispatch Execution Commands"}, {"refs": [{"id": "ref-for-map-iterate\u2463"}, {"id": "ref-for-map-iterate\u2464"}, {"id": "ref-for-map-iterate\u2465"}], "title": "7.5.1. Synchronous Execution"}, {"refs": [{"id": "ref-for-map-iterate\u2466"}], "title": "7.5.2. MLNamedArrayBufferViews transfer algorithm"}, {"refs": [{"id": "ref-for-map-iterate\u2467"}, {"id": "ref-for-map-iterate\u2468"}], "title": "7.7.4.2. buildSync(outputs)"}, {"refs": [{"id": "ref-for-map-iterate\u2460\u24ea"}, {"id": "ref-for-map-iterate\u2460\u2460"}, {"id": "ref-for-map-iterate\u2460\u2461"}, {"id": "ref-for-map-iterate\u2460\u2462"}], "title": "7.7.16. gather"}], "external": true};
-window.dfnpanelData['860300d4'] = {"dfnID": "860300d4", "url": "https://infra.spec.whatwg.org/#implementation-defined", "dfnText": "implementation-defined", "refSections": [{"refs": [{"id": "ref-for-implementation-defined"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-implementation-defined\u2460"}, {"id": "ref-for-implementation-defined\u2461"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-implementation-defined\u2462"}, {"id": "ref-for-implementation-defined\u2463"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-implementation-defined\u2464"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-implementation-defined\u2465"}], "title": "7.7.4.2. buildSync(outputs)"}, {"refs": [{"id": "ref-for-implementation-defined\u2466"}, {"id": "ref-for-implementation-defined\u2467"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-implementation-defined\u2468"}, {"id": "ref-for-implementation-defined\u2460\u24ea"}], "title": "7.7.6.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2460"}, {"id": "ref-for-implementation-defined\u2460\u2461"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2462"}], "title": "7.7.8.1. constant(descriptor, bufferView)"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2463"}], "title": "7.7.8.2. constant(value, type)"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2464"}, {"id": "ref-for-implementation-defined\u2460\u2465"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2466"}, {"id": "ref-for-implementation-defined\u2460\u2467"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-implementation-defined\u2460\u2468"}, {"id": "ref-for-implementation-defined\u2461\u24ea"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-implementation-defined\u2461\u2460"}, {"id": "ref-for-implementation-defined\u2461\u2461"}, {"id": "ref-for-implementation-defined\u2461\u2462"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2461\u2463"}, {"id": "ref-for-implementation-defined\u2461\u2464"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2461\u2465"}, {"id": "ref-for-implementation-defined\u2461\u2466"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2461\u2467"}, {"id": "ref-for-implementation-defined\u2461\u2468"}], "title": "7.7.14.1. elu(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u24ea"}, {"id": "ref-for-implementation-defined\u2462\u2460"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u2461"}, {"id": "ref-for-implementation-defined\u2462\u2462"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u2463"}, {"id": "ref-for-implementation-defined\u2462\u2464"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u2465"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u2466"}, {"id": "ref-for-implementation-defined\u2462\u2467"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-implementation-defined\u2462\u2468"}, {"id": "ref-for-implementation-defined\u2463\u24ea"}], "title": "7.7.20.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2463\u2460"}, {"id": "ref-for-implementation-defined\u2463\u2461"}], "title": "7.7.21.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2463\u2462"}], "title": "7.7.22. input"}, {"refs": [{"id": "ref-for-implementation-defined\u2463\u2463"}, {"id": "ref-for-implementation-defined\u2463\u2464"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-implementation-defined\u2463\u2465"}, {"id": "ref-for-implementation-defined\u2463\u2466"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-implementation-defined\u2463\u2467"}, {"id": "ref-for-implementation-defined\u2463\u2468"}], "title": "7.7.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2464\u24ea"}, {"id": "ref-for-implementation-defined\u2464\u2460"}], "title": "7.7.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2464\u2461"}, {"id": "ref-for-implementation-defined\u2464\u2462"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-implementation-defined\u2464\u2463"}, {"id": "ref-for-implementation-defined\u2464\u2464"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-implementation-defined\u2464\u2465"}, {"id": "ref-for-implementation-defined\u2464\u2466"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-implementation-defined\u2464\u2467"}, {"id": "ref-for-implementation-defined\u2464\u2468"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-implementation-defined\u2465\u24ea"}, {"id": "ref-for-implementation-defined\u2465\u2460"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2465\u2461"}, {"id": "ref-for-implementation-defined\u2465\u2462"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-implementation-defined\u2465\u2463"}, {"id": "ref-for-implementation-defined\u2465\u2464"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2465\u2465"}, {"id": "ref-for-implementation-defined\u2465\u2466"}], "title": "7.7.34.1. relu(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2465\u2467"}, {"id": "ref-for-implementation-defined\u2465\u2468"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-implementation-defined\u2466\u24ea"}, {"id": "ref-for-implementation-defined\u2466\u2460"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-implementation-defined\u2466\u2461"}, {"id": "ref-for-implementation-defined\u2466\u2462"}], "title": "7.7.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2466\u2463"}, {"id": "ref-for-implementation-defined\u2466\u2464"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-implementation-defined\u2466\u2465"}, {"id": "ref-for-implementation-defined\u2466\u2466"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2466\u2467"}, {"id": "ref-for-implementation-defined\u2466\u2468"}], "title": "7.7.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-implementation-defined\u2467\u24ea"}, {"id": "ref-for-implementation-defined\u2467\u2460"}], "title": "7.7.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2467\u2461"}, {"id": "ref-for-implementation-defined\u2467\u2462"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-implementation-defined\u2467\u2463"}, {"id": "ref-for-implementation-defined\u2467\u2464"}], "title": "7.7.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-implementation-defined\u2467\u2465"}, {"id": "ref-for-implementation-defined\u2467\u2466"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-implementation-defined\u2467\u2467"}, {"id": "ref-for-implementation-defined\u2467\u2468"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-implementation-defined\u2468\u24ea"}, {"id": "ref-for-implementation-defined\u2468\u2460"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-implementation-defined\u2468\u2461"}, {"id": "ref-for-implementation-defined\u2468\u2462"}], "title": "7.8. MLOperand interface"}], "external": true};
+window.dfnpanelData['860300d4'] = {"dfnID": "860300d4", "url": "https://infra.spec.whatwg.org/#implementation-defined", "dfnText": "implementation-defined", "refSections": [{"refs": [{"id": "ref-for-implementation-defined"}, {"id": "ref-for-implementation-defined\u2460"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-implementation-defined\u2461"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-implementation-defined\u2462"}], "title": "7.7.4.2. buildSync(outputs)"}, {"refs": [{"id": "ref-for-implementation-defined\u2463"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-implementation-defined\u2464"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-implementation-defined\u2465"}], "title": "7.7.22. input"}], "external": true};
 window.dfnpanelData['649608b9'] = {"dfnID": "649608b9", "url": "https://infra.spec.whatwg.org/#list", "dfnText": "list", "refSections": [{"refs": [{"id": "ref-for-list"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-list\u2460"}, {"id": "ref-for-list\u2461"}, {"id": "ref-for-list\u2462"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-list\u2463"}, {"id": "ref-for-list\u2464"}, {"id": "ref-for-list\u2465"}, {"id": "ref-for-list\u2466"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-list\u2467"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-list\u2468"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-list\u2460\u24ea"}, {"id": "ref-for-list\u2460\u2460"}, {"id": "ref-for-list\u2460\u2461"}, {"id": "ref-for-list\u2460\u2462"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-list\u2460\u2463"}, {"id": "ref-for-list\u2460\u2464"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-list\u2460\u2465"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-list\u2460\u2466"}, {"id": "ref-for-list\u2460\u2467"}, {"id": "ref-for-list\u2460\u2468"}, {"id": "ref-for-list\u2461\u24ea"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-list\u2461\u2460"}, {"id": "ref-for-list\u2461\u2461"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-list\u2461\u2462"}, {"id": "ref-for-list\u2461\u2463"}], "title": "7.7.36. reshape"}], "external": true};
+window.dfnpanelData['84b454ff'] = {"dfnID": "84b454ff", "url": "https://infra.spec.whatwg.org/#ordered-map", "dfnText": "ordered map", "refSections": [{"refs": [{"id": "ref-for-ordered-map"}], "title": "7.3. MLActivation interface"}], "external": true};
 window.dfnpanelData['0204d188'] = {"dfnID": "0204d188", "url": "https://infra.spec.whatwg.org/#list-size", "dfnText": "size", "refSections": [{"refs": [{"id": "ref-for-list-size"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-list-size\u2460"}, {"id": "ref-for-list-size\u2461"}, {"id": "ref-for-list-size\u2462"}, {"id": "ref-for-list-size\u2463"}, {"id": "ref-for-list-size\u2464"}, {"id": "ref-for-list-size\u2465"}, {"id": "ref-for-list-size\u2466"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-list-size\u2467"}, {"id": "ref-for-list-size\u2468"}, {"id": "ref-for-list-size\u2460\u24ea"}, {"id": "ref-for-list-size\u2460\u2460"}, {"id": "ref-for-list-size\u2460\u2461"}, {"id": "ref-for-list-size\u2460\u2462"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-list-size\u2460\u2463"}, {"id": "ref-for-list-size\u2460\u2464"}, {"id": "ref-for-list-size\u2460\u2465"}, {"id": "ref-for-list-size\u2460\u2466"}, {"id": "ref-for-list-size\u2460\u2467"}, {"id": "ref-for-list-size\u2460\u2468"}, {"id": "ref-for-list-size\u2461\u24ea"}, {"id": "ref-for-list-size\u2461\u2460"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-list-size\u2461\u2461"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-list-size\u2461\u2462"}, {"id": "ref-for-list-size\u2461\u2463"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-list-size\u2461\u2464"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-list-size\u2461\u2465"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-list-size\u2461\u2466"}, {"id": "ref-for-list-size\u2461\u2467"}, {"id": "ref-for-list-size\u2461\u2468"}, {"id": "ref-for-list-size\u2462\u24ea"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-list-size\u2462\u2460"}, {"id": "ref-for-list-size\u2462\u2461"}, {"id": "ref-for-list-size\u2462\u2462"}, {"id": "ref-for-list-size\u2462\u2463"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-list-size\u2462\u2464"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-list-size\u2462\u2465"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-list-size\u2462\u2466"}, {"id": "ref-for-list-size\u2462\u2467"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-list-size\u2462\u2468"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-list-size\u2463\u24ea"}, {"id": "ref-for-list-size\u2463\u2460"}, {"id": "ref-for-list-size\u2463\u2461"}, {"id": "ref-for-list-size\u2463\u2462"}, {"id": "ref-for-list-size\u2463\u2463"}, {"id": "ref-for-list-size\u2463\u2464"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-list-size\u2463\u2465"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-list-size\u2463\u2466"}, {"id": "ref-for-list-size\u2463\u2467"}, {"id": "ref-for-list-size\u2463\u2468"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-list-size\u2464\u24ea"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-list-size\u2464\u2460"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-list-size\u2464\u2461"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-list-size\u2464\u2462"}, {"id": "ref-for-list-size\u2464\u2463"}, {"id": "ref-for-list-size\u2464\u2464"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-list-size\u2464\u2465"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-list-size\u2464\u2466"}, {"id": "ref-for-list-size\u2464\u2467"}], "title": "7.8.1. Creating MLOperand"}], "external": true};
 window.dfnpanelData['0698d556'] = {"dfnID": "0698d556", "url": "https://infra.spec.whatwg.org/#string", "dfnText": "string", "refSections": [{"refs": [{"id": "ref-for-string"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-string\u2460"}, {"id": "ref-for-string\u2461"}, {"id": "ref-for-string\u2462"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-string\u2463"}, {"id": "ref-for-string\u2464"}, {"id": "ref-for-string\u2465"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-string\u2466"}], "title": "7.7.22. input"}, {"refs": [{"id": "ref-for-string\u2467"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-string\u2468"}, {"id": "ref-for-string\u2460\u24ea"}, {"id": "ref-for-string\u2460\u2460"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-string\u2460\u2461"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-string\u2460\u2462"}], "title": "7.8. MLOperand interface"}], "external": true};
 window.dfnpanelData['febebe0e'] = {"dfnID": "febebe0e", "url": "https://infra.spec.whatwg.org/#the-range", "dfnText": "the range", "refSections": [{"refs": [{"id": "ref-for-the-range"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-the-range\u2460"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-the-range\u2461"}, {"id": "ref-for-the-range\u2462"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-the-range\u2463"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-the-range\u2464"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-the-range\u2465"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-the-range\u2466"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-the-range\u2467"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-the-range\u2468"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-the-range\u2460\u24ea"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-the-range\u2460\u2460"}], "title": "7.7.44. transpose"}], "external": true};
@@ -10356,6 +10353,8 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['e97a9688'] = {"dfnID": "e97a9688", "url": "https://webidl.spec.whatwg.org/#idl-unsigned-long", "dfnText": "unsigned long", "refSections": [{"refs": [{"id": "ref-for-idl-unsigned-long"}, {"id": "ref-for-idl-unsigned-long\u2460"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2461"}, {"id": "ref-for-idl-unsigned-long\u2462"}, {"id": "ref-for-idl-unsigned-long\u2463"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2464"}, {"id": "ref-for-idl-unsigned-long\u2465"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2466"}, {"id": "ref-for-idl-unsigned-long\u2467"}, {"id": "ref-for-idl-unsigned-long\u2468"}, {"id": "ref-for-idl-unsigned-long\u2460\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2460\u2460"}, {"id": "ref-for-idl-unsigned-long\u2460\u2461"}, {"id": "ref-for-idl-unsigned-long\u2460\u2462"}, {"id": "ref-for-idl-unsigned-long\u2460\u2463"}, {"id": "ref-for-idl-unsigned-long\u2460\u2464"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2460\u2465"}, {"id": "ref-for-idl-unsigned-long\u2460\u2466"}, {"id": "ref-for-idl-unsigned-long\u2460\u2467"}, {"id": "ref-for-idl-unsigned-long\u2460\u2468"}, {"id": "ref-for-idl-unsigned-long\u2461\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2461\u2460"}, {"id": "ref-for-idl-unsigned-long\u2461\u2461"}, {"id": "ref-for-idl-unsigned-long\u2461\u2462"}, {"id": "ref-for-idl-unsigned-long\u2461\u2463"}, {"id": "ref-for-idl-unsigned-long\u2461\u2464"}, {"id": "ref-for-idl-unsigned-long\u2461\u2465"}, {"id": "ref-for-idl-unsigned-long\u2461\u2466"}, {"id": "ref-for-idl-unsigned-long\u2461\u2467"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2461\u2468"}, {"id": "ref-for-idl-unsigned-long\u2462\u24ea"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2462\u2460"}, {"id": "ref-for-idl-unsigned-long\u2462\u2461"}, {"id": "ref-for-idl-unsigned-long\u2462\u2462"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2462\u2463"}, {"id": "ref-for-idl-unsigned-long\u2462\u2464"}, {"id": "ref-for-idl-unsigned-long\u2462\u2465"}, {"id": "ref-for-idl-unsigned-long\u2462\u2466"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2462\u2467"}, {"id": "ref-for-idl-unsigned-long\u2462\u2468"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2463\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2463\u2460"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2463\u2461"}, {"id": "ref-for-idl-unsigned-long\u2463\u2462"}, {"id": "ref-for-idl-unsigned-long\u2463\u2463"}, {"id": "ref-for-idl-unsigned-long\u2463\u2464"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2463\u2465"}, {"id": "ref-for-idl-unsigned-long\u2463\u2466"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2463\u2467"}, {"id": "ref-for-idl-unsigned-long\u2463\u2468"}, {"id": "ref-for-idl-unsigned-long\u2464\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2464\u2460"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2464\u2461"}, {"id": "ref-for-idl-unsigned-long\u2464\u2462"}, {"id": "ref-for-idl-unsigned-long\u2464\u2463"}, {"id": "ref-for-idl-unsigned-long\u2464\u2464"}, {"id": "ref-for-idl-unsigned-long\u2464\u2465"}, {"id": "ref-for-idl-unsigned-long\u2464\u2466"}, {"id": "ref-for-idl-unsigned-long\u2464\u2467"}, {"id": "ref-for-idl-unsigned-long\u2464\u2468"}, {"id": "ref-for-idl-unsigned-long\u2465\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2465\u2460"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2465\u2461"}, {"id": "ref-for-idl-unsigned-long\u2465\u2462"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2465\u2463"}, {"id": "ref-for-idl-unsigned-long\u2465\u2464"}, {"id": "ref-for-idl-unsigned-long\u2465\u2465"}, {"id": "ref-for-idl-unsigned-long\u2465\u2466"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2465\u2467"}, {"id": "ref-for-idl-unsigned-long\u2465\u2468"}, {"id": "ref-for-idl-unsigned-long\u2466\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2466\u2460"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2466\u2461"}, {"id": "ref-for-idl-unsigned-long\u2466\u2462"}, {"id": "ref-for-idl-unsigned-long\u2466\u2463"}, {"id": "ref-for-idl-unsigned-long\u2466\u2464"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2466\u2465"}, {"id": "ref-for-idl-unsigned-long\u2466\u2466"}, {"id": "ref-for-idl-unsigned-long\u2466\u2467"}, {"id": "ref-for-idl-unsigned-long\u2466\u2468"}, {"id": "ref-for-idl-unsigned-long\u2467\u24ea"}, {"id": "ref-for-idl-unsigned-long\u2467\u2460"}, {"id": "ref-for-idl-unsigned-long\u2467\u2461"}, {"id": "ref-for-idl-unsigned-long\u2467\u2462"}, {"id": "ref-for-idl-unsigned-long\u2467\u2463"}, {"id": "ref-for-idl-unsigned-long\u2467\u2464"}, {"id": "ref-for-idl-unsigned-long\u2467\u2465"}, {"id": "ref-for-idl-unsigned-long\u2467\u2466"}, {"id": "ref-for-idl-unsigned-long\u2467\u2467"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2467\u2468"}, {"id": "ref-for-idl-unsigned-long\u2468\u24ea"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2468\u2460"}, {"id": "ref-for-idl-unsigned-long\u2468\u2461"}], "title": "7.8.3. shape"}, {"refs": [{"id": "ref-for-idl-unsigned-long\u2468\u2462"}], "title": "7.9. MLOperandDescriptor dictionary"}], "external": true};
 window.dfnpanelData['f14b47b8'] = {"dfnID": "f14b47b8", "url": "https://webidl.spec.whatwg.org/#idl-unsigned-long-long", "dfnText": "unsigned long long", "refSections": [{"refs": [{"id": "ref-for-idl-unsigned-long-long"}, {"id": "ref-for-idl-unsigned-long-long\u2460"}, {"id": "ref-for-idl-unsigned-long-long\u2461"}, {"id": "ref-for-idl-unsigned-long-long\u2462"}, {"id": "ref-for-idl-unsigned-long-long\u2463"}, {"id": "ref-for-idl-unsigned-long-long\u2464"}], "title": "7.7. MLGraphBuilder interface"}], "external": true};
 window.dfnpanelData['f3fd9d04'] = {"dfnID": "f3fd9d04", "url": "https://webidl.spec.whatwg.org/#arraybufferview-write", "dfnText": "write", "refSections": [{"refs": [{"id": "ref-for-arraybufferview-write"}], "title": "7.5.3. Asynchronous Execution"}], "external": true};
+window.dfnpanelData['platform-operator'] = {"dfnID": "platform-operator", "url": "#platform-operator", "dfnText": "platform operator", "refSections": [{"refs": [{"id": "ref-for-platform-operator"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-platform-operator\u2460"}, {"id": "ref-for-platform-operator\u2461"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-platform-operator\u2462"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-platform-operator\u2463"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-platform-operator\u2464"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-platform-operator\u2465"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-platform-operator\u2466"}], "title": "7.7.6.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2467"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-platform-operator\u2468"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u24ea"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2460"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2461"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2462"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2463"}], "title": "7.7.14.1. elu(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2464"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2465"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2466"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2467"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-platform-operator\u2460\u2468"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u24ea"}], "title": "7.7.20.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2460"}], "title": "7.7.21.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2461"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2462"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2463"}], "title": "7.7.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2464"}], "title": "7.7.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2465"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2466"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2467"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-platform-operator\u2461\u2468"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u24ea"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2460"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2461"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2462"}], "title": "7.7.34.1. relu(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2463"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2464"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2465"}], "title": "7.7.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2466"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2467"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2462\u2468"}], "title": "7.7.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u24ea"}], "title": "7.7.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2460"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2461"}], "title": "7.7.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2462"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2463"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2464"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-platform-operator\u2463\u2465"}, {"id": "ref-for-platform-operator\u2463\u2466"}], "title": "7.8. MLOperand interface"}], "external": false};
+window.dfnpanelData['platform-operand'] = {"dfnID": "platform-operand", "url": "#platform-operand", "dfnText": "platform operand", "refSections": [{"refs": [{"id": "ref-for-platform-operand"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-platform-operand\u2460"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-platform-operand\u2461"}], "title": "7.7.6.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2462"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-platform-operand\u2463"}], "title": "7.7.8.1. constant(descriptor, bufferView)"}, {"refs": [{"id": "ref-for-platform-operand\u2464"}], "title": "7.7.8.2. constant(value, type)"}, {"refs": [{"id": "ref-for-platform-operand\u2465"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"id": "ref-for-platform-operand\u2466"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-platform-operand\u2467"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-platform-operand\u2468"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u24ea"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2460"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2461"}], "title": "7.7.14.1. elu(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2462"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2463"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2464"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2465"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2466"}], "title": "7.7.20.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2467"}], "title": "7.7.21.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2460\u2468"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u24ea"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2460"}], "title": "7.7.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2461"}], "title": "7.7.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2462"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2463"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2464"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2465"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2466"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2467"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-platform-operand\u2461\u2468"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u24ea"}], "title": "7.7.34.1. relu(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2460"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2461"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2462"}], "title": "7.7.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2463"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2464"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2465"}], "title": "7.7.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2466"}], "title": "7.7.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2467"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-platform-operand\u2462\u2468"}], "title": "7.7.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-platform-operand\u2463\u24ea"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-platform-operand\u2463\u2460"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-platform-operand\u2463\u2461"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-platform-operand\u2463\u2462"}, {"id": "ref-for-platform-operand\u2463\u2463"}], "title": "7.8. MLOperand interface"}], "external": false};
 window.dfnpanelData['navigatorml'] = {"dfnID": "navigatorml", "url": "#navigatorml", "dfnText": "NavigatorML", "refSections": [{"refs": [{"id": "ref-for-navigatorml"}, {"id": "ref-for-navigatorml\u2460"}], "title": "7.1. The navigator.ml interface"}], "external": false};
 window.dfnpanelData['enumdef-mldevicetype'] = {"dfnID": "enumdef-mldevicetype", "url": "#enumdef-mldevicetype", "dfnText": "MLDeviceType", "refSections": [{"refs": [{"id": "ref-for-enumdef-mldevicetype"}], "title": "6.2. Device Selection"}, {"refs": [{"id": "ref-for-enumdef-mldevicetype\u2460"}], "title": "7.2. ML interface"}], "external": false};
 window.dfnpanelData['dom-mldevicetype-cpu'] = {"dfnID": "dom-mldevicetype-cpu", "url": "#dom-mldevicetype-cpu", "dfnText": "\"cpu\"", "refSections": [{"refs": [{"id": "ref-for-dom-mldevicetype-cpu"}], "title": "4. Privacy Considerations"}], "external": false};
@@ -10371,7 +10370,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['dom-ml-createcontext-gpudevice'] = {"dfnID": "dom-ml-createcontext-gpudevice", "url": "#dom-ml-createcontext-gpudevice", "dfnText": "createContext(gpuDevice)", "refSections": [{"refs": [{"id": "ref-for-dom-ml-createcontext-gpudevice"}], "title": "7.2. ML interface"}], "external": false};
 window.dfnpanelData['dom-ml-createcontextsync'] = {"dfnID": "dom-ml-createcontextsync", "url": "#dom-ml-createcontextsync", "dfnText": "createContextSync(options)", "refSections": [{"refs": [{"id": "ref-for-dom-ml-createcontextsync"}], "title": "7.2. ML interface"}, {"refs": [{"id": "ref-for-dom-ml-createcontextsync\u2460"}], "title": "7.2.3. createContextSync"}], "external": false};
 window.dfnpanelData['dom-ml-createcontextsync-gpudevice'] = {"dfnID": "dom-ml-createcontextsync-gpudevice", "url": "#dom-ml-createcontextsync-gpudevice", "dfnText": "createContextSync(gpuDevice)", "refSections": [{"refs": [{"id": "ref-for-dom-ml-createcontextsync-gpudevice"}], "title": "7.2. ML interface"}], "external": false};
-window.dfnpanelData['mlactivation'] = {"dfnID": "mlactivation", "url": "#mlactivation", "dfnText": "MLActivation", "refSections": [{"refs": [{"id": "ref-for-mlactivation"}, {"id": "ref-for-mlactivation\u2460"}, {"id": "ref-for-mlactivation\u2461"}, {"id": "ref-for-mlactivation\u2462"}, {"id": "ref-for-mlactivation\u2463"}, {"id": "ref-for-mlactivation\u2464"}, {"id": "ref-for-mlactivation\u2465"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-mlactivation\u2466"}, {"id": "ref-for-mlactivation\u2467"}, {"id": "ref-for-mlactivation\u2468"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u24ea"}, {"id": "ref-for-mlactivation\u2460\u2460"}, {"id": "ref-for-mlactivation\u2460\u2461"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2462"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2463"}], "title": "7.7.6.2. clamp(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2464"}, {"id": "ref-for-mlactivation\u2460\u2465"}, {"id": "ref-for-mlactivation\u2460\u2466"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2467"}, {"id": "ref-for-mlactivation\u2460\u2468"}, {"id": "ref-for-mlactivation\u2461\u24ea"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2460"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2461"}], "title": "7.7.14.2. elu(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2462"}, {"id": "ref-for-mlactivation\u2461\u2463"}, {"id": "ref-for-mlactivation\u2461\u2464"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2465"}, {"id": "ref-for-mlactivation\u2461\u2466"}, {"id": "ref-for-mlactivation\u2461\u2467"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2468"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u24ea"}], "title": "7.7.20.2. hardSigmoid(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2460"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2461"}], "title": "7.7.21.2. hardSwish()"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2462"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2463"}], "title": "7.7.25.2. leakyRelu(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2464"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2465"}], "title": "7.7.26.2. linear(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2466"}, {"id": "ref-for-mlactivation\u2462\u2467"}, {"id": "ref-for-mlactivation\u2462\u2468"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u24ea"}, {"id": "ref-for-mlactivation\u2463\u2460"}, {"id": "ref-for-mlactivation\u2463\u2461"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2462"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2463"}], "title": "7.7.34.2. relu()"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2464"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2465"}], "title": "7.7.37.2. sigmoid()"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2466"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2467"}], "title": "7.7.39.2. softmax()"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2468"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u24ea"}], "title": "7.7.40.2. softplus(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2460"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2461"}], "title": "7.7.41.2. softsign()"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2462"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2463"}], "title": "7.7.43.2. tanh()"}], "external": false};
+window.dfnpanelData['mlactivation'] = {"dfnID": "mlactivation", "url": "#mlactivation", "dfnText": "MLActivation", "refSections": [{"refs": [{"id": "ref-for-mlactivation"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mlactivation\u2460"}, {"id": "ref-for-mlactivation\u2461"}, {"id": "ref-for-mlactivation\u2462"}, {"id": "ref-for-mlactivation\u2463"}, {"id": "ref-for-mlactivation\u2464"}, {"id": "ref-for-mlactivation\u2465"}, {"id": "ref-for-mlactivation\u2466"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-mlactivation\u2467"}, {"id": "ref-for-mlactivation\u2468"}, {"id": "ref-for-mlactivation\u2460\u24ea"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2460"}, {"id": "ref-for-mlactivation\u2460\u2461"}, {"id": "ref-for-mlactivation\u2460\u2462"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2463"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2464"}], "title": "7.7.6.2. clamp(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2465"}, {"id": "ref-for-mlactivation\u2460\u2466"}, {"id": "ref-for-mlactivation\u2460\u2467"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-mlactivation\u2460\u2468"}, {"id": "ref-for-mlactivation\u2461\u24ea"}, {"id": "ref-for-mlactivation\u2461\u2460"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2461"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2462"}], "title": "7.7.14.2. elu(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2463"}, {"id": "ref-for-mlactivation\u2461\u2464"}, {"id": "ref-for-mlactivation\u2461\u2465"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-mlactivation\u2461\u2466"}, {"id": "ref-for-mlactivation\u2461\u2467"}, {"id": "ref-for-mlactivation\u2461\u2468"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u24ea"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2460"}], "title": "7.7.20.2. hardSigmoid(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2461"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2462"}], "title": "7.7.21.2. hardSwish()"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2463"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2464"}], "title": "7.7.25.2. leakyRelu(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2465"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2466"}], "title": "7.7.26.2. linear(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2462\u2467"}, {"id": "ref-for-mlactivation\u2462\u2468"}, {"id": "ref-for-mlactivation\u2463\u24ea"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2460"}, {"id": "ref-for-mlactivation\u2463\u2461"}, {"id": "ref-for-mlactivation\u2463\u2462"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2463"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2464"}], "title": "7.7.34.2. relu()"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2465"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2466"}], "title": "7.7.37.2. sigmoid()"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2467"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mlactivation\u2463\u2468"}], "title": "7.7.39.2. softmax()"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u24ea"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2460"}], "title": "7.7.40.2. softplus(options)"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2461"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2462"}], "title": "7.7.41.2. softsign()"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2463"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mlactivation\u2464\u2464"}], "title": "7.7.43.2. tanh()"}], "external": false};
 window.dfnpanelData['dom-mlactivation-name-slot'] = {"dfnID": "dom-mlactivation-name-slot", "url": "#dom-mlactivation-name-slot", "dfnText": "[[name]]", "refSections": [{"refs": [{"id": "ref-for-dom-mlactivation-name-slot"}], "title": "7.3.1. Creating MLActivation"}], "external": false};
 window.dfnpanelData['dom-mlactivation-builder-slot'] = {"dfnID": "dom-mlactivation-builder-slot", "url": "#dom-mlactivation-builder-slot", "dfnText": "[[builder]]", "refSections": [{"refs": [{"id": "ref-for-dom-mlactivation-builder-slot"}], "title": "7.3.1. Creating MLActivation"}], "external": false};
 window.dfnpanelData['dom-mlactivation-options-slot'] = {"dfnID": "dom-mlactivation-options-slot", "url": "#dom-mlactivation-options-slot", "dfnText": "[[options]]", "refSections": [{"refs": [{"id": "ref-for-dom-mlactivation-options-slot"}], "title": "7.3.1. Creating MLActivation"}], "external": false};
@@ -10414,7 +10413,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['typedefdef-mlnamedoperands'] = {"dfnID": "typedefdef-mlnamedoperands", "url": "#typedefdef-mlnamedoperands", "dfnText": "MLNamedOperands", "refSections": [{"refs": [{"id": "ref-for-typedefdef-mlnamedoperands"}, {"id": "ref-for-typedefdef-mlnamedoperands\u2460"}], "title": "7.7. MLGraphBuilder interface"}], "external": false};
 window.dfnpanelData['dictdef-mlbufferresourceview'] = {"dfnID": "dictdef-mlbufferresourceview", "url": "#dictdef-mlbufferresourceview", "dfnText": "MLBufferResourceView", "refSections": [{"refs": [{"id": "ref-for-dictdef-mlbufferresourceview"}, {"id": "ref-for-dictdef-mlbufferresourceview\u2460"}], "title": "7.7. MLGraphBuilder interface"}], "external": false};
 window.dfnpanelData['typedefdef-mlbufferview'] = {"dfnID": "typedefdef-mlbufferview", "url": "#typedefdef-mlbufferview", "dfnText": "MLBufferView", "refSections": [{"refs": [{"id": "ref-for-typedefdef-mlbufferview"}], "title": "7.7. MLGraphBuilder interface"}, {"refs": [{"id": "ref-for-typedefdef-mlbufferview\u2460"}], "title": "7.7.8.1. constant(descriptor, bufferView)"}], "external": false};
-window.dfnpanelData['mlgraphbuilder'] = {"dfnID": "mlgraphbuilder", "url": "#mlgraphbuilder", "dfnText": "MLGraphBuilder", "refSections": [{"refs": [{"id": "ref-for-mlgraphbuilder"}], "title": "3. Security Considerations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460"}, {"id": "ref-for-mlgraphbuilder\u2461"}, {"id": "ref-for-mlgraphbuilder\u2462"}, {"id": "ref-for-mlgraphbuilder\u2463"}, {"id": "ref-for-mlgraphbuilder\u2464"}, {"id": "ref-for-mlgraphbuilder\u2465"}, {"id": "ref-for-mlgraphbuilder\u2466"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2467"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2468"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u24ea"}, {"id": "ref-for-mlgraphbuilder\u2460\u2460"}, {"id": "ref-for-mlgraphbuilder\u2460\u2461"}, {"id": "ref-for-mlgraphbuilder\u2460\u2462"}, {"id": "ref-for-mlgraphbuilder\u2460\u2463"}, {"id": "ref-for-mlgraphbuilder\u2460\u2464"}, {"id": "ref-for-mlgraphbuilder\u2460\u2465"}], "title": "7.7. MLGraphBuilder interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2466"}], "title": "7.7.1. MLGraphBuilder constructor"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2467"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2468"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u24ea"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2460"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2461"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2462"}], "title": "7.7.8. constant"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2463"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2464"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2465"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2466"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2467"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2468"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u24ea"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2460"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2461"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2462"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2463"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2464"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2465"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2466"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2467"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2468"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u24ea"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2460"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2461"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2462"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2463"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2464"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2465"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2466"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2467"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2468"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u24ea"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2460"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2461"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2462"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2463"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2464"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2465"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2466"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2467"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2468"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u24ea"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u2460"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u2461"}, {"id": "ref-for-mlgraphbuilder\u2465\u2462"}, {"id": "ref-for-mlgraphbuilder\u2465\u2463"}], "title": "7.8.1. Creating MLOperand"}], "external": false};
+window.dfnpanelData['mlgraphbuilder'] = {"dfnID": "mlgraphbuilder", "url": "#mlgraphbuilder", "dfnText": "MLGraphBuilder", "refSections": [{"refs": [{"id": "ref-for-mlgraphbuilder"}], "title": "3. Security Considerations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460"}, {"id": "ref-for-mlgraphbuilder\u2461"}, {"id": "ref-for-mlgraphbuilder\u2462"}, {"id": "ref-for-mlgraphbuilder\u2463"}, {"id": "ref-for-mlgraphbuilder\u2464"}, {"id": "ref-for-mlgraphbuilder\u2465"}, {"id": "ref-for-mlgraphbuilder\u2466"}, {"id": "ref-for-mlgraphbuilder\u2467"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2468"}], "title": "7.3. MLActivation interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u24ea"}], "title": "7.3.1. Creating MLActivation"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2460"}, {"id": "ref-for-mlgraphbuilder\u2460\u2461"}, {"id": "ref-for-mlgraphbuilder\u2460\u2462"}, {"id": "ref-for-mlgraphbuilder\u2460\u2463"}, {"id": "ref-for-mlgraphbuilder\u2460\u2464"}, {"id": "ref-for-mlgraphbuilder\u2460\u2465"}, {"id": "ref-for-mlgraphbuilder\u2460\u2466"}], "title": "7.7. MLGraphBuilder interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2467"}], "title": "7.7.1. MLGraphBuilder constructor"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2460\u2468"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u24ea"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2460"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2461"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2462"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2463"}], "title": "7.7.8. constant"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2464"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2465"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2466"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2467"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2461\u2468"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u24ea"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2460"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2461"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2462"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2463"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2464"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2465"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2466"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2467"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2462\u2468"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u24ea"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2460"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2461"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2462"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2463"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2464"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2465"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2466"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2467"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2463\u2468"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u24ea"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2460"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2461"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2462"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2463"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2464"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2465"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2466"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2467"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2464\u2468"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u24ea"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u2460"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u2461"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-mlgraphbuilder\u2465\u2462"}, {"id": "ref-for-mlgraphbuilder\u2465\u2463"}, {"id": "ref-for-mlgraphbuilder\u2465\u2464"}], "title": "7.8.1. Creating MLOperand"}], "external": false};
 window.dfnpanelData['dom-mlbufferresourceview-resource'] = {"dfnID": "dom-mlbufferresourceview-resource", "url": "#dom-mlbufferresourceview-resource", "dfnText": "resource", "refSections": [{"refs": [{"id": "ref-for-dom-mlbufferresourceview-resource"}], "title": "7.7. MLGraphBuilder interface"}], "external": false};
 window.dfnpanelData['dom-mlbufferresourceview-offset'] = {"dfnID": "dom-mlbufferresourceview-offset", "url": "#dom-mlbufferresourceview-offset", "dfnText": "offset", "refSections": [{"refs": [{"id": "ref-for-dom-mlbufferresourceview-offset"}], "title": "7.7. MLGraphBuilder interface"}], "external": false};
 window.dfnpanelData['dom-mlbufferresourceview-size'] = {"dfnID": "dom-mlbufferresourceview-size", "url": "#dom-mlbufferresourceview-size", "dfnText": "size", "refSections": [{"refs": [{"id": "ref-for-dom-mlbufferresourceview-size"}], "title": "7.7. MLGraphBuilder interface"}], "external": false};
@@ -10527,7 +10526,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['dom-mlgemmoptions-beta'] = {"dfnID": "dom-mlgemmoptions-beta", "url": "#dom-mlgemmoptions-beta", "dfnText": "beta", "refSections": [{"refs": [{"id": "ref-for-dom-mlgemmoptions-beta"}], "title": "7.7.17. gemm"}], "external": false};
 window.dfnpanelData['dom-mlgemmoptions-atranspose'] = {"dfnID": "dom-mlgemmoptions-atranspose", "url": "#dom-mlgemmoptions-atranspose", "dfnText": "aTranspose", "refSections": [{"refs": [{"id": "ref-for-dom-mlgemmoptions-atranspose"}, {"id": "ref-for-dom-mlgemmoptions-atranspose\u2460"}], "title": "7.7.17. gemm"}], "external": false};
 window.dfnpanelData['dom-mlgemmoptions-btranspose'] = {"dfnID": "dom-mlgemmoptions-btranspose", "url": "#dom-mlgemmoptions-btranspose", "dfnText": "bTranspose", "refSections": [{"refs": [{"id": "ref-for-dom-mlgemmoptions-btranspose"}, {"id": "ref-for-dom-mlgemmoptions-btranspose\u2460"}], "title": "7.7.17. gemm"}], "external": false};
-window.dfnpanelData['dom-mlgraphbuilder-gemm'] = {"dfnID": "dom-mlgraphbuilder-gemm", "url": "#dom-mlgraphbuilder-gemm", "dfnText": "gemm(a, b, options)", "refSections": [{"refs": [{"id": "ref-for-dom-mlgraphbuilder-gemm"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-dom-mlgraphbuilder-gemm\u2460"}], "title": "7.7.17. gemm"}], "external": false};
+window.dfnpanelData['dom-mlgraphbuilder-gemm'] = {"dfnID": "dom-mlgraphbuilder-gemm", "url": "#dom-mlgraphbuilder-gemm", "dfnText": "gemm(a, b, options)", "refSections": [{"refs": [{"id": "ref-for-dom-mlgraphbuilder-gemm"}, {"id": "ref-for-dom-mlgraphbuilder-gemm\u2460"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-dom-mlgraphbuilder-gemm\u2461"}], "title": "7.7.17. gemm"}], "external": false};
 window.dfnpanelData['enumdef-mlgruweightlayout'] = {"dfnID": "enumdef-mlgruweightlayout", "url": "#enumdef-mlgruweightlayout", "dfnText": "MLGruWeightLayout", "refSections": [{"refs": [{"id": "ref-for-enumdef-mlgruweightlayout"}, {"id": "ref-for-enumdef-mlgruweightlayout\u2460"}, {"id": "ref-for-enumdef-mlgruweightlayout\u2461"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-enumdef-mlgruweightlayout\u2462"}, {"id": "ref-for-enumdef-mlgruweightlayout\u2463"}, {"id": "ref-for-enumdef-mlgruweightlayout\u2464"}], "title": "7.7.19. gruCell"}], "external": false};
 window.dfnpanelData['dom-mlgruweightlayout-zrn'] = {"dfnID": "dom-mlgruweightlayout-zrn", "url": "#dom-mlgruweightlayout-zrn", "dfnText": "\"zrn\"", "refSections": [{"refs": [{"id": "ref-for-dom-mlgruweightlayout-zrn"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-dom-mlgruweightlayout-zrn\u2460"}, {"id": "ref-for-dom-mlgruweightlayout-zrn\u2461"}], "title": "7.7.19. gruCell"}], "external": false};
 window.dfnpanelData['enumdef-mlrecurrentnetworkdirection'] = {"dfnID": "enumdef-mlrecurrentnetworkdirection", "url": "#enumdef-mlrecurrentnetworkdirection", "dfnText": "MLRecurrentNetworkDirection", "refSections": [{"refs": [{"id": "ref-for-enumdef-mlrecurrentnetworkdirection"}, {"id": "ref-for-enumdef-mlrecurrentnetworkdirection\u2460"}, {"id": "ref-for-enumdef-mlrecurrentnetworkdirection\u2461"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-enumdef-mlrecurrentnetworkdirection\u2462"}, {"id": "ref-for-enumdef-mlrecurrentnetworkdirection\u2463"}, {"id": "ref-for-enumdef-mlrecurrentnetworkdirection\u2464"}], "title": "7.7.27. lstm"}], "external": false};
@@ -10676,7 +10675,7 @@ <h2 class="no-num no-ref heading settled" id="issues-index"><span class="content
 window.dfnpanelData['dom-mltriangularoptions-diagonal'] = {"dfnID": "dom-mltriangularoptions-diagonal", "url": "#dom-mltriangularoptions-diagonal", "dfnText": "diagonal", "refSections": [{"refs": [{"id": "ref-for-dom-mltriangularoptions-diagonal"}], "title": "7.7.45. triangular"}], "external": false};
 window.dfnpanelData['dom-mlgraphbuilder-triangular'] = {"dfnID": "dom-mlgraphbuilder-triangular", "url": "#dom-mlgraphbuilder-triangular", "dfnText": "triangular(input, options)", "refSections": [{"refs": [{"id": "ref-for-dom-mlgraphbuilder-triangular"}], "title": "7.7.45. triangular"}], "external": false};
 window.dfnpanelData['dom-mlgraphbuilder-where'] = {"dfnID": "dom-mlgraphbuilder-where", "url": "#dom-mlgraphbuilder-where", "dfnText": "where(condition, input, other)", "refSections": [{"refs": [{"id": "ref-for-dom-mlgraphbuilder-where"}], "title": "7.7.46. 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"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mloperand\u2466"}, {"id": "ref-for-mloperand\u2467"}, {"id": "ref-for-mloperand\u2468"}, {"id": "ref-for-mloperand\u2460\u24ea"}], "title": "7.6. MLGraph interface"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2460"}, {"id": "ref-for-mloperand\u2460\u2461"}, {"id": "ref-for-mloperand\u2460\u2462"}, {"id": "ref-for-mloperand\u2460\u2463"}], "title": "7.7. MLGraphBuilder interface"}, {"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"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"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"}, {"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"}, {"id": "ref-for-mloperand\u2462\u2462"}, {"id": "ref-for-mloperand\u2462\u2463"}, {"id": "ref-for-mloperand\u2462\u2464"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465"}, {"id": "ref-for-mloperand\u2462\u2466"}, {"id": "ref-for-mloperand\u2462\u2467"}, {"id": "ref-for-mloperand\u2462\u2468"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-mloperand\u2463\u24ea"}, {"id": "ref-for-mloperand\u2463\u2460"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2461"}, {"id": "ref-for-mloperand\u2463\u2462"}], "title": "7.7.6.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2463"}, {"id": "ref-for-mloperand\u2463\u2464"}, {"id": "ref-for-mloperand\u2463\u2465"}, {"id": "ref-for-mloperand\u2463\u2466"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2467"}], "title": "7.7.8. constant"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2468"}, {"id": "ref-for-mloperand\u2464\u24ea"}], "title": "7.7.8.1. constant(descriptor, bufferView)"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2460"}, {"id": "ref-for-mloperand\u2464\u2461"}], "title": "7.7.8.2. constant(value, type)"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2462"}, {"id": "ref-for-mloperand\u2464\u2463"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"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"}, {"id": "ref-for-mloperand\u2465\u24ea"}, {"id": "ref-for-mloperand\u2465\u2460"}, {"id": "ref-for-mloperand\u2465\u2461"}, {"id": "ref-for-mloperand\u2465\u2462"}], "title": "7.7.9. conv2d"}, {"refs": [{"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"}, {"id": "ref-for-mloperand\u2466\u2461"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mloperand\u2466\u2462"}, {"id": "ref-for-mloperand\u2466\u2463"}, {"id": "ref-for-mloperand\u2466\u2464"}, {"id": "ref-for-mloperand\u2466\u2465"}, {"id": "ref-for-mloperand\u2466\u2466"}, {"id": "ref-for-mloperand\u2466\u2467"}, {"id": "ref-for-mloperand\u2466\u2468"}, {"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"}, {"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"}, {"id": "ref-for-mloperand\u2468\u2465"}, {"id": "ref-for-mloperand\u2468\u2466"}, {"id": "ref-for-mloperand\u2468\u2467"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"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"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"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"}, {"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"}, {"id": "ref-for-mloperand\u2460\u2463\u2467"}, {"id": "ref-for-mloperand\u2460\u2463\u2468"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2464\u24ea"}, {"id": "ref-for-mloperand\u2460\u2464\u2460"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2464\u2461"}, {"id": "ref-for-mloperand\u2460\u2464\u2462"}], "title": "7.7.14.1. elu(input, options)"}, {"refs": [{"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"}], "title": "7.7.15. expand"}, {"refs": [{"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"}], "title": "7.7.16. gather"}, {"refs": [{"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": "7.7.17. gemm"}, {"refs": [{"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"}, {"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"}, {"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"}, {"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": "7.7.18. gru"}, {"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"}, {"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"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u24ea\u2466"}, {"id": "ref-for-mloperand\u2461\u24ea\u2467"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2461\u24ea\u2468"}, {"id": "ref-for-mloperand\u2461\u2460\u24ea"}], "title": "7.7.20.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2460"}, {"id": "ref-for-mloperand\u2461\u2460\u2461"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2462"}, {"id": "ref-for-mloperand\u2461\u2460\u2463"}], "title": "7.7.21.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2464"}, {"id": "ref-for-mloperand\u2461\u2460\u2465"}], "title": "7.7.22. input"}, {"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"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"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"}, {"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"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2466"}, {"id": "ref-for-mloperand\u2461\u2462\u2467"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2468"}, {"id": "ref-for-mloperand\u2461\u2463\u24ea"}], "title": "7.7.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2460"}, {"id": "ref-for-mloperand\u2461\u2463\u2461"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2462"}, {"id": "ref-for-mloperand\u2461\u2463\u2463"}], "title": "7.7.26.1. linear(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2464"}, {"id": "ref-for-mloperand\u2461\u2463\u2465"}, {"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"}, {"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"}], "title": "7.7.27. lstm"}, {"refs": [{"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"}, {"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"}], "title": "7.7.28. lstmCell"}, {"refs": [{"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"}], "title": "7.7.29. matmul"}, {"refs": [{"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": "7.7.30. pad"}, {"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"}, {"id": "ref-for-mloperand\u2462\u24ea\u2466"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u24ea\u2467"}, {"id": "ref-for-mloperand\u2462\u24ea\u2468"}, {"id": "ref-for-mloperand\u2462\u2460\u24ea"}, {"id": "ref-for-mloperand\u2462\u2460\u2460"}, {"id": "ref-for-mloperand\u2462\u2460\u2461"}, {"id": "ref-for-mloperand\u2462\u2460\u2462"}], "title": "7.7.32. prelu"}, {"refs": [{"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"}, {"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"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2462\u2466"}, {"id": "ref-for-mloperand\u2462\u2462\u2467"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2462\u2468"}, {"id": "ref-for-mloperand\u2462\u2463\u24ea"}], "title": "7.7.34.1. relu(input)"}, {"refs": [{"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"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2465"}, {"id": "ref-for-mloperand\u2462\u2463\u2466"}, {"id": "ref-for-mloperand\u2462\u2463\u2467"}, {"id": "ref-for-mloperand\u2462\u2463\u2468"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u24ea"}, {"id": "ref-for-mloperand\u2462\u2464\u2460"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2461"}, {"id": "ref-for-mloperand\u2462\u2464\u2462"}], "title": "7.7.37.1. sigmoid(input)"}, {"refs": [{"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": "7.7.38. slice"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2467"}, {"id": "ref-for-mloperand\u2462\u2464\u2468"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u24ea"}, {"id": "ref-for-mloperand\u2462\u2465\u2460"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2461"}, {"id": "ref-for-mloperand\u2462\u2465\u2462"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2463"}, {"id": "ref-for-mloperand\u2462\u2465\u2464"}], "title": "7.7.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2465"}, {"id": "ref-for-mloperand\u2462\u2465\u2466"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2467"}, {"id": "ref-for-mloperand\u2462\u2465\u2468"}], "title": "7.7.41.1. softsign(input)"}, {"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": "7.7.42. split"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2463"}, {"id": "ref-for-mloperand\u2462\u2466\u2464"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2465"}, {"id": "ref-for-mloperand\u2462\u2466\u2466"}], "title": "7.7.43.1. tanh(input)"}, {"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": "7.7.44. transpose"}, {"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": "7.7.45. triangular"}, {"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"}, {"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": "7.7.46. where"}, {"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"}, {"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"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-mloperand\u2463\u24ea\u2464"}, {"id": "ref-for-mloperand\u2463\u24ea\u2465"}, {"id": "ref-for-mloperand\u2463\u24ea\u2466"}, {"id": "ref-for-mloperand\u2463\u24ea\u2467"}, {"id": "ref-for-mloperand\u2463\u24ea\u2468"}, {"id": "ref-for-mloperand\u2463\u2460\u24ea"}], "title": "7.8.1. Creating MLOperand"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2460\u2460"}, {"id": "ref-for-mloperand\u2463\u2460\u2461"}], "title": "7.8.2. dataType"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2460\u2462"}, {"id": "ref-for-mloperand\u2463\u2460\u2463"}], "title": "7.8.3. shape"}], "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"}], "title": "6.1. Overview"}, {"refs": [{"id": "ref-for-mloperand\u2468"}, {"id": "ref-for-mloperand\u2460\u24ea"}, {"id": "ref-for-mloperand\u2460\u2460"}, {"id": "ref-for-mloperand\u2460\u2461"}], "title": "7.6. MLGraph interface"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2462"}, {"id": "ref-for-mloperand\u2460\u2463"}, {"id": "ref-for-mloperand\u2460\u2464"}, {"id": "ref-for-mloperand\u2460\u2465"}], "title": "7.7. MLGraphBuilder interface"}, {"refs": [{"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"}], "title": "7.7.2. argMin/Max"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463"}, {"id": "ref-for-mloperand\u2461\u2464"}, {"id": "ref-for-mloperand\u2461\u2465"}, {"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"}, {"id": "ref-for-mloperand\u2462\u2462"}, {"id": "ref-for-mloperand\u2462\u2463"}, {"id": "ref-for-mloperand\u2462\u2464"}, {"id": "ref-for-mloperand\u2462\u2465"}, {"id": "ref-for-mloperand\u2462\u2466"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467"}, {"id": "ref-for-mloperand\u2462\u2468"}, {"id": "ref-for-mloperand\u2463\u24ea"}, {"id": "ref-for-mloperand\u2463\u2460"}], "title": "7.7.5. cast"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2461"}, {"id": "ref-for-mloperand\u2463\u2462"}], "title": "7.7.6. clamp"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2463"}, {"id": "ref-for-mloperand\u2463\u2464"}], "title": "7.7.6.1. clamp(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2465"}, {"id": "ref-for-mloperand\u2463\u2466"}, {"id": "ref-for-mloperand\u2463\u2467"}, {"id": "ref-for-mloperand\u2463\u2468"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-mloperand\u2464\u24ea"}], "title": "7.7.8. constant"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2460"}, {"id": "ref-for-mloperand\u2464\u2461"}], "title": "7.7.8.1. constant(descriptor, bufferView)"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2462"}, {"id": "ref-for-mloperand\u2464\u2463"}], "title": "7.7.8.2. constant(value, type)"}, {"refs": [{"id": "ref-for-mloperand\u2464\u2464"}, {"id": "ref-for-mloperand\u2464\u2465"}], "title": "7.7.8.3. constant(start, end, step, type)"}, {"refs": [{"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"}], "title": "7.7.9. conv2d"}, {"refs": [{"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"}, {"id": "ref-for-mloperand\u2466\u2461"}, {"id": "ref-for-mloperand\u2466\u2462"}, {"id": "ref-for-mloperand\u2466\u2463"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-mloperand\u2466\u2464"}, {"id": "ref-for-mloperand\u2466\u2465"}, {"id": "ref-for-mloperand\u2466\u2466"}, {"id": "ref-for-mloperand\u2466\u2467"}, {"id": "ref-for-mloperand\u2466\u2468"}, {"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"}, {"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"}, {"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"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"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"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"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"}, {"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"}], "title": "7.7.13. Element-wise unary operations"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2464\u2461"}, {"id": "ref-for-mloperand\u2460\u2464\u2462"}], "title": "7.7.14. elu"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2464\u2463"}, {"id": "ref-for-mloperand\u2460\u2464\u2464"}], "title": "7.7.14.1. elu(input, options)"}, {"refs": [{"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"}], "title": "7.7.15. expand"}, {"refs": [{"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"}], "title": "7.7.16. gather"}, {"refs": [{"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"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2466\u2464"}, {"id": "ref-for-mloperand\u2460\u2466\u2465"}, {"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"}, {"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"}, {"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"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-mloperand\u2460\u2468\u2462"}, {"id": "ref-for-mloperand\u2460\u2468\u2463"}, {"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": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-mloperand\u2461\u24ea\u2468"}, {"id": "ref-for-mloperand\u2461\u2460\u24ea"}], "title": "7.7.20. hardSigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2460"}, {"id": "ref-for-mloperand\u2461\u2460\u2461"}], "title": "7.7.20.1. hardSigmoid(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2462"}, {"id": "ref-for-mloperand\u2461\u2460\u2463"}], "title": "7.7.21. hardSwish"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2464"}, {"id": "ref-for-mloperand\u2461\u2460\u2465"}], "title": "7.7.21.1. hardSwish(input)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2460\u2466"}, {"id": "ref-for-mloperand\u2461\u2460\u2467"}], "title": "7.7.22. input"}, {"refs": [{"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"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2461\u2468"}, {"id": "ref-for-mloperand\u2461\u2462\u24ea"}, {"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": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2462\u2468"}, {"id": "ref-for-mloperand\u2461\u2463\u24ea"}], "title": "7.7.25. leakyRelu"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2460"}, {"id": "ref-for-mloperand\u2461\u2463\u2461"}], "title": "7.7.25.1. leakyRelu(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2462"}, {"id": "ref-for-mloperand\u2461\u2463\u2463"}], "title": "7.7.26. linear"}, {"refs": [{"id": "ref-for-mloperand\u2461\u2463\u2464"}, {"id": "ref-for-mloperand\u2461\u2463\u2465"}], "title": "7.7.26.1. linear(input, options)"}, {"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"}, {"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"}], "title": "7.7.27. lstm"}, {"refs": [{"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"}, {"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": "7.7.28. 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": "7.7.29. 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": "7.7.30. 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": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2460\u24ea"}, {"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"}], "title": "7.7.32. prelu"}, {"refs": [{"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"}, {"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"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2462\u2468"}, {"id": "ref-for-mloperand\u2462\u2463\u24ea"}], "title": "7.7.34. relu"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2460"}, {"id": "ref-for-mloperand\u2462\u2463\u2461"}], "title": "7.7.34.1. relu(input)"}, {"refs": [{"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"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2463\u2467"}, {"id": "ref-for-mloperand\u2462\u2463\u2468"}, {"id": "ref-for-mloperand\u2462\u2464\u24ea"}, {"id": "ref-for-mloperand\u2462\u2464\u2460"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2461"}, {"id": "ref-for-mloperand\u2462\u2464\u2462"}], "title": "7.7.37. sigmoid"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2463"}, {"id": "ref-for-mloperand\u2462\u2464\u2464"}], "title": "7.7.37.1. sigmoid(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2464\u2465"}, {"id": "ref-for-mloperand\u2462\u2464\u2466"}, {"id": "ref-for-mloperand\u2462\u2464\u2467"}, {"id": "ref-for-mloperand\u2462\u2464\u2468"}], "title": "7.7.38. slice"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u24ea"}, {"id": "ref-for-mloperand\u2462\u2465\u2460"}], "title": "7.7.39. softmax"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2461"}, {"id": "ref-for-mloperand\u2462\u2465\u2462"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2463"}, {"id": "ref-for-mloperand\u2462\u2465\u2464"}], "title": "7.7.40. softplus"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2465"}, {"id": "ref-for-mloperand\u2462\u2465\u2466"}], "title": "7.7.40.1. softplus(input, options)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2465\u2467"}, {"id": "ref-for-mloperand\u2462\u2465\u2468"}], "title": "7.7.41. softsign"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u24ea"}, {"id": "ref-for-mloperand\u2462\u2466\u2460"}], "title": "7.7.41.1. softsign(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2461"}, {"id": "ref-for-mloperand\u2462\u2466\u2462"}, {"id": "ref-for-mloperand\u2462\u2466\u2463"}, {"id": "ref-for-mloperand\u2462\u2466\u2464"}], "title": "7.7.42. split"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2465"}, {"id": "ref-for-mloperand\u2462\u2466\u2466"}], "title": "7.7.43. tanh"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2466\u2467"}, {"id": "ref-for-mloperand\u2462\u2466\u2468"}], "title": "7.7.43.1. tanh(input)"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u24ea"}, {"id": "ref-for-mloperand\u2462\u2467\u2460"}, {"id": "ref-for-mloperand\u2462\u2467\u2461"}, {"id": "ref-for-mloperand\u2462\u2467\u2462"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u2463"}, {"id": "ref-for-mloperand\u2462\u2467\u2464"}, {"id": "ref-for-mloperand\u2462\u2467\u2465"}, {"id": "ref-for-mloperand\u2462\u2467\u2466"}], "title": "7.7.45. triangular"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2467\u2467"}, {"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"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-mloperand\u2462\u2468\u2465"}, {"id": "ref-for-mloperand\u2462\u2468\u2466"}, {"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"}, {"id": "ref-for-mloperand\u2463\u24ea\u2465"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-mloperand\u2463\u24ea\u2466"}, {"id": "ref-for-mloperand\u2463\u24ea\u2467"}, {"id": "ref-for-mloperand\u2463\u24ea\u2468"}, {"id": "ref-for-mloperand\u2463\u2460\u24ea"}, {"id": "ref-for-mloperand\u2463\u2460\u2460"}, {"id": "ref-for-mloperand\u2463\u2460\u2461"}], "title": "7.8.1. Creating MLOperand"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2460\u2462"}, {"id": "ref-for-mloperand\u2463\u2460\u2463"}], "title": "7.8.2. dataType"}, {"refs": [{"id": "ref-for-mloperand\u2463\u2460\u2464"}, {"id": "ref-for-mloperand\u2463\u2460\u2465"}], "title": "7.8.3. shape"}], "external": false};
 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": "7.8. 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": "7.8.1. Creating 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"}, {"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"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea"}], "title": "7.7.3. batchNormalization"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460"}], "title": "7.7.4.2. buildSync(outputs)"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2464"}], "title": "7.7.7. concat"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2463"}], "title": "7.7.9. conv2d"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2461\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2462"}], "title": "7.7.10. convTranspose2d"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2467"}], "title": "7.7.11. Element-wise binary operations"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2462\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2462"}], "title": "7.7.12. Element-wise logical operations"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2463"}], "title": "7.7.15. expand"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2467"}], "title": "7.7.16. gather"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2463\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2460"}], "title": "7.7.17. gemm"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2463"}], "title": "7.7.18. gru"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2466"}], "title": "7.7.19. gruCell"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2464\u2468"}], "title": "7.7.23. instanceNormalization"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2462"}], "title": "7.7.24. layerNormalization"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2465\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2467"}], "title": "7.7.27. lstm"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2466\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2462"}], "title": "7.7.28. lstmCell"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2465"}], "title": "7.7.29. matmul"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2467"}], "title": "7.7.30. pad"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2467\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u24ea"}], "title": "7.7.31. Pooling operations"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2460"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2461"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2462"}], "title": "7.7.32. prelu"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2463"}], "title": "7.7.33. Reduction operations"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2468\u2468"}], "title": "7.7.35. resample2d"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2460"}], "title": "7.7.36. reshape"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2461"}], "title": "7.7.39.1. softmax(input)"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2464"}], "title": "7.7.44. transpose"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2465"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2466"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2467"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u24ea\u2468"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u24ea"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2460"}], "title": "7.7.46. where"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2461"}], "title": "7.8. MLOperand interface"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2462"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2463"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2464"}, {"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2465"}], "title": "7.8.1. Creating MLOperand"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2466"}], "title": "7.8.2. dataType"}, {"refs": [{"id": "ref-for-dom-mloperand-descriptor-slot\u2460\u2460\u2467"}], "title": "7.8.3. shape"}], "external": false};
 window.dfnpanelData['dom-mloperand-name-slot'] = {"dfnID": "dom-mloperand-name-slot", "url": "#dom-mloperand-name-slot", "dfnText": "[[name]]", "refSections": [{"refs": [{"id": "ref-for-dom-mloperand-name-slot"}, {"id": "ref-for-dom-mloperand-name-slot\u2460"}], "title": "7.7.4.2. buildSync(outputs)"}, {"refs": [{"id": "ref-for-dom-mloperand-name-slot\u2461"}], "title": "7.7.22. input"}, {"refs": [{"id": "ref-for-dom-mloperand-name-slot\u2462"}, {"id": "ref-for-dom-mloperand-name-slot\u2463"}, {"id": "ref-for-dom-mloperand-name-slot\u2464"}], "title": "7.8.1. Creating MLOperand"}], "external": false};