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diff --git a/testing/web-platform/tests/interfaces/webnn.idl b/testing/web-platform/tests/interfaces/webnn.idl
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+++ b/testing/web-platform/tests/interfaces/webnn.idl
@@ -0,0 +1,611 @@
+// GENERATED CONTENT - DO NOT EDIT
+// Content was automatically extracted by Reffy into webref
+// (https://github.com/w3c/webref)
+// Source: Web Neural Network API (https://webmachinelearning.github.io/webnn/)
+
+interface mixin NavigatorML {
+  [SecureContext, SameObject] readonly attribute ML ml;
+};
+Navigator includes NavigatorML;
+WorkerNavigator includes NavigatorML;
+
+enum MLDeviceType {
+  "cpu",
+  "gpu"
+};
+
+enum MLPowerPreference {
+  "default",
+  "high-performance",
+  "low-power"
+};
+
+dictionary MLContextOptions {
+  MLDeviceType deviceType = "cpu";
+  MLPowerPreference powerPreference = "default";
+};
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface ML {
+  Promise<MLContext> createContext(optional MLContextOptions options = {});
+  Promise<MLContext> createContext(GPUDevice gpuDevice);
+
+  [Exposed=(DedicatedWorker)]
+  MLContext createContextSync(optional MLContextOptions options = {});
+  [Exposed=(DedicatedWorker)]
+  MLContext createContextSync(GPUDevice gpuDevice);
+};
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLActivation {
+};
+
+typedef (GPUBuffer or GPUTexture) MLGPUResource;
+
+typedef record<DOMString, MLGPUResource> MLNamedGPUResources;
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLCommandEncoder {};
+
+partial interface MLCommandEncoder {
+  undefined initializeGraph(MLGraph graph);
+};
+
+partial interface MLCommandEncoder {
+  undefined dispatch(MLGraph graph, MLNamedGPUResources inputs, MLNamedGPUResources outputs);
+};
+
+partial interface MLCommandEncoder {
+  GPUCommandBuffer finish(optional GPUCommandBufferDescriptor descriptor = {});
+};
+
+typedef record<DOMString, ArrayBufferView> MLNamedArrayBufferViews;
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLContext {};
+
+partial interface MLContext {
+  [Exposed=(DedicatedWorker)]
+  undefined computeSync(
+      MLGraph graph, MLNamedArrayBufferViews inputs, MLNamedArrayBufferViews outputs);
+};
+
+dictionary MLComputeResult {
+  MLNamedArrayBufferViews inputs;
+  MLNamedArrayBufferViews outputs;
+};
+
+partial interface MLContext {
+  Promise<MLComputeResult> compute(
+      MLGraph graph, MLNamedArrayBufferViews inputs, MLNamedArrayBufferViews outputs);
+};
+
+partial interface MLContext {
+  MLCommandEncoder createCommandEncoder();
+};
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLGraph {};
+
+typedef record<DOMString, MLOperand> MLNamedOperands;
+
+dictionary MLBufferResourceView {
+  required GPUBuffer resource;
+  unsigned long long offset = 0;
+  unsigned long long size;
+};
+
+typedef (ArrayBufferView or MLBufferResourceView) MLBufferView;
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLGraphBuilder {
+  // Construct the graph builder from the context.
+  constructor(MLContext context);
+
+  // Create an operand for a graph input.
+  MLOperand input(DOMString name, MLOperandDescriptor descriptor);
+
+  // Create an operand for a graph constant.
+  MLOperand constant(MLOperandDescriptor descriptor, MLBufferView bufferView);
+
+  // Create a single-value operand from the specified number of the specified type.
+  MLOperand constant(double value, optional MLOperandDataType type = "float32");
+
+  // Compile the graph up to the specified output operands asynchronously.
+  Promise<MLGraph> build(MLNamedOperands outputs);
+
+  // Compile the graph up to the specified output operands synchronously.
+  [Exposed=(DedicatedWorker)]
+  MLGraph buildSync(MLNamedOperands outputs);
+};
+
+dictionary MLArgMinMaxOptions {
+  sequence<unsigned long> axes = null;
+  boolean keepDimensions = false;
+  boolean selectLastIndex = false;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand argMin(MLOperand input, optional MLArgMinMaxOptions options = {});
+  MLOperand argMax(MLOperand input, optional MLArgMinMaxOptions options = {});
+};
+
+dictionary MLBatchNormalizationOptions {
+  MLOperand scale;
+  MLOperand bias;
+  unsigned long axis = 1;
+  float epsilon = 1e-5;
+  MLActivation activation;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand batchNormalization(MLOperand input, MLOperand mean, MLOperand variance,
+                             optional MLBatchNormalizationOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand cast(MLOperand input, MLOperandDataType type);
+};
+
+dictionary MLClampOptions {
+  float minValue;
+  float maxValue;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand clamp(MLOperand input, optional MLClampOptions options = {});
+  MLActivation clamp(optional MLClampOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand concat(sequence<MLOperand> inputs, unsigned long axis);
+};
+
+enum MLConv2dFilterOperandLayout {
+  "oihw",
+  "hwio",
+  "ohwi",
+  "ihwo"
+};
+
+enum MLAutoPad {
+  "explicit",
+  "same-upper",
+  "same-lower"
+};
+
+dictionary MLConv2dOptions {
+  sequence<unsigned long> padding;
+  sequence<unsigned long> strides;
+  sequence<unsigned long> dilations;
+  MLAutoPad autoPad = "explicit";
+  unsigned long groups = 1;
+  MLInputOperandLayout inputLayout = "nchw";
+  MLConv2dFilterOperandLayout filterLayout = "oihw";
+  MLOperand bias;
+  MLActivation activation;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand conv2d(MLOperand input, MLOperand filter, optional MLConv2dOptions options = {});
+};
+
+enum MLConvTranspose2dFilterOperandLayout {
+  "iohw",
+  "hwoi",
+  "ohwi"
+};
+
+dictionary MLConvTranspose2dOptions {
+  sequence<unsigned long> padding;
+  sequence<unsigned long> strides;
+  sequence<unsigned long> dilations;
+  sequence<unsigned long> outputPadding;
+  sequence<unsigned long> outputSizes;
+  MLAutoPad autoPad = "explicit";
+  unsigned long groups = 1;
+  MLInputOperandLayout inputLayout = "nchw";
+  MLConvTranspose2dFilterOperandLayout filterLayout = "iohw";
+  MLOperand bias;
+  MLActivation activation;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand convTranspose2d(MLOperand input, MLOperand filter,
+                            optional MLConvTranspose2dOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand add(MLOperand a, MLOperand b);
+  MLOperand sub(MLOperand a, MLOperand b);
+  MLOperand mul(MLOperand a, MLOperand b);
+  MLOperand div(MLOperand a, MLOperand b);
+  MLOperand max(MLOperand a, MLOperand b);
+  MLOperand min(MLOperand a, MLOperand b);
+  MLOperand pow(MLOperand a, MLOperand b);
+};
+
+partial interface MLGraphBuilder {
+  MLOperand equal(MLOperand a, MLOperand b);
+  MLOperand greater(MLOperand a, MLOperand b);
+  MLOperand greaterOrEqual(MLOperand a, MLOperand b);
+  MLOperand lesser(MLOperand a, MLOperand b);
+  MLOperand lesserOrEqual(MLOperand a, MLOperand b);
+  MLOperand not(MLOperand a);
+};
+
+partial interface MLGraphBuilder {
+  MLOperand abs(MLOperand input);
+  MLOperand ceil(MLOperand input);
+  MLOperand cos(MLOperand input);
+  MLOperand erf(MLOperand input);
+  MLOperand exp(MLOperand input);
+  MLOperand floor(MLOperand input);
+  MLOperand identity(MLOperand input);
+  MLOperand log(MLOperand input);
+  MLOperand neg(MLOperand input);
+  MLOperand reciprocal(MLOperand input);
+  MLOperand sin(MLOperand input);
+  MLOperand sqrt(MLOperand input);
+  MLOperand tan(MLOperand input);
+};
+
+dictionary MLEluOptions {
+  float alpha = 1;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand elu(MLOperand input, optional MLEluOptions options = {});
+  MLActivation elu(optional MLEluOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand expand(MLOperand input, sequence<unsigned long> newShape);
+};
+
+dictionary MLGatherOptions {
+  unsigned long axis = 0;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand gather(MLOperand input, MLOperand indices, optional MLGatherOptions options = {});
+};
+
+dictionary MLGemmOptions {
+  MLOperand c;
+  float alpha = 1.0;
+  float beta = 1.0;
+  boolean aTranspose = false;
+  boolean bTranspose = false;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand gemm(MLOperand a, MLOperand b, optional MLGemmOptions options = {});
+};
+
+enum MLGruWeightLayout {
+  "zrn",  // update-reset-new gate ordering
+  "rzn"   // reset-update-new gate ordering
+};
+
+enum MLRecurrentNetworkDirection {
+  "forward",
+  "backward",
+  "both"
+};
+
+dictionary MLGruOptions {
+  MLOperand bias;
+  MLOperand recurrentBias;
+  MLOperand initialHiddenState;
+  boolean resetAfter = true;
+  boolean returnSequence = false;
+  MLRecurrentNetworkDirection direction = "forward";
+  MLGruWeightLayout layout = "zrn";
+  sequence<MLActivation> activations;
+};
+
+partial interface MLGraphBuilder {
+  sequence<MLOperand> gru(MLOperand input, MLOperand weight, MLOperand recurrentWeight,
+                          unsigned long steps, unsigned long hiddenSize,
+                          optional MLGruOptions options = {});
+};
+
+dictionary MLGruCellOptions {
+  MLOperand bias;
+  MLOperand recurrentBias;
+  boolean resetAfter = true;
+  MLGruWeightLayout layout = "zrn";
+  sequence<MLActivation> activations;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand gruCell(MLOperand input, MLOperand weight, MLOperand recurrentWeight,
+                    MLOperand hiddenState, unsigned long hiddenSize,
+                    optional MLGruCellOptions options = {});
+};
+
+dictionary MLHardSigmoidOptions {
+  float alpha = 0.2;
+  float beta = 0.5;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand hardSigmoid(MLOperand input, optional MLHardSigmoidOptions options = {});
+  MLActivation hardSigmoid(optional MLHardSigmoidOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand hardSwish(MLOperand input);
+  MLActivation hardSwish();
+};
+
+dictionary MLInstanceNormalizationOptions {
+  MLOperand scale;
+  MLOperand bias;
+  float epsilon = 1e-5;
+  MLInputOperandLayout layout = "nchw";
+};
+
+partial interface MLGraphBuilder {
+  MLOperand instanceNormalization(MLOperand input,
+                                optional MLInstanceNormalizationOptions options = {});
+};
+
+dictionary MLLayerNormalizationOptions {
+  MLOperand scale;
+  MLOperand bias;
+  sequence<unsigned long> axes;
+  float epsilon = 1e-5;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand layerNormalization(MLOperand input, optional MLLayerNormalizationOptions options = {});
+};
+
+dictionary MLLeakyReluOptions {
+  float alpha = 0.01;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand leakyRelu(MLOperand input, optional MLLeakyReluOptions options = {});
+  MLActivation leakyRelu(optional MLLeakyReluOptions options = {});
+};
+
+dictionary MLLinearOptions {
+  float alpha = 1;
+  float beta = 0;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand linear(MLOperand input, optional MLLinearOptions options = {});
+  MLActivation linear(optional MLLinearOptions options = {});
+};
+
+enum MLLstmWeightLayout {
+  "iofg", // input-output-forget-cell gate ordering
+  "ifgo"  // input-forget-cell-output gate ordering
+};
+
+dictionary MLLstmOptions {
+  MLOperand bias;
+  MLOperand recurrentBias;
+  MLOperand peepholeWeight;
+  MLOperand initialHiddenState;
+  MLOperand initialCellState;
+  boolean returnSequence = false;
+  MLRecurrentNetworkDirection direction = "forward";
+  MLLstmWeightLayout layout = "iofg";
+  sequence<MLActivation> activations;
+};
+
+partial interface MLGraphBuilder {
+  sequence<MLOperand> lstm(MLOperand input, MLOperand weight, MLOperand recurrentWeight,
+                           unsigned long steps, unsigned long hiddenSize,
+                           optional MLLstmOptions options = {});
+};
+
+dictionary MLLstmCellOptions {
+  MLOperand bias;
+  MLOperand recurrentBias;
+  MLOperand peepholeWeight;
+  MLLstmWeightLayout layout = "iofg";
+  sequence<MLActivation> activations;
+};
+
+partial interface MLGraphBuilder {
+  sequence<MLOperand> lstmCell(MLOperand input, MLOperand weight, MLOperand recurrentWeight,
+                               MLOperand hiddenState, MLOperand cellState, unsigned long hiddenSize,
+                               optional MLLstmCellOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand matmul(MLOperand a, MLOperand b);
+};
+
+enum MLPaddingMode {
+  "constant",
+  "edge",
+  "reflection",
+  "symmetric"
+};
+
+dictionary MLPadOptions {
+  MLPaddingMode mode = "constant";
+  float value = 0;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand pad(MLOperand input,
+                sequence<unsigned long> beginningPadding,
+                sequence<unsigned long> endingPadding,
+                optional MLPadOptions options = {});
+};
+
+enum MLRoundingType {
+  "floor",
+  "ceil"
+};
+
+dictionary MLPool2dOptions {
+  sequence<unsigned long> windowDimensions;
+  sequence<unsigned long> padding;
+  sequence<unsigned long> strides;
+  sequence<unsigned long> dilations;
+  MLAutoPad autoPad = "explicit";
+  MLInputOperandLayout layout = "nchw";
+  MLRoundingType roundingType = "floor";
+  sequence<unsigned long> outputSizes;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand averagePool2d(MLOperand input, optional MLPool2dOptions options = {});
+  MLOperand l2Pool2d(MLOperand input, optional MLPool2dOptions options = {});
+  MLOperand maxPool2d(MLOperand input, optional MLPool2dOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand prelu(MLOperand input, MLOperand slope);
+};
+
+dictionary MLReduceOptions {
+  sequence<unsigned long> axes = null;
+  boolean keepDimensions = false;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand reduceL1(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceL2(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceLogSum(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceLogSumExp(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceMax(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceMean(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceMin(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceProduct(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceSum(MLOperand input, optional MLReduceOptions options = {});
+  MLOperand reduceSumSquare(MLOperand input, optional MLReduceOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand relu(MLOperand input);
+  MLActivation relu();
+};
+
+enum MLInterpolationMode {
+  "nearest-neighbor",
+  "linear"
+};
+
+dictionary MLResample2dOptions {
+  MLInterpolationMode mode = "nearest-neighbor";
+  sequence<float> scales;
+  sequence<unsigned long> sizes;
+  sequence<unsigned long> axes;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand resample2d(MLOperand input, optional MLResample2dOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand reshape(MLOperand input, sequence<unsigned long> newShape);
+};
+
+partial interface MLGraphBuilder {
+  MLOperand sigmoid(MLOperand input);
+  MLActivation sigmoid();
+};
+
+partial interface MLGraphBuilder {
+  MLOperand slice(MLOperand input, sequence<unsigned long> starts, sequence<unsigned long> sizes);
+};
+
+partial interface MLGraphBuilder {
+  MLOperand softmax(MLOperand input);
+  MLActivation softmax();
+};
+
+dictionary MLSoftplusOptions {
+  float steepness = 1;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand softplus(MLOperand input, optional MLSoftplusOptions options = {});
+  MLActivation softplus(optional MLSoftplusOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand softsign(MLOperand input);
+  MLActivation softsign();
+};
+
+dictionary MLSplitOptions {
+  unsigned long axis = 0;
+};
+
+partial interface MLGraphBuilder {
+  sequence<MLOperand> split(MLOperand input,
+                          (unsigned long or sequence<unsigned long>) splits,
+                          optional MLSplitOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand tanh(MLOperand input);
+  MLActivation tanh();
+};
+
+dictionary MLTransposeOptions {
+  sequence<unsigned long> permutation;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand transpose(MLOperand input, optional MLTransposeOptions options = {});
+};
+
+dictionary MLTriangularOptions {
+  boolean upper = true;
+  long diagonal = 0;
+};
+
+partial interface MLGraphBuilder {
+  MLOperand triangular(MLOperand input, optional MLTriangularOptions options = {});
+};
+
+partial interface MLGraphBuilder {
+  MLOperand where(MLOperand condition, MLOperand input, MLOperand other);
+};
+
+[SecureContext, Exposed=(Window, DedicatedWorker)]
+interface MLOperand {};
+
+partial interface MLOperand {
+  MLOperandDataType dataType();
+};
+
+partial interface MLOperand {
+  sequence<unsigned long> shape();
+};
+
+enum MLInputOperandLayout {
+  "nchw",
+  "nhwc"
+};
+
+enum MLOperandDataType {
+  "float32",
+  "float16",
+  "int32",
+  "uint32",
+  "int64",
+  "uint64",
+  "int8",
+  "uint8"
+};
+
+dictionary MLOperandDescriptor {
+  // The operand type.
+  required MLOperandDataType dataType;
+
+  // The dimensions field is only required for tensor operands.
+  sequence<unsigned long> dimensions;
+};