// 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 MLDevicePreference {
  "default",
  "gpu",
  "cpu"
};

enum MLPowerPreference {
  "default",
  "high-performance",
  "low-power"
};

dictionary MLContextOptions {
  MLDevicePreference devicePreference = "default";
  MLPowerPreference powerPreference = "default";
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface ML {
  MLContext createContext(optional MLContextOptions options = {});
  MLContext createContext(WebGLRenderingContext glContext);
  MLContext createContext(GPUDevice gpuDevice);
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLContext {};

enum MLInputOperandLayout {
  "nchw",
  "nhwc"
};

enum MLOperandType {
  "float32",
  "float16",
  "int32",
  "uint32",
  "int8",
  "uint8"
};

dictionary MLOperandDescriptor {
  // The operand type.
  required MLOperandType type;

  // The dimensions field is only required for tensor operands.
  // The negative value means an unknown dimension.
  sequence<long> dimensions;
};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLOperand {};

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLOperator {};

typedef record<DOMString, MLOperand> MLNamedOperands;

dictionary MLBufferResourceView {
  required (WebGLBuffer or 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 desc);

  // Create an operand for a graph constant.
  MLOperand constant(MLOperandDescriptor desc, MLBufferView bufferView);

  // Create a single-value operand from the specified number of the specified type.
  MLOperand constant(double value, optional MLOperandType type = "float32");

  // Compile the graph up to the specified output operands
  MLGraph build(MLNamedOperands outputs);
};

dictionary MLBatchNormalizationOptions {
  MLOperand scale;
  MLOperand bias;
  long axis = 1;
  float epsilon = 1e-5;
  MLOperator activation;
};

partial interface MLGraphBuilder {
  MLOperand batchNormalization(MLOperand input, MLOperand mean, MLOperand variance,
                             optional MLBatchNormalizationOptions options = {});
};

dictionary MLClampOptions {
  float minValue;
  float maxValue;
};

partial interface MLGraphBuilder {
  MLOperand clamp(MLOperand x, optional MLClampOptions options = {});
  MLOperator clamp(optional MLClampOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand concat(sequence<MLOperand> inputs, long axis);
};

enum MLConv2dFilterOperandLayout {
  "oihw",
  "hwio",
  "ohwi",
  "ihwo"
};

enum MLAutoPad {
  "explicit",
  "same-upper",
  "same-lower"
};

dictionary MLConv2dOptions {
  sequence<long> padding;
  sequence<long> strides;
  sequence<long> dilations;
  MLAutoPad autoPad = "explicit";
  long groups = 1;
  MLInputOperandLayout inputLayout = "nchw";
  MLConv2dFilterOperandLayout filterLayout = "oihw";
  MLOperand bias;
  MLOperator activation;
};

partial interface MLGraphBuilder {
  MLOperand conv2d(MLOperand input, MLOperand filter, optional MLConv2dOptions options = {});
};

enum MLConvTranspose2dFilterOperandLayout {
  "iohw",
  "hwoi",
  "ohwi"
};

dictionary MLConvTranspose2dOptions {
  sequence<long> padding;
  sequence<long> strides;
  sequence<long> dilations;
  sequence<long> outputPadding;
  sequence<long> outputSizes;
  MLAutoPad autoPad = "explicit";
  long groups = 1;
  MLInputOperandLayout inputLayout = "nchw";
  MLConvTranspose2dFilterOperandLayout filterLayout = "iohw";
  MLOperand bias;
  MLOperator 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 abs(MLOperand x);
  MLOperand ceil(MLOperand x);
  MLOperand cos(MLOperand x);
  MLOperand exp(MLOperand x);
  MLOperand floor(MLOperand x);
  MLOperand log(MLOperand x);
  MLOperand neg(MLOperand x);
  MLOperand sin(MLOperand x);
  MLOperand tan(MLOperand x);
};

dictionary MLEluOptions {
  float alpha = 1;
};

partial interface MLGraphBuilder {
  MLOperand elu(MLOperand x, optional MLEluOptions options = {});
  MLOperator elu(optional MLEluOptions 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 MLRecurrentNetworkWeightLayout {
  "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";
  MLRecurrentNetworkWeightLayout layout = "zrn";
  sequence<MLOperator> activations;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> gru(MLOperand input, MLOperand weight, MLOperand recurrentWeight,

                        long steps, long hiddenSize, optional MLGruOptions options = {});
};

dictionary MLGruCellOptions {
  MLOperand bias;
  MLOperand recurrentBias;
  boolean resetAfter = true;
  MLRecurrentNetworkWeightLayout layout = "zrn";
  sequence<MLOperator> activations;
};

partial interface MLGraphBuilder {
  MLOperand gruCell(MLOperand input, MLOperand weight, MLOperand recurrentWeight,

                  MLOperand hiddenState, long hiddenSize, optional MLGruCellOptions options = {});
};

dictionary MLHardSigmoidOptions {
  float alpha = 0.2;
  float beta = 0.5;
};

partial interface MLGraphBuilder {
  MLOperand hardSigmoid(MLOperand x, optional MLHardSigmoidOptions options = {});
  MLOperator hardSigmoid(optional MLHardSigmoidOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand hardSwish(MLOperand x);
  MLOperator 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 MLLeakyReluOptions {
  float alpha = 0.01;
};

partial interface MLGraphBuilder {
  MLOperand leakyRelu(MLOperand x, optional MLLeakyReluOptions options = {});
  MLOperator leakyRelu(optional MLLeakyReluOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand matmul(MLOperand a, MLOperand b);
};

dictionary MLLinearOptions {
  float alpha = 1;
  float beta = 0;
};

partial interface MLGraphBuilder {
  MLOperand linear(MLOperand x, optional MLLinearOptions options = {});
  MLOperator linear(optional MLLinearOptions options = {});
};

enum MLPaddingMode {
  "constant",
  "edge",
  "reflection",
  "symmetric"
};

dictionary MLPadOptions {
  MLPaddingMode mode = "constant";
  float value = 0;
};

partial interface MLGraphBuilder {
  MLOperand pad(MLOperand input, MLOperand padding, optional MLPadOptions options = {});
};

enum MLRoundingType {
  "floor",
  "ceil"
};

dictionary MLPool2dOptions {
  sequence<long> windowDimensions;
  sequence<long> padding;
  sequence<long> strides;
  sequence<long> dilations;
  MLAutoPad autoPad = "explicit";
  MLInputOperandLayout layout = "nchw";
  MLRoundingType roundingType = "floor";
  sequence<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 = {});
};

dictionary MLReduceOptions {
  sequence<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 x);
  MLOperator relu();
};

enum MLInterpolationMode {
  "nearest-neighbor",
  "linear"
};

dictionary MLResample2dOptions {
  MLInterpolationMode mode = "nearest-neighbor";
  sequence<float> scales;
  sequence<long> sizes;
  sequence<long> axes;
};

partial interface MLGraphBuilder {
  MLOperand resample2d(MLOperand input, optional MLResample2dOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand reshape(MLOperand input, sequence<long> newShape);
};

partial interface MLGraphBuilder {
  MLOperand sigmoid(MLOperand x);
  MLOperator sigmoid();
};

dictionary MLSliceOptions {
  sequence<long> axes;
};

partial interface MLGraphBuilder {
  MLOperand slice(MLOperand input, sequence<long> starts, sequence<long> sizes,
                optional MLSliceOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand softmax(MLOperand x);
};

dictionary MLSoftplusOptions {
  float steepness = 1;
};

partial interface MLGraphBuilder {
  MLOperand softplus(MLOperand x, optional MLSoftplusOptions options = {});
  MLOperator softplus(optional MLSoftplusOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand softsign(MLOperand x);
  MLOperator softsign();
};

dictionary MLSplitOptions {
  long axis = 0;
};

partial interface MLGraphBuilder {
  sequence<MLOperand> split(MLOperand input,
                          (unsigned long or sequence<unsigned long>) splits,
                          optional MLSplitOptions options = {});
};

dictionary MLSqueezeOptions {
  sequence<long> axes;
};

partial interface MLGraphBuilder {
  MLOperand squeeze(MLOperand input, optional MLSqueezeOptions options = {});
};

partial interface MLGraphBuilder {
  MLOperand tanh(MLOperand x);
  MLOperator tanh();
};

dictionary MLTransposeOptions {
  sequence<long> permutation;
};

partial interface MLGraphBuilder {
  MLOperand transpose(MLOperand input, optional MLTransposeOptions options = {});
};

typedef (MLBufferView or WebGLTexture or GPUTexture) MLResource;

dictionary MLInput {
  required MLResource resource;
  required sequence<long> dimensions;
};

typedef record<DOMString, (MLResource or MLInput)> MLNamedInputs;
typedef record<DOMString, MLResource> MLNamedOutputs;

[SecureContext, Exposed=(Window, DedicatedWorker)]
interface MLGraph {
  undefined compute(MLNamedInputs inputs, MLNamedOutputs outputs);
};