const context = await navigator.ml.createContext({powerPreference: 'low-power'});

// The following code builds a graph as:
// constant1 ---+
//              +--- Add ---> intermediateOutput1 ---+
// input1    ---+                                    |
//                                                   +--- Mul---> output
// constant2 ---+                                    |
//              +--- Add ---> intermediateOutput2 ---+
// input2    ---+

// Use tensors in 4 dimensions.
const TENSOR_DIMS = [1, 2, 2, 2];
const TENSOR_SIZE = 8;

const builder = new MLGraphBuilder(context);

// Create MLOperandDescriptor object.
const desc = {type: 'float32', dimensions: TENSOR_DIMS};

// constant1 is a constant MLOperand with the value 0.5.
const constantBuffer1 = new Float32Array(TENSOR_SIZE).fill(0.5);
const constant1 = builder.constant(desc, constantBuffer1);

// input1 is one of the input MLOperands. Its value will be set before execution.
const input1 = builder.input('input1', desc);

// constant2 is another constant MLOperand with the value 0.5.
const constantBuffer2 = new Float32Array(TENSOR_SIZE).fill(0.5);
const constant2 = builder.constant(desc, constantBuffer2);
								

									// input2 is another input MLOperand. Its value will be set before execution.
const input2 = builder.input('input2', desc);

// intermediateOutput1 is the output of the first Add operation.
const intermediateOutput1 = builder.add(constant1, input1);

// intermediateOutput2 is the output of the second Add operation.
const intermediateOutput2 = builder.add(constant2, input2);

// output is the output MLOperand of the Mul operation.
const output = builder.mul(intermediateOutput1, intermediateOutput2);

// Compile the constructed graph.
const graph = await builder.build({'output': output});

// Setup the input buffers with value 1.
const inputBuffer1 = new Float32Array(TENSOR_SIZE).fill(1);
const inputBuffer2 = new Float32Array(TENSOR_SIZE).fill(1);
const outputBuffer = new Float32Array(TENSOR_SIZE);

// Execute the compiled graph with the specified inputs.
const inputs = {
	'input1': inputBuffer1,
	'input2': inputBuffer2,
};
const outputs = {'output': outputBuffer};
const results = await context.compute(graph, inputs, outputs);

console.log('Output value: ' + results.outputs.output);
// Output value: 2.25,2.25,2.25,2.25,2.25,2.25,2.25,2.25
							

										import { InferenceSession } from "onnxruntime-web";

// ...

// Initialize the ONNX model
const initModel = async () => {
	ort.env.wasm.numThreads = 1; // 4
	ort.env.wasm.simd = true;
	ort.env.wasm.proxy = true; 

	const options: InferenceSession.SessionOptions = {
		// provider name: wasm, webnn
		// deviceType: cpu, gpu
		// powerPreference: default, high-performance
		executionProviders: 
			[{ name: "wasm"}], // WebAssembly CPU
	};

	// ...
};

const results = await model.run(feeds);
const output = results[model.outputNames[0]];
								

									import { InferenceSession } from "onnxruntime-web";

// ...

// Initialize the ONNX model
const initModel = async () => {
	env.wasm.numThreads = 1; // 4
	env.wasm.simd = true;
	env.wasm.proxy = true;

	const options: InferenceSession.SessionOptions = {
		// provider name: wasm, webnn
		// deviceType: cpu, gpu
		// powerPreference: default, high-performance
		executionProviders: 
			[{ name: "webnn", deviceType: "gpu", powerPreference: 'default' }],
	};

	// ...
};

const results = await model.run(feeds);
const output = results[model.outputNames[0]];