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]];