import '@webmachinelearning/webnn-polyfill';

    const backend = 'cpu';
    const context = await navigator.ml.createContext();
    const tf = context.tf;
    await tf.setBackend(backend);
    await tf.ready();

    const backend = 'webgl'; // 'cpu' or 'wasm'
    const context = await navigator.ml.createContext();
    const tf = context.tf;
    await tf.setBackend(backend);
    await tf.ready();

    const backend = 'wasm';
    const context = await navigator.ml.createContext();
    const wasm = context.wasm;

    // 1- Enforce use Wasm SIMD binary
    wasm.setWasmPath(`${path}/tfjs-backend-wasm-simd.wasm`);

    // 2- Use Wasm SIMD + Threads bianry if supported both SIMD and Threads
    // 2.1- Configure by the path to the directory where the WASM binaries are located
    //        wasm.setWasmPaths(`https://unpkg.com/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`);
    //      or mapping from names of WASM binaries to custom full paths specifying the locations of those binaries
    //        wasm.setWasmPaths({
    //          'tfjs-backend-wasm.wasm': 'renamed.wasm',
    //          'tfjs-backend-wasm-simd.wasm': 'renamed-simd.wasm',
    //          'tfjs-backend-wasm-threaded-simd.wasm': 'renamed-threaded-simd.wasm'
    //        });
    wasm.setWasmPaths(${prefixOrFileMap}); 
    // 2.2- Configure threads number manually, or it will use the number of logical CPU cores as the threads count by default
    wasm.setThreadsCount(n); // n can be 1, 2, ...

    const tf = context.tf;
    await tf.setBackend(backend);
    await tf.ready();