How to use the @tensorflow/tfjs-node/dist/io/file_system.fileSystem function in @tensorflow/tfjs-node

export async function loadGraphModel(modelName: string):
    Promise {
  if (inNodeJS()) {
    // tslint:disable-next-line:no-require-imports
    const fileSystem = require('@tensorflow/tfjs-node/dist/io/file_system');
    return tfconverter.loadGraphModel(
        fileSystem.fileSystem(`./data/${modelName}/model.json`));
  } else {
    return tfconverter.loadGraphModel(
        `${DATA_SERVER_ROOT}/${modelName}/model.json`);
  }
}

async function run(
    dataPath, metadataPath, outputFolder, epochs, validationSplit = 0.15) {
  const {xsArr, ysArr} = loadJSON(dataPath);
  const metadata = loadJSON(metadataPath);

  const xs = tf.tensor(xsArr, metadata.xsShape);
  const ys = tf.tensor(ysArr, metadata.ysShape);

  const model = getModel(metadata.labels);

  // We use model.fit as the whole dataset comfortably fits in memory.
  await model.fit(xs, ys, {epochs, validationSplit});

  mkdirp(outputFolder);
  await model.save(fileIO.fileSystem(outputFolder));

  const metaOutPath = path.resolve(outputFolder, 'intent_metadata.json');
  const metadataStr = JSON.stringify(metadata, null, 2);
  fs.writeFileSync(metaOutPath, metadataStr, {encoding: 'utf8'});
}

embeddingsPath,
      taggedTokensPath,
      modelOpts.sequenceLength,
      trainingOpts.batchSize,
  );
  const dataset = {iterator: dataIterator};
  const model = getModel(modelOpts);

  console.log('Start training', trainingOpts.epochs);
  await model.fitDataset(dataset, {
    epochs: trainingOpts.epochs,
  });

  mkdirp(outFolder);
  console.log(`Saving model to ${outFolder}`);
  await model.save(fileIO.fileSystem(outFolder));


  // Write out the related metadata
  const metaOutPath = path.resolve(outFolder, 'tagger_metadata.json');
  const metadata = {
    labels: TAGS,
    sequenceLength: modelOpts.sequenceLength,
    embeddingSize: EMBEDDING_SIZE,
  };

  const metadataStr = JSON.stringify(metadata, null, 2);
  fs.writeFileSync(metaOutPath, metadataStr, {encoding: 'utf8'});
}