How to use the @tensorflow/tfjs-node-gpu.util function in @tensorflow/tfjs-node-gpu

let numTensors;
  let logWriter;
  if (args.logDir) {
    console.log(`Logging to tensorboard at logdir: ${args.logDir}`);
    logWriter = tf.node.summaryFileWriter(args.logDir);
  }

  let step = 0;
  for (let epoch = 0; epoch < args.epochs; ++epoch) {
    // Write some metadata to disk at the beginning of every epoch.
    fs.writeFileSync(
        metadataPath,
        JSON.stringify(makeMetadata(args.epochs, epoch, false)));

    const tBatchBegin = tf.util.now();

    const numBatches = Math.ceil(xTrain.shape[0] / args.batchSize);

    for (let batch = 0; batch < numBatches; ++batch) {
      const actualBatchSize = (batch + 1) * args.batchSize >= xTrain.shape[0] ?
          (xTrain.shape[0] - batch * args.batchSize) :
          args.batchSize;

      const dLoss = await trainDiscriminatorOneStep(
          xTrain, yTrain, batch * args.batchSize, actualBatchSize,
          args.latentSize, generator, discriminator);

      // Here we use 2 * actualBatchSize here, so that we have
      // the generator optimizer over an identical number of images
      // as the discriminator.
      const gLoss = await trainCombinedModelOneStep(

function buildGenerator(latentSize) {
  tf.util.assert(
      latentSize > 0 && Number.isInteger(latentSize),
      `Expected latent-space size to be a positive integer, but ` +
          `got ${latentSize}.`);

  const cnn = tf.sequential();

  // The number of units is chosen so that when the output is reshaped
  // and fed through the subsequent conv2dTranspose layers, the tensor
  // that comes out at the end has the exact shape that matches MNIST
  // images ([28, 28, 1]).
  cnn.add(tf.layers.dense(
      {units: 3 * 3 * 384, inputShape: [latentSize], activation: 'relu'}));
  cnn.add(tf.layers.reshape({targetShape: [3, 3, 384]}));

  // Upsample from [3, 3, ...] to [7, 7, ...].
  cnn.add(tf.layers.conv2dTranspose({