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

const [noise, sampledLabels, trick] = tf.tidy(() => {
    // Make new latent vectors.
    const zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    const sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    // We want to train the generator to trick the discriminator.
    // For the generator, we want all the {fake, not-fake} labels to say
    // not-fake.
    const trick = tf.tidy(() => tf.ones([batchSize, 1]).mul(SOFT_ONE));
    return [zVectors, sampledLabels, trick];
  });

const [noise, sampledLabels, trick] = tf.tidy(() => {
    // Make new latent vectors.
    const zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    const sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    // We want to train the generator to trick the discriminator.
    // For the generator, we want all the {fake, not-fake} labels to say
    // not-fake.
    const trick = tf.tidy(() => tf.ones([batchSize, 1]).mul(SOFT_ONE));
    return [zVectors, sampledLabels, trick];
  });

const [x, y, auxY] = tf.tidy(() => {
    const imageBatch = xTrain.slice(batchStart, batchSize);
    const labelBatch = yTrain.slice(batchStart, batchSize).asType('float32');

    // Latent vectors.
    let zVectors = tf.randomUniform([batchSize, latentSize], -1, 1);
    let sampledLabels =
        tf.randomUniform([batchSize, 1], 0, NUM_CLASSES, 'int32')
            .asType('float32');

    const generatedImages =
        generator.predict([zVectors, sampledLabels], {batchSize: batchSize});

    const x = tf.concat([imageBatch, generatedImages], 0);

    const y = tf.tidy(
        () => tf.concat(
            [tf.ones([batchSize, 1]).mul(SOFT_ONE), tf.zeros([batchSize, 1])]));

    const auxY = tf.concat([labelBatch, sampledLabels], 0);
    return [x, y, auxY];
  });