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

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({
    filters: 192,
    kernelSize: 5,
    strides: 1,
    padding: 'valid',
    activation: 'relu',

function buildDiscriminator() {
  const cnn = tf.sequential();

  cnn.add(tf.layers.conv2d({
    filters: 32,
    kernelSize: 3,
    padding: 'same',
    strides: 2,
    inputShape: [IMAGE_SIZE, IMAGE_SIZE, 1]
  }));
  cnn.add(tf.layers.leakyReLU({alpha: 0.2}));
  cnn.add(tf.layers.dropout({rate: 0.3}));

  cnn.add(tf.layers.conv2d(
      {filters: 64, kernelSize: 3, padding: 'same', strides: 1}));
  cnn.add(tf.layers.leakyReLU({alpha: 0.2}));
  cnn.add(tf.layers.dropout({rate: 0.3}));