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',
    kernelInitializer: 'glorotNormal'
  }));
  cnn.add(tf.layers.batchNormalization());

  // Upsample to [14, 14, ...].
  cnn.add(tf.layers.conv2dTranspose({
    filters: 96,
    kernelSize: 5,
    strides: 2,
    padding: 'same',
    activation: 'relu',
    kernelInitializer: 'glorotNormal'
  }));
  cnn.add(tf.layers.batchNormalization());

  // Upsample to [28, 28, ...].
  cnn.add(tf.layers.conv2dTranspose({
    filters: 1,
    kernelSize: 5,
    strides: 2,
    padding: 'same',
    activation: 'tanh',
    kernelInitializer: 'glorotNormal'
  }));

  // Unlike most TensorFlow.js models, the generator part of an ACGAN has
  // two inputs:
  //   1. The latent vector that is used as the "seed" of the fake image
  //      generation.
  //   2. A class label that controls which of the ten MNIST digit classes
  //      the generated fake image is meant to belong to.

  // This is the z space commonly referred to in GAN papers.
  const latent = tf.input({shape: [latentSize]});

  // The desired label of the generated image, an integer in the interval
  // [0, NUM_CLASSES).
  const imageClass = tf.input({shape: [1]});

  // The desired label is converted to a vector of length `latentSize`
  // through embedding lookup.
  const classEmbedding = tf.layers.embedding({
    inputDim: NUM_CLASSES,
    outputDim: latentSize,
    embeddingsInitializer: 'glorotNormal'
  }).apply(imageClass);

  // Hadamard product between z-space and a class conditional embedding.
  const h = tf.layers.multiply().apply([latent, classEmbedding]);

  const fakeImage = cnn.apply(h);
  return tf.model({inputs: [latent, imageClass], outputs: fakeImage});
}
Build the generator part of ACGAN.

The generator of ACGAN takes two inputs:

  1. A random latent-space vector (the latent space is often referred to
     as "z-space" in GAN literature).
  2. A label for the desired image category (0, 1, ..., 9).

It generates one output: the generated (i.e., fake) image.

@param {number} latentSize Size of the latent space.
@returns {tf.LayersModel} The generator model.