text: import tensorflow as tf
  from tensorflow.keras import layers
  
  def make_generator_model(noise_dim = 62, categorical_dim = 10, continuous_dim = 2):
      model = tf.keras.Sequential()
  
      # Layer 1: Dense 1024
      model.add(layers.Dense(1024, use_bias=False, input_shape=(noise_dim + categorical_dim + continuous_dim,)))
      model.add(layers.BatchNormalization())
      model.add(layers.ReLU())
  
      # Layer 2: Dense 7*7*128
      model.add(layers.Dense(7*7*128))
      model.add(layers.BatchNormalization())
      model.add(layers.ReLU())
  
      # Reshape output to 7x7x128 image
      model.add(layers.Reshape((7, 7, 128)))
      assert model.output_shape == (None, 7, 7, 128) # Note: None is the batch size
  
      model.add(layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same', use_bias=False))
      assert model.output_shape == (None, 14, 14, 64)
      model.add(layers.BatchNormalization())
      model.add(layers.ReLU())
  
      model.add(layers.Conv2DTranspose(1, (4, 4), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
      assert model.output_shape == (None, 28, 28, 1)
  
      return model
      
  def make_discriminator_model(categorical_dim = 10, continous_dim = 2):
      model = tf.keras.Sequential()
  
      #Layer 1: Conv2D 4x4, output : 14x14x64
      model.add(layers.Conv2D(64, (4, 4), strides=(2, 2), padding='same',
                                       input_shape=[28, 28, 1]))
      model.add(layers.BatchNormalization())
      model.add(layers.LeakyReLU(alpha = 0.1))
    
      #Layer 2: Conv2D 4x4, output : 7x7x128
      model.add(layers.Conv2D(128, (4, 4), strides=(2, 2), padding='same'))
      model.add(layers.BatchNormalization())
      model.add(layers.LeakyReLU(alpha = 0.1))
  
      #Layer 3: Dense 128
      model.add(layers.Flatten())
      model.add(layers.Dense(128))
      model.add(layers.BatchNormalization())
      model.add(layers.LeakyReLU(alpha = 0.1))
  
      #Layer 4: prediction layer: Dense (1 + categorical_dim + continous_dim)
      model.add(layers.Dense(1 + categorical_dim + continous_dim))
  
      return model