code: def create_generator(latent_size):  # pragma: no cover
      """Create the generator of the GAN for a given latent size.
  
      Parameters
      ----------
      latent_size : int
          The size of the latent space of the generator
  
      Returns
      -------
      D : Keras model() object
          Returns a model() object.
      """
  
      gen = Sequential()
      gen.add(
          Dense(
              latent_size,
              input_dim=latent_size,
              activation="relu",
              kernel_initializer=keras.initializers.Identity(gain=1.0),
          )
      )
      gen.add(
          Dense(
              latent_size,
              activation="relu",
              kernel_initializer=keras.initializers.Identity(gain=1.0),
          )
      )
      latent = Input(shape=(latent_size,))
      fake_data = gen(latent)
      return Model(latent, fake_data)
  
imports: from __future__ import division
  from __future__ import print_function
  import math
  from .base_dl import _get_tensorflow_version
label_desc: Use image node_id 2 for calling a global function with example usage: create_generator(latent_size) and returns: Model