code: def create_discriminator(latent_size, data_size):  # pragma: no cover
      """Create the discriminator of the GAN for a given latent size.
  
      Parameters
      ----------
      latent_size : int
          The size of the latent space of the generator.
  
      data_size : int
          Size of the input data.
  
      Returns
      -------
      D : Keras model() object
          Returns a model() object.
      """
  
      dis = Sequential()
      dis.add(
          Dense(
              int(math.ceil(math.sqrt(data_size))),
              input_dim=latent_size,
              activation="relu",
              kernel_initializer=keras.initializers.VarianceScaling(
                  scale=1.0,
                  mode="fan_in",
                  distribution="normal",
                  seed=None,
              ),
          )
      )
      dis.add(
          Dense(
              1,
              activation="sigmoid",
              kernel_initializer=keras.initializers.VarianceScaling(
                  scale=1.0,
                  mode="fan_in",
                  distribution="normal",
                  seed=None,
              ),
          )
      )
      data = Input(shape=(latent_size,))
      fake = dis(data)
      return Model(data, fake)
  
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 1 for calling a global function with example usage: create_discriminator(latent_size, data_size) and returns: Model