code: def wasserstein_hinge_discriminator_loss(
      discriminator_output_real_data: tf.Tensor,
      discriminator_output_generated_data: tf.Tensor,
      name: str = "wasserstein_hinge_discriminator_loss",
  ) -> tf.Tensor:
      """Discriminator loss for the hinge Wasserstein GAN.
  
      This loss function is generally used together with a regularization of the
      discriminator such as weight clipping (https://arxiv.org/abs/1701.07875),
      gradient penalty (https://arxiv.org/abs/1704.00028) or spectral
      normalization (https://arxiv.org/abs/1802.05957).
  
      Args:
        discriminator_output_real_data: Output of the discriminator for the real
          data.
        discriminator_output_generated_data: Output of the discriminator for
          generated data.
        name: The name of the name_scope that is placed around the loss.
  
      Returns:
        The loss for the discriminator.
      """
      with tf.name_scope(name=name):
          return tf.nn.relu(
              1.0 - discriminator_output_real_data
          ) + tf.nn.relu(discriminator_output_generated_data + 1.0)
  
file_path: graphics/tensorflow_graphics/projects/gan/losses.py
imports: from collections import abc
  from typing import Sequence, Union
  import tensorflow
label_desc: Use image node_id 7 for calling a global function with example usage: wasserstein_hinge_discriminator_loss(discriminator_output_real_data, discriminator_output_generated_data, name) and returns: unknown