code: def test_wasserstein_hinge_discriminator_loss_correct_value(
      self, real_data_input, generated_data_input, expected_loss_value
  ):
      real_data_input = tf.fill(dims=(), value=real_data_input)
      generated_data_input = tf.fill(
          dims=(), value=generated_data_input
      )
  
      loss = self.evaluate(
          losses.wasserstein_hinge_discriminator_loss(
              real_data_input, generated_data_input
          )
      )
  
      self.assertAlmostEqual(loss, expected_loss_value)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_graphics.projects.gan import losses
  from tensorflow_graphics.util import test_case
label_desc: Use image node_id 25 for calling the LossesTest obj's underlying member method code with example usage: obj.test_wasserstein_hinge_discriminator_loss_correct_value(real_data_input, generated_data_input, expected_loss_value) without return types