code: def test_gradient_penalty_shape_correct_sequence_input(self):
      discriminator = tf.keras.Sequential()
      discriminator.add(tf.keras.layers.Concatenate())
      discriminator.add(tf.keras.layers.Reshape((50,)))
      discriminator.add(tf.keras.layers.Dense(units=1))
      real_data = (tf.ones(shape=(3, 5, 5)), tf.ones(shape=(3, 5, 5)))
      generated_data = (
          tf.ones(shape=(3, 5, 5)),
          tf.ones(shape=(3, 5, 5)),
      )
  
      gradient_penalty = losses.gradient_penalty_loss(
          real_data=real_data,
          generated_data=generated_data,
          discriminator=discriminator,
      )
  
      self.assertAllEqual(tf.shape(gradient_penalty), (3,))
  
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 2 for calling the LossesTest obj's underlying member method code with example usage: obj.test_gradient_penalty_shape_correct_sequence_input() without return types