code: def test_gradient_penalty_loss_with_unequal_number_of_elements_raises(
      self,
  ):
      discriminator = tf.keras.Sequential()
  
      with self.assertRaisesRegex(
          ValueError,
          "number of elements in real_data and generated_data are "
          "expected to be equal",
      ):
          losses.gradient_penalty_loss(
              real_data=(tf.ones((1,)),),
              generated_data=(tf.ones((1,)), tf.ones((1,))),
              discriminator=discriminator,
          )
  
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 10 for calling the LossesTest obj's underlying member method code with example usage: obj.test_gradient_penalty_loss_with_unequal_number_of_elements_raises() without return types