code: def test_gradient_penalty_loss_lambda_for_zero_gradient(self):
      discriminator = tf.keras.Sequential()
      discriminator.add(tf.keras.layers.Reshape((4,)))
      # Generates a dense layer that is initialized with all zeros.
      # This leads to a network that has zero gradient for any input.
      discriminator.add(
          tf.keras.layers.Dense(
              units=1,
              kernel_initializer="zeros",
              bias_initializer="zeros",
          )
      )
      real_data = tf.ones(shape=(1, 2, 2))
      generated_data = tf.ones(shape=(1, 2, 2))
      weight = 1.0
  
      gradient_penalty = losses.gradient_penalty_loss(
          real_data=real_data,
          generated_data=generated_data,
          discriminator=discriminator,
          weight=weight,
      )
  
      # Tolerance is large due to eps that is added in the gradient pentaly loss
      # for numerical stability at 0.
      self.assertAllClose(gradient_penalty, (weight,), atol=0.001)
  
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 7 for calling the LossesTest obj's underlying member method code with example usage: obj.test_gradient_penalty_loss_lambda_for_zero_gradient() without return types