code: def test_gradient_penalty(self):
      def dummy_discriminator(
          transition: types.Transition,
      ) -> networks_lib.Logits:
          return transition.observation + jnp.square(transition.action)
  
      zero_transition = types.Transition(0.0, 0.0, 0.0, 0.0, 0.0)
      zero_transition = tree.map_structure(
          lambda x: jnp.expand_dims(x, axis=0), zero_transition
      )
      self.assertEqual(
          losses._compute_gradient_penalty(
              zero_transition, dummy_discriminator, 0.0
          ),
          1**2 + 0**2,
      )
  
      one_transition = types.Transition(1.0, 1.0, 0.0, 0.0, 0.0)
      one_transition = tree.map_structure(
          lambda x: jnp.expand_dims(x, axis=0), one_transition
      )
      self.assertEqual(
          losses._compute_gradient_penalty(
              one_transition, dummy_discriminator, 0.0
          ),
          1**2 + 2**2,
      )
  
file_path: acme/acme/agents/jax/ail/losses_test.py
imports: from acme import types
  from acme.agents.jax.ail import losses
  from acme.jax import networks
  import jax
  import jax.numpy
  import tree
  from absl.testing import absltest
label_desc: Use image node_id 1 for calling the AilLossTest obj's underlying member method code with example usage: obj.test_gradient_penalty() and returns: unknown