code: def test_step(self):
      simple_spec = specs.Array(shape=(), dtype=float)
  
      spec = specs.EnvironmentSpec(
          simple_spec, simple_spec, simple_spec, simple_spec
      )
  
      discriminator = _make_discriminator(spec)
      ail_network = ail_networks.AILNetworks(
          discriminator,
          imitation_reward_fn=lambda x: x,
          direct_rl_networks=None,
      )
  
      loss = losses.gail_loss()
  
      optimizer = optax.adam(0.01)
  
      step = jax.jit(
          functools.partial(
              ail_learning.ail_update_step,
              optimizer=optimizer,
              ail_network=ail_network,
              loss_fn=loss,
          )
      )
  
      zero_transition = types.Transition(
          np.array([0.0]), np.array([0.0]), 0.0, 0.0, np.array([0.0])
      )
      zero_transition = utils.add_batch_dim(zero_transition)
  
      one_transition = types.Transition(
          np.array([1.0]), np.array([0.0]), 0.0, 0.0, np.array([0.0])
      )
      one_transition = utils.add_batch_dim(one_transition)
  
      key = jax.random.PRNGKey(0)
      discriminator_params, discriminator_state = discriminator.init(
          key
      )
  
      state = ail_learning.DiscriminatorTrainingState(
          optimizer_state=optimizer.init(discriminator_params),
          discriminator_params=discriminator_params,
          discriminator_state=discriminator_state,
          policy_params=None,
          key=key,
          steps=0,
      )
  
      expected_loss = [1.062, 1.057, 1.052]
  
      for i in range(3):
          state, loss = step(state, (one_transition, zero_transition))
          self.assertAlmostEqual(
              loss["total_loss"], expected_loss[i], places=3
          )
  
file_path: acme/acme/agents/jax/ail/learning_test.py
imports: import functools
  from acme import specs
  from acme import types
  from acme.agents.jax.ail import learning
  from acme.agents.jax.ail import losses
  from acme.agents.jax.ail import networks
  from acme.jax import networks
  from acme.jax import utils
  import haiku
  import jax
  import numpy
  import optax
  from absl.testing import absltest
label_desc: Use image node_id 1 for calling the AilLearnerTest obj's underlying member method code with example usage: obj.test_step() without return types