code: def test_train(self, policy_loss_coeff_fn):
      seed = 0
      num_iterations = 5
      batch_size = 64
      grad_updates_per_batch = 1
  
      # Create a fake environment to test with.
      environment = fakes.ContinuousEnvironment(
          episode_length=10, bounded=True, action_dim=6
      )
      spec = specs.make_environment_spec(environment)
  
      # Construct the learner.
      networks = crr.make_networks(
          spec, policy_layer_sizes=(8, 8), critic_layer_sizes=(8, 8)
      )
      key = jax.random.PRNGKey(seed)
      dataset = fakes.transition_iterator(environment)
      learner = crr.CRRLearner(
          networks,
          key,
          discount=0.95,
          target_update_period=2,
          policy_loss_coeff_fn=policy_loss_coeff_fn,
          iterator=dataset(batch_size * grad_updates_per_batch),
          policy_optimizer=optax.adam(1e-4),
          critic_optimizer=optax.adam(1e-4),
          grad_updates_per_batch=grad_updates_per_batch,
      )
  
      # Train the learner.
      for _ in range(num_iterations):
          learner.step()
  
file_path: acme/acme/agents/jax/crr/agent_test.py
imports: from acme import specs
  from acme.agents.jax import crr
  from acme.testing import fakes
  import jax
  import optax
  from absl.testing import absltest
  from absl.testing import parameterized
label_desc: Use image node_id 1 for calling the CRRTest obj's underlying member method code with example usage: obj.test_train(policy_loss_coeff_fn) without return types