code: def test_learner(self):
      with chex.fake_pmap_and_jit():
          num_sgd_steps_per_step = 1
          num_steps = 5
          num_networks = 7
  
          # Create a fake environment to test with.
          environment = fakes.ContinuousEnvironment(
              episode_length=10,
              bounded=True,
              observation_dim=3,
              action_dim=2,
          )
  
          spec = specs.make_environment_spec(environment)
          dataset = fakes.transition_dataset(environment)
  
          # Add dummy n-step return to the transitions.
          def _add_dummy_n_step_return(sample):
              return types.Transition(*sample.data)._replace(
                  extras={"n_step_return": 1.0}
              )
  
          dataset = dataset.map(_add_dummy_n_step_return)
          # Convert into time-batched format with previous, current and next
          # transitions.
          dataset = rlds.transformations.batch(dataset, 3)
          dataset = dataset.batch(8).as_numpy_iterator()
  
          # Use the default networks and losses.
          networks = mbop_networks.make_networks(spec)
          losses = mbop_losses.MBOPLosses()
  
          def logger_fn(label: str, steps_key: str):
              return loggers.make_default_logger(
                  label, steps_key=steps_key
              )
  
          def make_learner_fn(
              name, logger_fn, counter, rng_key, dataset, network, loss
          ):
              return learning.make_ensemble_regressor_learner(
                  name,
                  num_networks,
                  logger_fn,
                  counter,
                  rng_key,
                  dataset,
                  network,
                  loss,
                  optax.adam(0.01),
                  num_sgd_steps_per_step,
              )
  
          learner = learning.MBOPLearner(
              networks,
              losses,
              dataset,
              jax.random.PRNGKey(0),
              logger_fn,
              functools.partial(make_learner_fn, "world_model"),
              functools.partial(make_learner_fn, "policy_prior"),
              functools.partial(make_learner_fn, "n_step_return"),
          )
  
          # Train the agent
          for _ in range(num_steps):
              learner.step()
  
          # Save and restore.
          learner_state = learner.save()
          learner.restore(learner_state)
  
file_path: acme/acme/agents/jax/mbop/agent_test.py
imports: import functools
  from acme import specs
  from acme import types
  from acme.agents.jax.mbop import learning
  from acme.agents.jax.mbop import losses
  from acme.agents.jax.mbop import networks
  from acme.testing import fakes
  from acme.utils import loggers
  import chex
  import jax
  import optax
  import rlds
  from absl.testing import absltest
label_desc: Use image node_id 1 for calling the MBOPTest obj's underlying member method code with example usage: obj.test_learner() and returns: types, loggers, learning