code: def test_feedforward(self, has_extras):
      environment = _make_fake_env()
      env_spec = specs.make_environment_spec(environment)
  
      def policy(inputs: jnp.ndarray):
          action_values = hk.Sequential(
              [
                  hk.Flatten(),
                  hk.Linear(env_spec.actions.num_values),
              ]
          )(inputs)
          action = jnp.argmax(action_values, axis=-1)
          if has_extras:
              return action, (action_values,)
          else:
              return action
  
      policy = hk.transform(policy)
  
      rng = hk.PRNGSequence(1)
      dummy_obs = utils.add_batch_dim(
          utils.zeros_like(env_spec.observations)
      )
      params = policy.init(next(rng), dummy_obs)
  
      variable_source = fakes.VariableSource(params)
      variable_client = variable_utils.VariableClient(
          variable_source, "policy"
      )
  
      if has_extras:
          actor_core = actor_core_lib.batched_feed_forward_with_extras_to_actor_core(
              policy.apply
          )
      else:
          actor_core = (
              actor_core_lib.batched_feed_forward_to_actor_core(
                  policy.apply
              )
          )
      actor = actors.GenericActor(
          actor_core,
          random_key=jax.random.PRNGKey(1),
          variable_client=variable_client,
      )
  
      loop = environment_loop.EnvironmentLoop(environment, actor)
      loop.run(20)
  
imports: from typing import Optional, Tuple
  from acme import environment_loop
  from acme import specs
  from acme.agents.jax import actor_core
  from acme.agents.jax import actors
  from acme.jax import utils
  from acme.jax import variable_utils
  from acme.testing import fakes
  import dm_env
  import haiku
  import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest
  from absl.testing import parameterized
label_desc: Use image node_id 1 for calling the ActorTest obj's underlying member method code with example usage: obj.test_feedforward(has_extras) and returns: action,  action_values, action