code: def test_recurrent(self):
      environment = _make_fake_env()
      env_spec = specs.make_environment_spec(environment)
      output_size = env_spec.actions.num_values
      obs = utils.add_batch_dim(utils.zeros_like(env_spec.observations))
      rng = hk.PRNGSequence(1)
  
      @_transform_without_rng
      def network(inputs: jnp.ndarray, state: hk.LSTMState):
          return hk.DeepRNN(
              [hk.Reshape([-1], preserve_dims=1), hk.LSTM(output_size)]
          )(inputs, state)
  
      @_transform_without_rng
      def initial_state(batch_size: Optional[int] = None):
          network = hk.DeepRNN(
              [hk.Reshape([-1], preserve_dims=1), hk.LSTM(output_size)]
          )
          return network.initial_state(batch_size)
  
      initial_state = initial_state.apply(
          initial_state.init(next(rng)), 1
      )
      params = network.init(next(rng), obs, initial_state)
  
      def policy(
          params: jnp.ndarray,
          key: jnp.ndarray,
          observation: jnp.ndarray,
          core_state: hk.LSTMState,
      ) -> Tuple[jnp.ndarray, hk.LSTMState]:
          del key  # Unused for test-case deterministic policy.
          action_values, core_state = network.apply(
              params, observation, core_state
          )
          actions = jnp.argmax(action_values, axis=-1)
          return actions, core_state
  
      variable_source = fakes.VariableSource(params)
      variable_client = variable_utils.VariableClient(
          variable_source, "policy"
      )
  
      actor_core = actor_core_lib.batched_recurrent_to_actor_core(
          policy, initial_state
      )
      actor = actors.GenericActor(
          actor_core, jax.random.PRNGKey(1), 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 RecurrentActorTest obj's underlying member method code with example usage: obj.test_recurrent() and returns: hk, network, actions,  core_state