code: def run_actor(
      agent: Agent,
      rng_key: jax.Array,
      get_params: Callable[[], hk.Params],
      enqueue_traj: Callable[[Transition], None],
      unroll_len: int,
      num_trajectories: int,
  ):
      """Runs an actor to produce num_trajectories trajectories."""
      env = catch.Catch()
      state = env.reset()
      traj = []
  
      for i in range(num_trajectories):
          params = get_params()
          # The first rollout is one step longer.
          for _ in range(unroll_len + int(i == 0)):
              rng_key, step_key = jax.random.split(rng_key)
              state = preprocess_step(state)
              action, logits = agent.step(params, step_key, state)
              transition = Transition(state, action, logits)
              traj.append(transition)
              state = env.step(action)
              if state.step_type == dm_env.StepType.LAST:
                  logging.log_every_n(
                      logging.INFO,
                      "Episode ended with reward: %s",
                      5,
                      state.reward,
                  )
  
          # Stack and send the trajectory.
          stacked_traj = jax.tree_util.tree_map(
              lambda *ts: np.stack(ts), *traj
          )
          enqueue_traj(stacked_traj)
          # Reset the trajectory, keeping the last timestep.
          traj = traj[-1:]
  
imports: import functools
  import queue
  import threading
  from typing import Any, Callable, NamedTuple
  from absl import app
  from absl import logging
  from bsuite.environments import catch
  import dm_env
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  import rlax
label_desc: Use image node_id 2 for calling a global function with example usage: run_actor(agent, rng_key, get_params, enqueue_traj, unroll_len, num_trajectories) without return types