code: def run_worker(
      factory, to_worker, to_sampler, worker_number, agent, env
  ):
      """Run the streaming worker state machine.
  
      Starts in the "not streaming" state.
      Enters the "streaming" state when the "start" or "continue" message is
      received.
      While in the "streaming" state, it streams episodes back to the parent
      process.
      When it receives a "stop" message, or the queue back to the parent process
      is full, it enters the "not streaming" state.
      When it receives the "exit" message, it terminates.
  
      Critically, the worker never blocks on sending messages back to the
      sampler, to ensure it remains responsive to messages.
  
      Args:
          factory (WorkerFactory): Pickleable factory for creating workers.
              Should be transmitted to other processes / nodes where work needs
              to be done, then workers should be constructed there.
          to_worker (multiprocessing.Queue): Queue to send commands to the
              worker.
          to_sampler (multiprocessing.Queue): Queue to send episodes back to the
              sampler.
          worker_number (int): Number of this worker.
          agent (Policy): Agent to use to sample episodes.  If a list is passed
              in, it must have length exactly `worker_factory.n_workers`, and
              will be spread across the workers.
          env (Environment): Environment from which episodes are sampled. If a
              list is passed in, it must have length exactly
              `worker_factory.n_workers`, and will be spread across the workers.
  
      Raises:
          AssertionError: On internal errors.
  
      """
      # When a python process is closing, multiprocessing Queues attempt to flush
      # their contents to the underlying pipe.  If the pipe is full, they block
      # until someone reads from it.  In this case, the to_sampler pipe may be
      # full (or nearly full), and never read from, causing this process to hang
      # forever.  To avoid this, call cancel_join_thread, which will cause the
      # data to never be flushed to the pipe, allowing this process to end, and
      # the join on this process in the parent process to complete.
      # We call this immediately on process start in case this process crashes
      # (usually do to a bug or out-of-memory error in the underlying worker).
      to_sampler.cancel_join_thread()
      setproctitle.setproctitle("worker:" + setproctitle.getproctitle())
  
      inner_worker = factory(worker_number)
      inner_worker.update_agent(cloudpickle.loads(agent))
      inner_worker.update_env(cloudpickle.loads(env))
  
      version = 0
      streaming_samples = False
  
      while True:
          if streaming_samples:
              # We're streaming, so try to get a message without waiting. If we
              # can't, the message is "continue", without any contents.
              try:
                  tag, contents = to_worker.get_nowait()
              except queue.Empty:
                  tag = "continue"
                  contents = None
          else:
              # We're not streaming anymore, so wait for a message.
              tag, contents = to_worker.get()
  
          if tag == "start":
              # Update env and policy.
              agent_update, env_update, version = contents
              inner_worker.update_agent(cloudpickle.loads(agent_update))
              inner_worker.update_env(cloudpickle.loads(env_update))
              streaming_samples = True
          elif tag == "stop":
              streaming_samples = False
          elif tag == "continue":
              batch = inner_worker.rollout()
              try:
                  to_sampler.put_nowait(
                      ("episode", (batch, version, worker_number))
                  )
              except queue.Full:
                  # Either the sampler has fallen far behind the workers, or we
                  # missed a "stop" message. Either way, stop streaming.
                  # If the queue becomes empty again, the sampler will send a
                  # continue (or some other) message.
                  streaming_samples = False
          elif tag == "exit":
              to_worker.close()
              to_sampler.close()
              inner_worker.shutdown()
              return
          else:
              raise AssertionError(
                  "Unknown tag {} with contents {}".format(
                      tag, contents
                  )
              )
  
imports: from collections import defaultdict
  import itertools
  import multiprocessing
  import queue
  import click
  import cloudpickle
  import psutil
  import setproctitle
  from garage import EpisodeBatch
  from garage.experiment.deterministic import get_seed
  from garage.sampler.default_worker import DefaultWorker
  from garage.sampler.sampler import Sampler
  from garage.sampler.worker_factory import WorkerFactory
label_desc: Use image node_id 1 for calling a global function with example usage: run_worker(factory, to_worker, to_sampler, worker_number, agent, env) without return types