class_docstr: Sampler that uses multiprocessing to distribute workers.
  
  The sampler need to be created either from a worker factory or from
  parameters which can construct a worker factory. See the __init__ method
  of WorkerFactory for the detail of these parameters.
  
  Args:
      agents (Policy or List[Policy]): Agent(s) 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.
      envs (Environment or List[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.
      worker_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. Either this param or params after this are required to
          construct a sampler.
      max_episode_length(int): Params used to construct a worker factory.
          The maximum length episodes which will be sampled.
      is_tf_worker (bool): Whether it is workers for TFTrainer.
      seed(int): The seed to use to initialize random number generators.
      n_workers(int): The number of workers to use.
      worker_class(type): Class of the workers. Instances should implement
          the Worker interface.
      worker_args (dict or None): Additional arguments that should be passed
          to the worker.
docstr: Get the pickle state.
  
  Returns:
      dict: The pickled state.
docstr_tok: ["Get","the","pickle","state",".","Returns",":","dict",":","The","pickled","state","."]
code: def __getstate__(self):
      """Get the pickle state.
  
      Returns:
          dict: The pickled state.
  
      """
      return dict(
          factory=self._factory,
          agents=[cloudpickle.loads(agent) for agent in self._agents],
          envs=[cloudpickle.loads(env) for env in self._envs],
      )
  
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 7 for calling the MultiprocessingSampler obj's underlying member method code with example usage: obj.__getstate__() and returns: dict