class_docstr: Vectorized Worker that collects partial episodes.
  
  Useful for off-policy RL.
  
  Args:
      seed (int): The seed to use to intialize random number generators.
      max_episode_length (int or float): The maximum length of episodes which
          will be sampled. Can be (floating point) infinity.
      length of fragments before they're transmitted out of
      worker_number (int): The number of the worker this update is
          occurring in. This argument is used to set a different seed for
          each worker.
      n_envs (int): Number of environment copies to use.
      timesteps_per_call (int): Maximum number of timesteps to gather per env
          per call to the worker. Defaults to 1 (i.e. gather 1 timestep per
          env each call, or n_envs timesteps in total each call).
docstr: Sample a single episode of the agent in the environment.
  
  Returns:
      EpisodeBatch: The collected episode.
docstr_tok: ["Sample","a","single","episode","of","the","agent","in","the","environment",".","Returns",":","EpisodeBatch",":","The","collected","episode","."]
code: def rollout(self):
      """Sample a single episode of the agent in the environment.
  
      Returns:
          EpisodeBatch: The collected episode.
  
      """
      self.start_episode()
      for _ in range(self._timesteps_per_call):
          self.step_episode()
      complete_frag = self.collect_episode()
      return complete_frag
  
file_path: garage/src/garage/sampler/fragment_worker.py
imports: import copy
  import numpy
  from garage import EpisodeBatch, StepType
  from garage.sampler import _apply_env_update, InProgressEpisode
  from garage.sampler.default_worker import DefaultWorker
label_desc: Use image node_id 6 for calling the FragmentWorker obj's underlying member method code with example usage: obj.rollout() and returns: complete_frag