class_docstr: A worker class used in sampling for PEARL.
  
  It stores context and resample belief in the policy every step.
  
  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.
      worker_number (int): The number of the worker where this update is
          occurring. This argument is used to set a different seed for each
          worker.
      deterministic (bool): If True, use the mean action returned by the
          stochastic policy instead of sampling from the returned action
          distribution.
      accum_context (bool): If True, update context of the agent.
  
  Attributes:
      agent (Policy or None): The worker's agent.
      env (Environment or None): The worker's environment.
docstr: Begin a new episode.
code: def start_episode(self):
      """Begin a new episode."""
      self._eps_length = 0
      self._prev_obs, self._episode_info = self.env.reset()
  
imports: import copy
  import akro
  from dowel import logger
  import numpy
  import torch
  from garage import EnvSpec, InOutSpec, StepType, TimeStep
  from garage.experiment import MetaEvaluator
  from garage.np.algos import MetaRLAlgorithm
  from garage.replay_buffer import PathBuffer
  from garage.sampler import DefaultWorker
  from garage.torch import global_device
  from garage.torch._functions import np_to_torch, zero_optim_grads
  from garage.torch.embeddings import MLPEncoder
  from garage.torch.policies import ContextConditionedPolicy
label_desc: Use image node_id 2 for calling the PEARLWorker obj's underlying member method code with example usage: obj.start_episode() without return types