class_docstr: Abstract base class for vectorised environments.
  
  An instance of this class encapsulates multiple environments under the hood and steps them synchronously.
  
  Note that actions and observations are handled and returned in a stacked form, i.e. not as a list,
  but as a single action/observation dict where the items have an additional dimension corresponding
  to the number of encapsulated environments (as such setting is more convenient when working with
  Torch policies). To convert these to/from a list, use the training helpers such as
  :func:`maze.train.utils.train_utils.stack_numpy_dict_list` and
  :func:`maze.train.utils.train_utils.unstack_numpy_list_dict`.
  
  Also note that in structured scenarios, only synchronous environments are supported -- i.e., in each
  sub-step, the actor ID must be the same for all environments.
  
  :param n_envs: The number of encapsulated environments.
docstr: Reset all the environments and return respective observations in env-aggregated form.
  
  :return: observations in env-aggregated form.
docstr_tok: ["Reset","all","the","environments","and","return","respective","observations","in","env-aggregated","form",".",":","return",":","observations","in","env-aggregated","form","."]
code: def reset(self):
      """Reset all the environments and return respective observations in env-aggregated form.
  
      :return: observations in env-aggregated form.
      """
  
file_path: maze/maze/train/parallelization/vector_env/vector_env.py
imports: from abc import ABC, abstractmethod
  from typing import Iterable, Any, Tuple, Dict, List
  import numpy
  from maze.core.env.action_conversion import ActionType
  from maze.core.env.base_env import BaseEnv
  from maze.core.env.observation_conversion import ObservationType
label_desc: Use image node_id 3 for calling the VectorEnv obj's underlying member method code with example usage: obj.reset() without return types