class_docstr: Creates a simple vectorized wrapper for multiple environments, calling each environment in sequence on the current
  Python process. This is useful for computationally simple environment such as ``cartpole-v1``, as the overhead of
  multiprocess or multithread outweighs the environment computation time. This can also be used for RL methods that
  require a vectorized environment, but that you want a single environments to train with.
  
  :param env_fns: ([callable]) A list of functions that will create the environments
      (each callable returns a `Gym.Env` instance when called).
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docstr: Return attribute from vectorized environment (see base class).
docstr_tok: ["Return","attribute","from","vectorized","environment","(","see","base","class",")","."]
code: def get_attr(self, attr_name, indices=None):
      """Return attribute from vectorized environment (see base class)."""
      target_envs = self._get_target_envs(indices)
      return [getattr(env_i, attr_name) for env_i in target_envs]
  
imports: from collections import OrderedDict
  from typing import Sequence
  from copy import deepcopy
  import numpy
  from stable_baselines.common.vec_env.base_vec_env import VecEnv
  from stable_baselines.common.vec_env.util import copy_obs_dict, dict_to_obs, obs_space_info
label_desc: Use image node_id 11 for calling the DummyVecEnv obj's underlying member method code with example usage: obj.get_attr(attr_name, indices) and returns: unknown