class_docstr: An environment that supports batch evaluation using clones of sub-envs.
class_docstr_tok: ["An","environment","that","supports","batch","evaluation","using","clones","of","sub-envs","."]
docstr: Initializes a VectorEnv instance.
  
  Args:
      observation_space: The observation Space of a single
          sub-env.
      action_space: The action Space of a single sub-env.
      num_envs: The number of clones to make of the given sub-env.
code: def __init__(
      self,
      observation_space: gym.Space,
      action_space: gym.Space,
      num_envs: int,
  ):
      """Initializes a VectorEnv instance.
  
      Args:
          observation_space: The observation Space of a single
              sub-env.
          action_space: The action Space of a single sub-env.
          num_envs: The number of clones to make of the given sub-env.
      """
      self.observation_space = observation_space
      self.action_space = action_space
      self.num_envs = num_envs
  
imports: import logging
  import gymnasium
  import numpy
  from typing import Callable, List, Optional, Tuple, Union, Set
  from ray.rllib.env.base_env import BaseEnv, _DUMMY_AGENT_ID
  from ray.rllib.utils.annotations import Deprecated, override, PublicAPI
  from ray.rllib.utils.typing import EnvActionType, EnvID, EnvInfoDict, EnvObsType, EnvType, MultiEnvDict, AgentID
  from ray.util import log_once
label_desc: Use image node_id 1 to create a new VectorEnv object  with example: obj = VectorEnv(observation_space, action_space, num_envs)