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: Performs a vectorized step on all sub environments using `actions`.
  
  Args:
      actions: List of actions (one for each sub-env).
  
  Returns:
      A tuple consisting of
      1) New observations for each sub-env.
      2) Reward values for each sub-env.
      3) Terminated values for each sub-env.
      4) Truncated values for each sub-env.
      5) Info values for each sub-env.
code: def vector_step(
      self, actions: List[EnvActionType]
  ) -> Tuple[
      List[EnvObsType],
      List[float],
      List[bool],
      List[bool],
      List[EnvInfoDict],
  ]:
      """Performs a vectorized step on all sub environments using `actions`.
  
      Args:
          actions: List of actions (one for each sub-env).
  
      Returns:
          A tuple consisting of
          1) New observations for each sub-env.
          2) Reward values for each sub-env.
          3) Terminated values for each sub-env.
          4) Truncated values for each sub-env.
          5) Info values for each sub-env.
      """
      raise NotImplementedError
  
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 6 for calling the VectorEnv obj's underlying member method code with example usage: obj.vector_step(actions) without return types