class_docstr: Base class for vectorized environments. Runs multiple independent copies of the same environment in parallel.
  
  This is not the same as 1 environment that has multiple subcomponents, but it is many copies of the same base env.
  
  Each observation returned from vectorized environment is a batch of observations for each parallel environment.
  And :meth:`step` is also expected to receive a batch of actions for each parallel environment.
  
  Notes:
      All parallel environments should share the identical observation and action spaces.
      In other words, a vector of multiple different environments is not supported.
docstr: Reset all parallel environments and return a batch of initial observations.
  
  Args:
      seed: The environment reset seeds
      options: If to return the options
  
  Returns:
      A batch of observations from the vectorized environment.
code: def reset(
      self,
      *,
      seed: Optional[Union[int, List[int]]] = None,
      options: Optional[dict] = None,
  ):
      """Reset all parallel environments and return a batch of initial observations.
  
      Args:
          seed: The environment reset seeds
          options: If to return the options
  
      Returns:
          A batch of observations from the vectorized environment.
      """
      self.reset_async(seed=seed, options=options)
      return self.reset_wait(seed=seed, options=options)
  
imports: from typing import Any, List, Optional, Tuple, Union
  import numpy
  import gym
  from gym.vector.utils.spaces import batch_space
label_desc: Use image node_id 4 for calling the VectorEnv obj's underlying member method code with example usage: obj.reset() and returns: self