code: def batch_space(space: Space, n: int = 1) -> Space:
      """Create a (batched) space, containing multiple copies of a single space.
  
      Example::
  
          >>> from gym.spaces import Box, Dict
          >>> space = Dict({
          ...     'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
          ...     'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)
          ... })
          >>> batch_space(space, n=5)
          Dict(position:Box(5, 3), velocity:Box(5, 2))
  
      Args:
          space: Space (e.g. the observation space) for a single environment in the vectorized environment.
          n: Number of environments in the vectorized environment.
  
      Returns:
          Space (e.g. the observation space) for a batch of environments in the vectorized environment.
  
      Raises:
          ValueError: Cannot batch space that is not a valid :class:`gym.Space` instance
      """
      raise ValueError(
          f"Cannot batch space with type `{type(space)}`. The space must be a valid `gym.Space` instance."
      )
  
imports: from collections import OrderedDict
  from copy import deepcopy
  from functools import singledispatch
  from typing import Iterator
  import numpy
  from gym.error import CustomSpaceError
  from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Space, Tuple
label_desc: Use image node_id 1 for calling a global function with example usage: batch_space(space, n) without return types