class_docstr: Common superclass for the structured vectorised env implementations in Maze.
  
  :param n_envs: The number of vectorised environments.
  :param action_spaces_dict: Action spaces dict (not vectorized, as it is the same for all environments)
  :param observation_spaces_dict: Observation spaces dict (not vectorized, as it is the same for all environments)
  :param logging_prefix: If set, will report epoch statistics under this logging prefix.
docstr: Return the observation space of one of the vectorised envs.
docstr_tok: ["Return","the","observation","space","of","one","of","the","vectorised","envs","."]
code: def observation_spaces_dict(
      self,
  ) -> Dict[Union[int, str], gym.spaces.Space]:
      """Return the observation space of one of the vectorised envs."""
      return self._observation_spaces_dict
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, Union, Optional, Callable
  import gym
  import numpy
  from maze.core.annotations import override
  from maze.core.env.structured_env import StructuredEnv, ActorID, StepKeyType
  from maze.core.env.structured_env_spaces_mixin import StructuredEnvSpacesMixin
  from maze.core.env.time_env_mixin import TimeEnvMixin
  from maze.core.log_stats.log_stats import LogStatsAggregator, LogStatsLevel, get_stats_logger, LogStatsValue
  from maze.core.log_stats.log_stats_env import LogStatsEnv
  from maze.train.parallelization.vector_env.vector_env import VectorEnv
label_desc: Use image node_id 8 for calling the StructuredVectorEnv obj's underlying member method code with example usage: obj.observation_spaces_dict() and returns: self