class_docstr: An environment that hosts multiple independent agents.
  
  Agents are identified by (string) agent ids. Note that these "agents" here
  are not to be confused with RLlib Algorithms, which are also sometimes
  referred to as "agents" or "RL agents".
  
  The preferred format for action- and observation space is a mapping from agent
  ids to their individual spaces. If that is not provided, the respective methods'
  observation_space_contains(), action_space_contains(),
  action_space_sample() and observation_space_sample() have to be overwritten.
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docstr: Checks if obs space maps from agent ids to spaces of individual agents.
docstr_tok: ["Checks","if","obs","space","maps","from","agent","ids","to","spaces","of","individual","agents","."]
code: def _check_if_obs_space_maps_agent_id_to_sub_space(self) -> bool:
      """Checks if obs space maps from agent ids to spaces of individual agents."""
      return (
          hasattr(self, "observation_space")
          and isinstance(self.observation_space, gym.spaces.Dict)
          and set(self.observation_space.spaces.keys())
          == self.get_agent_ids()
      )
  
imports: import gymnasium
  import logging
  from typing import Callable, Dict, List, Tuple, Optional, Union, Set, Type
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.utils.annotations import ExperimentalAPI, override, PublicAPI, DeveloperAPI
  from ray.rllib.utils.typing import AgentID, EnvCreator, EnvID, EnvType, MultiAgentDict, MultiEnvDict
  from ray.util import log_once
label_desc: Use image node_id 12 for calling the MultiAgentEnv obj's underlying member method code with example usage: obj._check_if_obs_space_maps_agent_id_to_sub_space() and returns: unknown