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: Returns a set of agent ids in the environment.
  
  Returns:
      Set of agent ids.
docstr_tok: ["Returns","a","set","of","agent","ids","in","the","environment",".","Returns",":","Set","of","agent","ids","."]
code: def get_agent_ids(self) -> Set[AgentID]:
      """Returns a set of agent ids in the environment.
  
      Returns:
          Set of agent ids.
      """
      if not isinstance(self._agent_ids, set):
          self._agent_ids = set(self._agent_ids)
      return self._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 8 for calling the MultiAgentEnv obj's underlying member method code with example usage: obj.get_agent_ids() and returns: self