code: def poll(
      self,
  ) -> Tuple[
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
  ]:
      if not self.initialized:
          # TODO(sven): Should we make it possible to pass in a seed here?
          self.reset()
          self.initialized = True
  
      observations = self.last_obs
      rewards = {}
      terminateds = {"__all__": self.last_terminateds["__all__"]}
      truncateds = {"__all__": self.last_truncateds["__all__"]}
      infos = self.last_infos
  
      # If episode is done or we have an error, release everything we have.
      if (
          terminateds["__all__"]
          or truncateds["__all__"]
          or isinstance(observations, Exception)
      ):
          rewards = self.last_rewards
          self.last_rewards = {}
          terminateds = self.last_terminateds
          if isinstance(observations, Exception):
              terminateds["__all__"] = True
              truncateds["__all__"] = False
          self.last_terminateds = {}
          truncateds = self.last_truncateds
          self.last_truncateds = {}
          self.last_obs = {}
          infos = self.last_infos
          self.last_infos = {}
      # Only release those agents' rewards/terminateds/truncateds/infos, whose
      # observations we have.
      else:
          for ag in observations.keys():
              if ag in self.last_rewards:
                  rewards[ag] = self.last_rewards[ag]
                  del self.last_rewards[ag]
              if ag in self.last_terminateds:
                  terminateds[ag] = self.last_terminateds[ag]
                  del self.last_terminateds[ag]
              if ag in self.last_truncateds:
                  truncateds[ag] = self.last_truncateds[ag]
                  del self.last_truncateds[ag]
  
      self.last_terminateds["__all__"] = False
      self.last_truncateds["__all__"] = False
      return observations, rewards, terminateds, truncateds, infos
  
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 2 for calling the _MultiAgentEnvState obj's underlying member method code with example usage: obj.poll() and returns: observations,  rewards,  terminateds,  truncateds,  infos