class_docstr: Set of EnvRunners with n @ray.remote workers and zero or one local worker.
  
  Where: n >= 0.
class_docstr_tok: ["Set","of","EnvRunners","with","n","@","ray.remote","workers","and","zero","or","one","local","worker",".","Where",":","n",">","=","0","."]
docstr: Get esults from outstanding asynchronous requests that are ready.
  
  Args:
      timeout_seconds: Time to wait for results. Default is 0, meaning
          those requests that are already ready.
      return_obj_refs: Whether to return ObjectRef instead of actual results.
      mark_healthy: Whether to mark the worker as healthy based on call results.
  
  Returns:
      A list of results successfully returned from outstanding remote calls,
      paired with the indices of the callee workers.
docstr_tok: ["Get","esults","from","outstanding","asynchronous","requests","that","are","ready",".","Args",":","timeout_seconds",":","Time","to","wait","for","results",".","Default","is","0",",","meaning","those","requests","that","are","already","ready",".","return_obj_refs",":","Whether","to","return","ObjectRef","instead","of","actual","results",".","mark_healthy",":","Whether","to","mark","the","worker","as","healthy","based","on","call","results",".","Returns",":","A","list","of","results","successfully","returned","from","outstanding","remote","calls",",","paired","with","the","indices","of","the","callee","workers","."]
code: def fetch_ready_async_reqs(
      self,
      *,
      timeout_seconds: Optional[int] = 0,
      return_obj_refs: bool = False,
      mark_healthy: bool = False,
  ) -> List[Tuple[int, T]]:
      """Get esults from outstanding asynchronous requests that are ready.
  
      Args:
          timeout_seconds: Time to wait for results. Default is 0, meaning
              those requests that are already ready.
          return_obj_refs: Whether to return ObjectRef instead of actual results.
          mark_healthy: Whether to mark the worker as healthy based on call results.
  
      Returns:
          A list of results successfully returned from outstanding remote calls,
          paired with the indices of the callee workers.
      """
      remote_results = self.__worker_manager.fetch_ready_async_reqs(
          timeout_seconds=timeout_seconds,
          return_obj_refs=return_obj_refs,
          mark_healthy=mark_healthy,
      )
  
      handle_remote_call_result_errors(
          remote_results, self._ignore_worker_failures
      )
  
      return [
          (r.actor_id, r.get()) for r in remote_results.ignore_errors()
      ]
  
imports: import functools
  import gymnasium
  import logging
  import importlib.util
  import os
  from typing import Callable, Container, Dict, List, Optional, Tuple, Type, TYPE_CHECKING, TypeVar, Union
  import ray
  from ray.actor import ActorHandle
  from ray.exceptions import RayActorError
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.utils.actor_manager import RemoteCallResults
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.env.env_runner import EnvRunner
  from ray.rllib.offline import get_dataset_and_shards
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils.actor_manager import FaultTolerantActorManager
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.deprecation import Deprecated, deprecation_warning, DEPRECATED_VALUE
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.policy import validate_policy_id
  from ray.rllib.utils.typing import AgentID, EnvCreator, EnvType, EpisodeID, PartialAlgorithmConfigDict, PolicyID, SampleBatchType, TensorType
label_desc: Use image node_id 20 for calling the WorkerSet obj's underlying member method code with example usage: obj.fetch_ready_async_reqs() and returns: unknown