code: def training(
      self,
      *,
      max_requests_in_flight_per_replay_worker: Optional[
          int
      ] = NotProvided,
      timeout_s_sampler_manager: Optional[float] = NotProvided,
      timeout_s_replay_manager: Optional[float] = NotProvided,
      **kwargs,
  ) -> "ApexDQNConfig":
      super().training(**kwargs)
  
      if max_requests_in_flight_per_replay_worker is not NotProvided:
          self.max_requests_in_flight_per_replay_worker = (
              max_requests_in_flight_per_replay_worker
          )
      if timeout_s_sampler_manager is not NotProvided:
          self.timeout_s_sampler_manager = timeout_s_sampler_manager
      if timeout_s_replay_manager is not NotProvided:
          self.timeout_s_replay_manager = timeout_s_replay_manager
  
      return self
  
file_path: ray/rllib/algorithms/apex_dqn/apex_dqn.py
imports: from typing import Optional
  from ray._private.dict import merge_dicts
  from ray.rllib.algorithms.algorithm_config import NotProvided
  from ray.rllib.algorithms.dqn.dqn import DQN, DQNConfig
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE, Deprecated, ALGO_DEPRECATION_WARNING
label_desc: Use image node_id 2 for calling the ApexDQNConfig obj's underlying member method code with example usage: obj.training() and returns: self