code: def training(
      self,
      *,
      timeout_s_sampler_manager: Optional[float] = NotProvided,
      timeout_s_replay_manager: Optional[float] = NotProvided,
      **kwargs,
  ) -> "ApexDDPGConfig":
      super().training(**kwargs)
  
      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_ddpg/apex_ddpg.py
imports: from typing import Optional
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
  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 ApexDDPGConfig obj's underlying member method code with example usage: obj.training() and returns: self