code: def training(
      self,
      *,
      twin_q: Optional[bool] = NotProvided,
      policy_delay: Optional[int] = NotProvided,
      smooth_target_policy: Optional[bool] = NotProvided,
      target_noise: Optional[bool] = NotProvided,
      target_noise_clip: Optional[float] = NotProvided,
      use_state_preprocessor: Optional[bool] = NotProvided,
      actor_hiddens: Optional[List[int]] = NotProvided,
      actor_hidden_activation: Optional[str] = NotProvided,
      critic_hiddens: Optional[List[int]] = NotProvided,
      critic_hidden_activation: Optional[str] = NotProvided,
      n_step: Optional[int] = NotProvided,
      critic_lr: Optional[float] = NotProvided,
      actor_lr: Optional[float] = NotProvided,
      tau: Optional[float] = NotProvided,
      use_huber: Optional[bool] = NotProvided,
      huber_threshold: Optional[float] = NotProvided,
      l2_reg: Optional[float] = NotProvided,
      training_intensity: Optional[float] = NotProvided,
      **kwargs,
  ) -> "DDPGConfig":
      super().training(**kwargs)
  
      if twin_q is not NotProvided:
          self.twin_q = twin_q
      if policy_delay is not NotProvided:
          self.policy_delay = policy_delay
      if smooth_target_policy is not NotProvided:
          self.smooth_target_policy = smooth_target_policy
      if target_noise is not NotProvided:
          self.target_noise = target_noise
      if target_noise_clip is not NotProvided:
          self.target_noise_clip = target_noise_clip
      if use_state_preprocessor is not NotProvided:
          self.use_state_preprocessor = use_state_preprocessor
      if actor_hiddens is not NotProvided:
          self.actor_hiddens = actor_hiddens
      if actor_hidden_activation is not NotProvided:
          self.actor_hidden_activation = actor_hidden_activation
      if critic_hiddens is not NotProvided:
          self.critic_hiddens = critic_hiddens
      if critic_hidden_activation is not NotProvided:
          self.critic_hidden_activation = critic_hidden_activation
      if n_step is not NotProvided:
          self.n_step = n_step
      if critic_lr is not NotProvided:
          self.critic_lr = critic_lr
      if actor_lr is not NotProvided:
          self.actor_lr = actor_lr
      if tau is not NotProvided:
          self.tau = tau
      if use_huber is not NotProvided:
          self.use_huber = use_huber
      if huber_threshold is not NotProvided:
          self.huber_threshold = huber_threshold
      if l2_reg is not NotProvided:
          self.l2_reg = l2_reg
      if training_intensity is not NotProvided:
          self.training_intensity = training_intensity
  
      return self
  
file_path: ray/python/ray/rllib/algorithms/ddpg/ddpg.py
imports: from typing import List, Optional
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.simple_q.simple_q import SimpleQ, SimpleQConfig
  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 DDPGConfig obj's underlying member method code with example usage: obj.training() and returns: self