code: def training(
      self,
      *,
      replay_buffer_config: Optional[Dict[str, Any]] = NotProvided,
      fcnet_hiddens_per_candidate: Optional[List[int]] = NotProvided,
      target_network_update_freq: Optional[int] = NotProvided,
      tau: Optional[float] = NotProvided,
      use_huber: Optional[bool] = NotProvided,
      huber_threshold: Optional[float] = NotProvided,
      training_intensity: Optional[float] = NotProvided,
      lr_schedule: Optional[
          List[List[Union[int, float]]]
      ] = NotProvided,
      lr_choice_model: Optional[bool] = NotProvided,
      rmsprop_epsilon: Optional[float] = NotProvided,
      grad_clip: Optional[float] = NotProvided,
      n_step: Optional[int] = NotProvided,
      num_steps_sampled_before_learning_starts: Optional[
          int
      ] = NotProvided,
      **kwargs,
  ) -> "SlateQConfig":
      super().training(**kwargs)
  
      if replay_buffer_config is not NotProvided:
          self.replay_buffer_config.update(replay_buffer_config)
      if fcnet_hiddens_per_candidate is not NotProvided:
          self.fcnet_hiddens_per_candidate = fcnet_hiddens_per_candidate
      if target_network_update_freq is not NotProvided:
          self.target_network_update_freq = target_network_update_freq
      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 training_intensity is not NotProvided:
          self.training_intensity = training_intensity
      if lr_schedule is not NotProvided:
          self.lr_schedule = lr_schedule
      if lr_choice_model is not NotProvided:
          self.lr_choice_model = lr_choice_model
      if rmsprop_epsilon is not NotProvided:
          self.rmsprop_epsilon = rmsprop_epsilon
      if grad_clip is not NotProvided:
          self.grad_clip = grad_clip
      if n_step is not NotProvided:
          self.n_step = n_step
      if num_steps_sampled_before_learning_starts is not NotProvided:
          self.num_steps_sampled_before_learning_starts = (
              num_steps_sampled_before_learning_starts
          )
  
      return self
  
file_path: ray/rllib/algorithms/slateq/slateq.py
imports: from typing import Any, Dict, List, Optional, Union
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dqn.dqn import DQN
  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 SlateQConfig obj's underlying member method code with example usage: obj.training() and returns: self