code: def training(
      self,
      *,
      replay_buffer_config: Optional[Dict[str, Any]] = NotProvided,
      embed_dim: Optional[int] = NotProvided,
      num_layers: Optional[int] = NotProvided,
      num_heads: Optional[int] = NotProvided,
      embed_pdrop: Optional[float] = NotProvided,
      resid_pdrop: Optional[float] = NotProvided,
      attn_pdrop: Optional[float] = NotProvided,
      grad_clip: Optional[float] = NotProvided,
      loss_coef_actions: Optional[float] = NotProvided,
      loss_coef_obs: Optional[float] = NotProvided,
      loss_coef_returns_to_go: Optional[float] = NotProvided,
      lr_schedule: Optional[
          List[List[Union[int, float]]]
      ] = NotProvided,
      horizon: Optional[int] = NotProvided,
      **kwargs,
  ) -> "DTConfig":
      super().training(**kwargs)
      if replay_buffer_config is not NotProvided:
          new_replay_buffer_config = deep_update(
              {"replay_buffer_config": self.replay_buffer_config},
              {"replay_buffer_config": replay_buffer_config},
              False,
              ["replay_buffer_config"],
              ["replay_buffer_config"],
          )
          self.replay_buffer_config = new_replay_buffer_config[
              "replay_buffer_config"
          ]
      if embed_dim is not NotProvided:
          self.embed_dim = embed_dim
      if num_layers is not NotProvided:
          self.num_layers = num_layers
      if num_heads is not NotProvided:
          self.num_heads = num_heads
      if embed_pdrop is not NotProvided:
          self.embed_pdrop = embed_pdrop
      if resid_pdrop is not NotProvided:
          self.resid_pdrop = resid_pdrop
      if attn_pdrop is not NotProvided:
          self.attn_pdrop = attn_pdrop
      if grad_clip is not NotProvided:
          self.grad_clip = grad_clip
      if lr_schedule is not NotProvided:
          self.lr_schedule = lr_schedule
      if loss_coef_actions is not NotProvided:
          self.loss_coef_actions = loss_coef_actions
      if loss_coef_obs is not NotProvided:
          self.loss_coef_obs = loss_coef_obs
      if loss_coef_returns_to_go is not NotProvided:
          self.loss_coef_returns_to_go = loss_coef_returns_to_go
      if horizon is not NotProvided:
          self.horizon = horizon
  
      return self
  
file_path: ray/rllib/algorithms/dt/dt.py
imports: from typing import List, Optional, Dict, Any, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated, ALGO_DEPRECATION_WARNING
label_desc: Use image node_id 2 for calling the DTConfig obj's underlying member method code with example usage: obj.training() and returns: self