code: def training(
      self,
      *,
      weight_type: Optional[str] = NotProvided,
      temperature: Optional[float] = NotProvided,
      max_weight: Optional[float] = NotProvided,
      advantage_type: Optional[str] = NotProvided,
      n_action_sample: Optional[int] = NotProvided,
      twin_q: Optional[bool] = NotProvided,
      target_network_update_freq: Optional[int] = 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,
      tau: Optional[float] = NotProvided,
      td_error_loss_fn: Optional[str] = NotProvided,
      categorical_distribution_temperature: Optional[
          float
      ] = NotProvided,
      **kwargs,
  ) -> "CRRConfig":
      super().training(**kwargs)
  
      if weight_type is not NotProvided:
          self.weight_type = weight_type
      if temperature is not NotProvided:
          self.temperature = temperature
      if max_weight is not NotProvided:
          self.max_weight = max_weight
      if advantage_type is not NotProvided:
          self.advantage_type = advantage_type
      if n_action_sample is not NotProvided:
          self.n_action_sample = n_action_sample
      if twin_q is not NotProvided:
          self.twin_q = twin_q
      if target_network_update_freq is not NotProvided:
          self.target_network_update_freq = target_network_update_freq
      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 tau is not NotProvided:
          self.tau = tau
      if td_error_loss_fn is not NotProvided:
          self.td_error_loss_fn = td_error_loss_fn
      if categorical_distribution_temperature is not NotProvided:
          self.categorical_distribution_temperature = (
              categorical_distribution_temperature
          )
  
      return self
  
file_path: ray/rllib/algorithms/crr/crr.py
imports: from typing import List, Optional
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  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 CRRConfig obj's underlying member method code with example usage: obj.training() and returns: self