code: def training(
      self,
      *,
      action_noise_std: Optional[float] = NotProvided,
      noise_stdev: Optional[float] = NotProvided,
      num_rollouts: Optional[int] = NotProvided,
      rollouts_used: Optional[int] = NotProvided,
      sgd_stepsize: Optional[float] = NotProvided,
      noise_size: Optional[int] = NotProvided,
      eval_prob: Optional[float] = NotProvided,
      report_length: Optional[int] = NotProvided,
      offset: Optional[int] = NotProvided,
      tf_single_threaded: Optional[bool] = NotProvided,
      **kwargs,
  ) -> "ARSConfig":
      super().training(**kwargs)
  
      if action_noise_std is not NotProvided:
          self.action_noise_std = action_noise_std
      if noise_stdev is not NotProvided:
          self.noise_stdev = noise_stdev
      if num_rollouts is not NotProvided:
          self.num_rollouts = num_rollouts
      if rollouts_used is not NotProvided:
          self.rollouts_used = rollouts_used
      if sgd_stepsize is not NotProvided:
          self.sgd_stepsize = sgd_stepsize
      if noise_size is not NotProvided:
          self.noise_size = noise_size
      if eval_prob is not NotProvided:
          self.eval_prob = eval_prob
      if report_length is not NotProvided:
          self.report_length = report_length
      if offset is not NotProvided:
          self.offset = offset
      if tf_single_threaded is not NotProvided:
          self.tf_single_threaded = tf_single_threaded
  
      return self
  
file_path: ray/python/ray/rllib/algorithms/ars/ars.py
imports: from typing import Optional
  from ray.rllib.algorithms 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 ARSConfig obj's underlying member method code with example usage: obj.training() and returns: self