code: def additional_update_for_module(
      self,
      *,
      module_id: ModuleID,
      config: "PPOConfig",
      timestep: int,
      sampled_kl_values: dict,
  ) -> Dict[str, Any]:
      results = super().additional_update_for_module(
          module_id=module_id,
          config=config,
          timestep=timestep,
          sampled_kl_values=sampled_kl_values,
      )
  
      # Update entropy coefficient via our Scheduler.
      new_entropy_coeff = self.entropy_coeff_schedulers_per_module[
          module_id
      ].update(timestep=timestep)
      results.update(
          {LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY: new_entropy_coeff}
      )
  
      return results
  
file_path: ray/python/ray/rllib/algorithms/ppo/ppo_learner.py
imports: from typing import Any, Dict
  from ray.rllib.algorithms.ppo.ppo import LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY, PPOConfig
  from ray.rllib.core.learner.learner import Learner
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.lambda_defaultdict import LambdaDefaultDict
  from ray.rllib.utils.schedules.scheduler import Scheduler
  from ray.rllib.utils.typing import ModuleID
label_desc: Use image node_id 3 for calling the PPOLearner obj's underlying member method code with example usage: obj.additional_update_for_module() and returns: results