code: def additional_update_for_module(
      self, *, module_id: ModuleID, config: ImpalaConfig, timestep: int
  ) -> Dict[str, Any]:
      results = super().additional_update_for_module(
          module_id=module_id, config=config, timestep=timestep
      )
  
      # 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/rllib/algorithms/impala/impala_learner.py
imports: from typing import Any, Dict
  from ray.rllib.algorithms.impala.impala import ImpalaConfig, LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY
  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 ImpalaLearner obj's underlying member method code with example usage: obj.additional_update_for_module() and returns: results