code: def training_step(self) -> ResultDict:
      train_results = super().training_step()
  
      # Update KL, target network periodically.
      self.after_train_step(train_results)
  
      return train_results
  
imports: from typing import Optional, Type
  import logging
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.impala.impala import Impala, ImpalaConfig
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES
  from ray.rllib.utils.metrics import ALL_MODULES, LEARNER_STATS_KEY
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 4 for calling the APPO obj's underlying member method code with example usage: obj.training_step() and returns: train_results