code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          from ray.rllib.algorithms.a3c.a3c_torch_policy import (
              A3CTorchPolicy,
          )
  
          return A3CTorchPolicy
      elif config["framework"] == "tf":
          from ray.rllib.algorithms.a3c.a3c_tf_policy import (
              A3CTF1Policy,
          )
  
          return A3CTF1Policy
      else:
          from ray.rllib.algorithms.a3c.a3c_tf_policy import (
              A3CTF2Policy,
          )
  
          return A3CTF2Policy
  
imports: import logging
  from typing import Any, Dict, List, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import APPLY_GRADS_TIMER, GRAD_WAIT_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 2 for calling the A3C obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: A3CTorchPolicy, A3CTF1Policy, A3CTF2Policy