code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          from ray.rllib.algorithms.marwil.marwil_torch_policy import (
              MARWILTorchPolicy,
          )
  
          return MARWILTorchPolicy
      elif config["framework"] == "tf":
          from ray.rllib.algorithms.marwil.marwil_tf_policy import (
              MARWILTF1Policy,
          )
  
          return MARWILTF1Policy
      else:
          from ray.rllib.algorithms.marwil.marwil_tf_policy import (
              MARWILTF2Policy,
          )
  
          return MARWILTF2Policy
  
imports: from typing import Callable, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, SYNCH_WORKER_WEIGHTS_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.typing import EnvType, ResultDict
  from ray.tune.logger import Logger
label_desc: Use image node_id 2 for calling the MARWIL obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: MARWILTorchPolicy, MARWILTF1Policy, MARWILTF2Policy