code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          from rllib_crr.crr.crr_torch_policy import CRRTorchPolicy
  
          return CRRTorchPolicy
      else:
          raise ValueError(
              "Non-torch frameworks are not supported yet!"
          )
  
imports: import logging
  from typing import List, Optional, Type
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.execution import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.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_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, NUM_TARGET_UPDATES, SAMPLE_TIMER, TARGET_NET_UPDATE_TIMER
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 3 for calling the CRR obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: CRRTorchPolicy