code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          return SimpleQTorchPolicy
      elif config["framework"] == "tf":
          return SimpleQTF1Policy
      else:
          return SimpleQTF2Policy
  
imports: import logging
  from typing import List, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.simple_q.simple_q_tf_policy import SimpleQTF1Policy, SimpleQTF2Policy
  from ray.rllib.algorithms.simple_q.simple_q_torch_policy import SimpleQTorchPolicy
  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 import deep_update
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE, Deprecated, ALGO_DEPRECATION_WARNING
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, NUM_TARGET_UPDATES, SYNCH_WORKER_WEIGHTS_TIMER, TARGET_NET_UPDATE_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.replay_buffers.utils import update_priorities_in_replay_buffer, validate_buffer_config
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 2 for calling the SimpleQ obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: SimpleQTorchPolicy, SimpleQTF1Policy, SimpleQTF2Policy