code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          from ray.rllib.algorithms.maml.maml_torch_policy import (
              MAMLTorchPolicy,
          )
  
          return MAMLTorchPolicy
      elif config["framework"] == "tf":
          from ray.rllib.algorithms.maml.maml_tf_policy import (
              MAMLTF1Policy,
          )
  
          return MAMLTF1Policy
      else:
          from ray.rllib.algorithms.maml.maml_tf_policy import (
              MAMLTF2Policy,
          )
  
          return MAMLTF2Policy
  
imports: import logging
  from typing import Optional, Type
  import numpy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.metrics import collect_metrics, get_learner_stats
  from ray.rllib.evaluation.worker_set import WorkerSet
  from ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, STEPS_TRAINED_COUNTER, STEPS_TRAINED_THIS_ITER_COUNTER, _get_shared_metrics
  from ray.rllib.execution.metric_ops import CollectMetrics
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import concat_samples, convert_ma_batch_to_sample_batch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
  from ray.rllib.utils.sgd import standardized
  from ray.util.iter import LocalIterator, from_actors
label_desc: Use image node_id 2 for calling the MAML obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: MAMLTorchPolicy, MAMLTF1Policy, MAMLTF2Policy