class_docstr: PyTorch policy class used with MAML.
code: def __init__(self, observation_space, action_space, config):
      config = dict(
          ray.rllib.algorithms.maml.maml.MAMLConfig(), **config
      )
      validate_config(config)
  
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
  
      KLCoeffMixin.__init__(self, config)
      ValueNetworkMixin.__init__(self, config)
  
      # TODO: Don't require users to call this manually.
      self._initialize_loss_from_dummy_batch()
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new MAMLTorchPolicy object from inherited base classes: ValueNetworkMixin, KLCoeffMixin, TorchPolicyV2 with example: obj = MAMLTorchPolicy(observation_space, action_space, config)