code: def get_marwil_tf_policy(name: str, base: type) -> type:
      """Construct a MARWILTFPolicy inheriting either dynamic or eager base policies.
  
      Args:
          base: Base class for this policy. DynamicTFPolicyV2 or EagerTFPolicyV2.
  
      Returns:
          A TF Policy to be used with MAML.
      """
  
      class MARWILTFPolicy(
          ValueNetworkMixin, PostprocessAdvantages, base
      ):
          def __init__(
              self,
              observation_space,
              action_space,
              config,
              existing_model=None,
              existing_inputs=None,
          ):
              # First thing first, enable eager execution if necessary.
              base.enable_eager_execution_if_necessary()
  
              # Initialize base class.
              base.__init__(
                  self,
                  observation_space,
                  action_space,
                  config,
                  existing_inputs=existing_inputs,
                  existing_model=existing_model,
              )
  
              ValueNetworkMixin.__init__(self, config)
              PostprocessAdvantages.__init__(self)
  
              # Not needed for pure BC.
              if config["beta"] != 0.0:
                  # Set up a tf-var for the moving avg (do this here to make it work
                  # with eager mode); "c^2" in the paper.
                  self._moving_average_sqd_adv_norm = get_variable(
                      config["moving_average_sqd_adv_norm_start"],
                      framework="tf",
                      tf_name="moving_average_of_advantage_norm",
                      trainable=False,
                  )
  
              # Note: this is a bit ugly, but loss and optimizer initialization must
              # happen after all the MixIns are initialized.
              self.maybe_initialize_optimizer_and_loss()
  
          @override(base)
          def loss(
              self,
              model: Union[ModelV2, "tf.keras.Model"],
              dist_class: Type[TFActionDistribution],
              train_batch: SampleBatch,
          ) -> Union[TensorType, List[TensorType]]:
              model_out, _ = model(train_batch)
              action_dist = dist_class(model_out, model)
              value_estimates = model.value_function()
  
              self._marwil_loss = MARWILLoss(
                  self,
                  value_estimates,
                  action_dist,
                  train_batch,
                  self.config["vf_coeff"],
                  self.config["beta"],
              )
  
              return self._marwil_loss.total_loss
  
          @override(base)
          def stats_fn(
              self, train_batch: SampleBatch
          ) -> Dict[str, TensorType]:
              stats = {
                  "policy_loss": self._marwil_loss.p_loss,
                  "total_loss": self._marwil_loss.total_loss,
              }
              if self.config["beta"] != 0.0:
                  stats[
                      "moving_average_sqd_adv_norm"
                  ] = self._moving_average_sqd_adv_norm
                  stats[
                      "vf_explained_var"
                  ] = self._marwil_loss.explained_variance
                  stats["vf_loss"] = self._marwil_loss.v_loss
  
              return stats
  
          @override(base)
          def compute_gradients_fn(
              self, optimizer: LocalOptimizer, loss: TensorType
          ) -> ModelGradients:
              return compute_gradients(self, optimizer, loss)
  
      MARWILTFPolicy.__name__ = name
      MARWILTFPolicy.__qualname__ = name
  
      return MARWILTFPolicy
  
imports: import logging
  from typing import Any, Dict, List, Optional, Type, Union
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_advantages, Postprocessing
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import ValueNetworkMixin, compute_gradients
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, get_variable
  from ray.rllib.utils.tf_utils import explained_variance
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType
label_desc: Use image node_id 1 for calling a global function with example usage: get_marwil_tf_policy(name, base) and returns: MARWILTFPolicy, self, stats, compute_gradients