code: def get_maml_tf_policy(name: str, base: type) -> type:
      """Construct a MAMLTFPolicy 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 MAMLTFPolicy(KLCoeffMixin, ValueNetworkMixin, 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()
  
              validate_config(config)
  
              # Initialize base class.
              base.__init__(
                  self,
                  observation_space,
                  action_space,
                  config,
                  existing_inputs=existing_inputs,
                  existing_model=existing_model,
              )
  
              KLCoeffMixin.__init__(self, config)
              ValueNetworkMixin.__init__(self, config)
  
              # Create the `split` placeholder before initialize loss.
              if self.framework == "tf":
                  self._loss_input_dict["split"] = tf1.placeholder(
                      tf.int32,
                      name="Meta-Update-Splitting",
                      shape=(
                          self.config["inner_adaptation_steps"] + 1,
                          self.config["num_workers"],
                      ),
                  )
  
              # 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]]:
              logits, state = model(train_batch)
              self.cur_lr = self.config["lr"]
  
              if self.config["worker_index"]:
                  self.loss_obj = WorkerLoss(
                      dist_class=dist_class,
                      actions=train_batch[SampleBatch.ACTIONS],
                      curr_logits=logits,
                      behaviour_logits=train_batch[
                          SampleBatch.ACTION_DIST_INPUTS
                      ],
                      advantages=train_batch[Postprocessing.ADVANTAGES],
                      value_fn=model.value_function(),
                      value_targets=train_batch[
                          Postprocessing.VALUE_TARGETS
                      ],
                      vf_preds=train_batch[SampleBatch.VF_PREDS],
                      cur_kl_coeff=0.0,
                      entropy_coeff=self.config["entropy_coeff"],
                      clip_param=self.config["clip_param"],
                      vf_clip_param=self.config["vf_clip_param"],
                      vf_loss_coeff=self.config["vf_loss_coeff"],
                      clip_loss=False,
                  )
              else:
                  self.var_list = tf1.get_collection(
                      tf1.GraphKeys.TRAINABLE_VARIABLES,
                      tf1.get_variable_scope().name,
                  )
                  self.loss_obj = MAMLLoss(
                      model=model,
                      dist_class=dist_class,
                      value_targets=train_batch[
                          Postprocessing.VALUE_TARGETS
                      ],
                      advantages=train_batch[Postprocessing.ADVANTAGES],
                      actions=train_batch[SampleBatch.ACTIONS],
                      behaviour_logits=train_batch[
                          SampleBatch.ACTION_DIST_INPUTS
                      ],
                      vf_preds=train_batch[SampleBatch.VF_PREDS],
                      cur_kl_coeff=self.kl_coeff,
                      policy_vars=self.var_list,
                      obs=train_batch[SampleBatch.CUR_OBS],
                      num_tasks=self.config["num_workers"],
                      split=train_batch["split"],
                      config=self.config,
                      inner_adaptation_steps=self.config[
                          "inner_adaptation_steps"
                      ],
                      entropy_coeff=self.config["entropy_coeff"],
                      clip_param=self.config["clip_param"],
                      vf_clip_param=self.config["vf_clip_param"],
                      vf_loss_coeff=self.config["vf_loss_coeff"],
                      use_gae=self.config["use_gae"],
                  )
  
              return self.loss_obj.loss
  
          @override(base)
          def optimizer(
              self,
          ) -> Union[
              "tf.keras.optimizers.Optimizer",
              List["tf.keras.optimizers.Optimizer"],
          ]:
              """
              Workers use simple SGD for inner adaptation
              Meta-Policy uses Adam optimizer for meta-update
              """
              if not self.config["worker_index"]:
                  return tf1.train.AdamOptimizer(
                      learning_rate=self.config["lr"]
                  )
              return tf1.train.GradientDescentOptimizer(
                  learning_rate=self.config["inner_lr"]
              )
  
          @override(base)
          def stats_fn(
              self, train_batch: SampleBatch
          ) -> Dict[str, TensorType]:
              if self.config["worker_index"]:
                  return {"worker_loss": self.loss_obj.loss}
              else:
                  return {
                      "cur_kl_coeff": tf.cast(
                          self.kl_coeff, tf.float64
                      ),
                      "cur_lr": tf.cast(self.cur_lr, tf.float64),
                      "total_loss": self.loss_obj.loss,
                      "policy_loss": self.loss_obj.mean_policy_loss,
                      "vf_loss": self.loss_obj.mean_vf_loss,
                      "kl": self.loss_obj.mean_kl,
                      "inner_kl": self.loss_obj.mean_inner_kl,
                      "entropy": self.loss_obj.mean_entropy,
                  }
  
          @override(base)
          def postprocess_trajectory(
              self, sample_batch, other_agent_batches=None, episode=None
          ):
              sample_batch = super().postprocess_trajectory(
                  sample_batch
              )
              return compute_gae_for_sample_batch(
                  self, sample_batch, other_agent_batches, episode
              )
  
          @override(base)
          def compute_gradients_fn(
              self, optimizer: LocalOptimizer, loss: TensorType
          ) -> ModelGradients:
              return compute_gradients(self, optimizer, loss)
  
      MAMLTFPolicy.__name__ = name
      MAMLTFPolicy.__qualname__ = name
  
      return MAMLTFPolicy
  
imports: import logging
  from typing import Dict, List, Type, Union
  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.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.utils import get_activation_fn
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LocalOptimizer, ModelGradients, ValueNetworkMixin, compute_gradients
  from ray.rllib.utils import try_import_tf
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 2 for calling a global function with example usage: get_maml_tf_policy(name, base) and returns: MAMLTFPolicy, self, tf1, compute_gae_for_sample_batch, compute_gradients, tf1, dict, dict