code: def get_a3c_tf_policy(
      name: str, base: TFPolicyV2Type
  ) -> TFPolicyV2Type:
      """Construct a A3CTFPolicy 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 A3CTFPolicy(
          ValueNetworkMixin,
          LearningRateSchedule,
          EntropyCoeffSchedule,
          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, self.config)
              LearningRateSchedule.__init__(
                  self, self.config["lr"], self.config["lr_schedule"]
              )
              EntropyCoeffSchedule.__init__(
                  self,
                  config["entropy_coeff"],
                  config["entropy_coeff_schedule"],
              )
  
              # 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)
              if self.is_recurrent():
                  max_seq_len = tf.reduce_max(
                      train_batch[SampleBatch.SEQ_LENS]
                  )
                  valid_mask = tf.sequence_mask(
                      train_batch[SampleBatch.SEQ_LENS], max_seq_len
                  )
                  valid_mask = tf.reshape(valid_mask, [-1])
              else:
                  valid_mask = tf.ones_like(
                      train_batch[SampleBatch.REWARDS]
                  )
  
              log_prob = action_dist.logp(
                  train_batch[SampleBatch.ACTIONS]
              )
              vf = model.value_function()
  
              # The "policy gradients" loss
              self.pi_loss = -tf.reduce_sum(
                  tf.boolean_mask(
                      log_prob * train_batch[Postprocessing.ADVANTAGES],
                      valid_mask,
                  )
              )
  
              delta = tf.boolean_mask(
                  vf - train_batch[Postprocessing.VALUE_TARGETS],
                  valid_mask,
              )
  
              # Compute a value function loss.
              if self.config.get("use_critic", True):
                  self.vf_loss = 0.5 * tf.reduce_sum(
                      tf.math.square(delta)
                  )
              # Ignore the value function.
              else:
                  self.vf_loss = tf.constant(0.0)
  
              self.entropy_loss = tf.reduce_sum(
                  tf.boolean_mask(action_dist.entropy(), valid_mask)
              )
  
              self.total_loss = (
                  self.pi_loss
                  + self.vf_loss * self.config["vf_loss_coeff"]
                  - self.entropy_loss * self.entropy_coeff
              )
  
              return self.total_loss
  
          @override(base)
          def stats_fn(
              self, train_batch: SampleBatch
          ) -> Dict[str, TensorType]:
              return {
                  "cur_lr": tf.cast(self.cur_lr, tf.float64),
                  "entropy_coeff": tf.cast(
                      self.entropy_coeff, tf.float64
                  ),
                  "policy_loss": self.pi_loss,
                  "policy_entropy": self.entropy_loss,
                  "var_gnorm": tf.linalg.global_norm(
                      list(self.model.trainable_variables())
                  ),
                  "vf_loss": self.vf_loss,
              }
  
          @override(base)
          def grad_stats_fn(
              self, train_batch: SampleBatch, grads: ModelGradients
          ) -> Dict[str, TensorType]:
              return {
                  "grad_gnorm": tf.linalg.global_norm(grads),
                  "vf_explained_var": explained_variance(
                      train_batch[Postprocessing.VALUE_TARGETS],
                      self.model.value_function(),
                  ),
              }
  
          @override(base)
          def postprocess_trajectory(
              self,
              sample_batch: SampleBatch,
              other_agent_batches: Optional[
                  Dict[AgentID, SampleBatch]
              ] = None,
              episode: Optional[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)
  
      A3CTFPolicy.__name__ = name
      A3CTFPolicy.__qualname__ = name
  
      return A3CTFPolicy
  
file_path: ray/rllib_contrib/a3c/src/rllib_a3c/a3c/a3c_tf_policy.py
imports: from typing import Dict, List, Optional, Type, Union
  from ray.rllib.evaluation.episode import Episode
  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.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 EntropyCoeffSchedule, LearningRateSchedule, ValueNetworkMixin, compute_gradients
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.tf_utils import explained_variance
  from ray.rllib.utils.typing import AgentID, LocalOptimizer, ModelGradients, TensorType, TFPolicyV2Type
label_desc: Use image node_id 1 for calling a global function with example usage: get_a3c_tf_policy(name, base) and returns: A3CTFPolicy, self, dict, dict, compute_gae_for_sample_batch, compute_gradients