code: def get_impala_tf_policy(
      name: str, base: TFPolicyV2Type
  ) -> TFPolicyV2Type:
      """Construct an ImpalaTFPolicy 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 Impala.
      """
  
      # VTrace mixins are placed in front of more general mixins to make sure
      # their functions like optimizer() overrides all the other implementations
      # (e.g., LearningRateSchedule.optimizer())
      class ImpalaTFPolicy(
          VTraceClipGradients,
          VTraceOptimizer,
          LearningRateSchedule,
          EntropyCoeffSchedule,
          GradStatsMixin,
          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()
  
              # Initialize base class.
              base.__init__(
                  self,
                  observation_space,
                  action_space,
                  config,
                  existing_inputs=existing_inputs,
                  existing_model=existing_model,
              )
              ValueNetworkMixin.__init__(self, config)
  
              # If Learner API is used, we don't need any loss-specific mixins.
              # However, we also would like to avoid creating special Policy-subclasses
              # for this as the entire Policy concept will soon not be used anymore with
              # the new Learner- and RLModule APIs.
              if not self.config.get("_enable_new_api_stack"):
                  GradStatsMixin.__init__(self)
                  VTraceClipGradients.__init__(self)
                  VTraceOptimizer.__init__(self)
                  LearningRateSchedule.__init__(
                      self, config["lr"], 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 isinstance(self.action_space, gym.spaces.Discrete):
                  is_multidiscrete = False
                  output_hidden_shape = [self.action_space.n]
              elif isinstance(
                  self.action_space, gym.spaces.MultiDiscrete
              ):
                  is_multidiscrete = True
                  output_hidden_shape = self.action_space.nvec.astype(
                      np.int32
                  )
              else:
                  is_multidiscrete = False
                  output_hidden_shape = 1
  
              def make_time_major(*args, **kw):
                  return _make_time_major(
                      self,
                      train_batch.get(SampleBatch.SEQ_LENS),
                      *args,
                      **kw,
                  )
  
              actions = train_batch[SampleBatch.ACTIONS]
              dones = train_batch[SampleBatch.TERMINATEDS]
              rewards = train_batch[SampleBatch.REWARDS]
              behaviour_action_logp = train_batch[
                  SampleBatch.ACTION_LOGP
              ]
              behaviour_logits = train_batch[
                  SampleBatch.ACTION_DIST_INPUTS
              ]
              unpacked_behaviour_logits = tf.split(
                  behaviour_logits, output_hidden_shape, axis=1
              )
              unpacked_outputs = tf.split(
                  model_out, output_hidden_shape, axis=1
              )
              values = model.value_function()
              values_time_major = make_time_major(values)
              bootstrap_values_time_major = make_time_major(
                  train_batch[SampleBatch.VALUES_BOOTSTRAPPED]
              )
              bootstrap_value = bootstrap_values_time_major[-1]
  
              if self.is_recurrent():
                  max_seq_len = tf.reduce_max(
                      train_batch[SampleBatch.SEQ_LENS]
                  )
                  mask = tf.sequence_mask(
                      train_batch[SampleBatch.SEQ_LENS], max_seq_len
                  )
                  mask = tf.reshape(mask, [-1])
              else:
                  mask = tf.ones_like(rewards)
  
              # Prepare actions for loss
              loss_actions = (
                  actions
                  if is_multidiscrete
                  else tf.expand_dims(actions, axis=1)
              )
  
              # Inputs are reshaped from [B * T] => [(T|T-1), B] for V-trace calc.
              self.vtrace_loss = VTraceLoss(
                  actions=make_time_major(loss_actions),
                  actions_logp=make_time_major(
                      action_dist.logp(actions)
                  ),
                  actions_entropy=make_time_major(
                      action_dist.multi_entropy()
                  ),
                  dones=make_time_major(dones),
                  behaviour_action_logp=make_time_major(
                      behaviour_action_logp
                  ),
                  behaviour_logits=make_time_major(
                      unpacked_behaviour_logits
                  ),
                  target_logits=make_time_major(unpacked_outputs),
                  discount=self.config["gamma"],
                  rewards=make_time_major(rewards),
                  values=values_time_major,
                  bootstrap_value=bootstrap_value,
                  dist_class=Categorical
                  if is_multidiscrete
                  else dist_class,
                  model=model,
                  valid_mask=make_time_major(mask),
                  config=self.config,
                  vf_loss_coeff=self.config["vf_loss_coeff"],
                  entropy_coeff=self.entropy_coeff,
                  clip_rho_threshold=self.config[
                      "vtrace_clip_rho_threshold"
                  ],
                  clip_pg_rho_threshold=self.config[
                      "vtrace_clip_pg_rho_threshold"
                  ],
              )
  
              if self.config.get("_separate_vf_optimizer"):
                  return (
                      self.vtrace_loss.loss_wo_vf,
                      self.vtrace_loss.vf_loss,
                  )
              else:
                  return self.vtrace_loss.total_loss
  
          @override(base)
          def stats_fn(
              self, train_batch: SampleBatch
          ) -> Dict[str, TensorType]:
              values_batched = _make_time_major(
                  self,
                  train_batch.get(SampleBatch.SEQ_LENS),
                  self.model.value_function(),
              )
  
              return {
                  "cur_lr": tf.cast(self.cur_lr, tf.float64),
                  "policy_loss": self.vtrace_loss.mean_pi_loss,
                  "entropy": self.vtrace_loss.mean_entropy,
                  "entropy_coeff": tf.cast(
                      self.entropy_coeff, tf.float64
                  ),
                  "var_gnorm": tf.linalg.global_norm(
                      self.model.trainable_variables()
                  ),
                  "vf_loss": self.vtrace_loss.mean_vf_loss,
                  "vf_explained_var": explained_variance(
                      tf.reshape(self.vtrace_loss.value_targets, [-1]),
                      tf.reshape(values_batched, [-1]),
                  ),
              }
  
          @override(base)
          def postprocess_trajectory(
              self,
              sample_batch: SampleBatch,
              other_agent_batches: Optional[SampleBatch] = None,
              episode: Optional["Episode"] = None,
          ):
              # Call super's postprocess_trajectory first.
              # sample_batch = super().postprocess_trajectory(
              #    sample_batch, other_agent_batches, episode
              # )
  
              if self.config["vtrace"]:
                  # Add the SampleBatch.VALUES_BOOTSTRAPPED column, which we'll need
                  # inside the loss for vtrace calculations.
                  sample_batch = compute_bootstrap_value(
                      sample_batch, self
                  )
  
              return sample_batch
  
          @override(base)
          def get_batch_divisibility_req(self) -> int:
              return self.config["rollout_fragment_length"]
  
      ImpalaTFPolicy.__name__ = name
      ImpalaTFPolicy.__qualname__ = name
  
      return ImpalaTFPolicy
  
imports: import numpy
  import logging
  import gymnasium
  from typing import Dict, List, Optional, Type, Union
  from ray.rllib.algorithms.impala import vtrace_tf
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_bootstrap_value
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import Categorical, 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 LearningRateSchedule, EntropyCoeffSchedule
  from ray.rllib.utils import force_list
  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.policy.tf_mixins import GradStatsMixin, ValueNetworkMixin
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, TFPolicyV2Type
label_desc: Use image node_id 2 for calling a global function with example usage: get_impala_tf_policy(name, base) and returns: ImpalaTFPolicy, dict, sample_batch, self, _make_time_major, self,  self, self