code: def _average_gradients(tower_grads):
      """Averages gradients across towers.
  
      Calculate the average gradient for each shared variable across all towers.
      Note that this function provides a synchronization point across all towers.
  
      Args:
          tower_grads: List of lists of (gradient, variable) tuples. The outer
              list is over individual gradients. The inner list is over the
              gradient calculation for each tower.
  
      Returns:
         List of pairs of (gradient, variable) where the gradient has been
             averaged across all towers.
  
      TODO(ekl): We could use NCCL if this becomes a bottleneck.
      """
  
      average_grads = []
      for grad_and_vars in zip(*tower_grads):
          # Note that each grad_and_vars looks like the following:
          #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
          grads = []
          for g, _ in grad_and_vars:
              if g is not None:
                  # Add 0 dimension to the gradients to represent the tower.
                  expanded_g = tf.expand_dims(g, 0)
  
                  # Append on a 'tower' dimension which we will average over
                  # below.
                  grads.append(expanded_g)
  
          if not grads:
              continue
  
          # Average over the 'tower' dimension.
          grad = tf.concat(axis=0, values=grads)
          grad = tf.reduce_mean(grad, 0)
  
          # Keep in mind that the Variables are redundant because they are shared
          # across towers. So .. we will just return the first tower's pointer to
          # the Variable.
          v = grad_and_vars[0][1]
          grad_and_var = (grad, v)
          average_grads.append(grad_and_var)
  
      return average_grads
  
imports: from collections import namedtuple, OrderedDict
  import gymnasium
  import logging
  import re
  import tree
  from typing import Callable, Dict, List, Optional, Tuple, Type, Union
  from ray.util.debug import log_once
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.utils import force_list
  from ray.rllib.utils.annotations import override, DeveloperAPI
  from ray.rllib.utils.debug import summarize
  from ray.rllib.utils.deprecation import deprecation_warning, DEPRECATED_VALUE
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.spaces.space_utils import get_dummy_batch_for_space
  from ray.rllib.utils.tf_utils import get_placeholder
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 2 for calling a global function with example usage: _average_gradients(tower_grads) and returns: average_grads