code: def _average_gradients_across_replicas(replica_context, gradients):
      """Computes the average gradient across replicas.
  
      This computes the gradient locally on this device, then copies over the
      gradients computed on the other replicas, and takes the average across
      replicas.
  
      This is faster than copying the gradients from TPU to CPU, and averaging
      them on the CPU (which is what we do for the losses/fetches).
  
      Args:
        replica_context: the return value of `tf.distribute.get_replica_context()`.
        gradients: The output of tape.gradients(loss, variables)
  
      Returns:
        A list of (d_loss/d_varabiable)s.
      """
  
      # We must remove any Nones from gradients before passing them to all_reduce.
      # Nones occur when you call tape.gradient(loss, variables) with some
      # variables that don't affect the loss.
      # See: https://github.com/tensorflow/tensorflow/issues/783
      gradients_without_nones = [g for g in gradients if g is not None]
      original_indices = [
          i for i, g in enumerate(gradients) if g is not None
      ]
  
      results_without_nones = replica_context.all_reduce(
          "mean", gradients_without_nones
      )
      results = [None] * len(gradients)
      for ii, result in zip(original_indices, results_without_nones):
          results[ii] = result
  
      return results
  
imports: import time
  from typing import Dict, Iterator, List, Optional, Union, Sequence
  import acme
  from acme import types
  from acme.tf import losses
  from acme.tf import networks
  from acme.tf import savers
  from acme.tf import utils
  from acme.utils import counting
  from acme.utils import loggers
  import numpy
  import reverb
  import sonnet
  import tensorflow
  import tree
label_desc: Use image node_id 2 for calling a global function with example usage: _average_gradients_across_replicas(replica_context, gradients) and returns: results