code: def average_gradients(tower_gradients):
      r"""
      A routine for computing each variable's average of the gradients obtained from the GPUs.
      Note also that this code acts as a synchronization point as it requires all
      GPUs to be finished with their mini-batch before it can run to completion.
      """
      # List of average gradients to return to the caller
      average_grads = []
  
      # Run this on cpu_device to conserve GPU memory
      with tf.device(Config.cpu_device):
          # Loop over gradient/variable pairs from all towers
          for grad_and_vars in zip(*tower_gradients):
              # Introduce grads to store the gradients for the current variable
              grads = []
  
              # Loop over the gradients for the current variable
              for g, _ in grad_and_vars:
                  # 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)
  
              # Average over the 'tower' dimension
              grad = tf.concat(grads, 0)
              grad = tf.reduce_mean(input_tensor=grad, axis=0)
  
              # Create a gradient/variable tuple for the current variable with its average gradient
              grad_and_var = (grad, grad_and_vars[0][1])
  
              # Add the current tuple to average_grads
              average_grads.append(grad_and_var)
  
      # Return result to caller
      return average_grads
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import absl.app
  import numpy
  import progressbar
  import shutil
  import tensorflow
  import tensorflow.compat.v1
  import time
  from datetime import datetime
  from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
  from .evaluate import evaluate
  from six.moves import zip, range
  from .util.config import Config, initialize_globals
  from .util.checkpoints import load_or_init_graph_for_training, load_graph_for_evaluation, reload_best_checkpoint
  from .util.evaluate_tools import save_samples_json
  from .util.feeding import create_dataset, audio_to_features, audiofile_to_features
  from .util.flags import create_flags, FLAGS
  from .util.helpers import check_ctcdecoder_version, ExceptionBox
  from .util.logging import create_progressbar, log_debug, log_error, log_info, log_progress, log_warn
  from .util.io import open_remote, remove_remote, listdir_remote, is_remote_path, isdir_remote
label_desc: Use image node_id 11 for calling a global function with example usage: average_gradients(tower_gradients) and returns: average_grads