code: def allreduce_grads_naive(all_grads, devices=None, average=True):
      """
      AllReduce the gradients with raw ops (instead of collective ops).
  
      Args:
          all_grads (K x N): A list of K lists. Each of the list is a list of N grad tuples.
              The variables have to be the same across the K lists.
          devices (list[str]): assign the averaging to these device in
              round-robin. Cannot be used together with ``colocation``.
          average (bool): do average or sum
  
      Returns:
          list[Tensor]: list of grads where each grad is averaged or summed over K.
      """
      if devices is not None:
          assert isinstance(devices, list), devices
          # device_setter = LeastLoadedDeviceSetter(None, devices)
  
      nr_tower = len(all_grads)
      if nr_tower == 1:
          return all_grads[0]
  
      def aggregate(grads):
          if average:
              return tf.multiply(tf.add_n(grads), 1.0 / nr_tower)
          else:
              return tf.add_n(grads)
  
      grads_ret = []  # N(rev) grads
      # reverse so the device placement makes the last part of model more balance?
      all_grads_rev = [x[::-1] for x in all_grads]  # K x N(rev)
  
      for idx, grads in enumerate(zip(*all_grads_rev)):
          # grads: K tensors
          if devices is None:
              grad = aggregate(grads)
          else:
              # dev = device_setter.place_with_balance(v.op)
              dev = devices[idx % len(devices)]
              with tf.device(dev):
                  grad = aggregate(grads)
          grads_ret.append(grad)
      grads_ret = grads_ret[::-1]
      return grads_ret
  
imports: import operator
  from contextlib import contextmanager
  import tensorflow
  import threading
  from ..compat import tfv1
  from ..tfutils.common import get_tf_version_tuple
  from ..tfutils.scope_utils import cached_name_scope, under_name_scope
  from ..tfutils.varreplace import custom_getter_scope
  from ..utils import logger
  from ..utils.argtools import call_only_once
label_desc: Use image node_id 9 for calling a global function with example usage: allreduce_grads_naive(all_grads, devices, average) and returns: grads_ret, all_grads, tf, tf