code: def aggregate_gradients_using_nccl(replica_grads):
      """Aggregate gradients using nccl allreduce."""
      agg_all_g_and_v = []
      for single_g_and_v in zip(*replica_grads):
          single_grads = [g for g, _ in single_g_and_v]
          agg_grads = nccl_ops.all_sum(single_grads)
          agg_all_g_and_v.append(
              [(g, v) for g, (_, v) in zip(agg_grads, single_g_and_v)]
          )
  
      agg_all_g_and_v = list(zip(*agg_all_g_and_v))
  
      return agg_all_g_and_v
  
imports: import copy
  import threading
  from typing import Callable, List, Optional, Union
  from tensorflow.python.distribute import collective_util
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import backprop_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import collective_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nccl_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import core
label_desc: Use image node_id 1 for calling a global function with example usage: aggregate_gradients_using_nccl(replica_grads) and returns: agg_all_g_and_v