code: def aggregate_single_gradient_using_copy(
      grad_and_vars, use_mean, check_inf_nan
  ):
      """Calculate the average gradient for a shared variable across all replicas.
  
      Note that this function provides a synchronization point across all replicas.
  
      Args:
        grad_and_vars: A list or tuple of (gradient, variable) tuples. Each
          (gradient, variable) pair within the outer list represents the gradient
          of the variable calculated for a single replica, and the number of pairs
          equals the number of replicas.
        use_mean: if True, mean is taken, else sum of gradients is taken.
        check_inf_nan: check grads for nans and infs.
  
      Returns:
        The tuple ([(average_gradient, variable),], has_nan_or_inf) where the
          gradient has been averaged across all replicas. The variable is chosen
          from the first replica. The has_nan_or_inf indicates the grads has nan or
          inf.
      """
      grads = [g for g, _ in grad_and_vars]
      grad = math_ops.add_n(grads)
  
      if use_mean and len(grads) > 1:
          grad = array_ops.multiply(grad, 1.0 / len(grads))
  
      v = grad_and_vars[0][1]
      if check_inf_nan:
          has_nan_or_inf = array_ops.logical_not(
              array_ops.reduce_all(array_ops.is_finite(grads))
          )
          return (grad, v), has_nan_or_inf
      else:
          return (grad, v), None
  
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 3 for calling a global function with example usage: aggregate_single_gradient_using_copy(grad_and_vars, use_mean, check_inf_nan) and returns: grad,  v,  has_nan_or_inf, grad,  v,  None