code: def grad_gradient(ans, x, *vargs, **kwargs):
      axis = kwargs.pop("axis", None)
      if vargs or kwargs:
          raise NotImplementedError(
              "The only optional argument currently supported for np.gradient "
              "is axis."
          )
      if axis is None:
          axis = range(x.ndim)
      elif type(axis) is int:
          axis = [axis]
      else:
          axis = list(axis)
  
      x_dtype = x.dtype
      x_shape = x.shape
      nd = x.ndim
  
      def vjp(g):
          if anp.ndim(g) == nd:
              # add axis if gradient was along one axis only
              g = g[anp.newaxis]
  
          # accumulate gradient
          out = anp.zeros(x_shape, dtype=x_dtype)
  
          for i, a in enumerate(axis):
              # swap gradient axis to the front
              g_swap = anp.swapaxes(g[i], 0, a)[:, anp.newaxis]
  
              out_axis = anp.concatenate(
                  (
                      -g_swap[0] - 0.5 * g_swap[1],
                      g_swap[0] - 0.5 * g_swap[2],
                      (-1.0) * anp.gradient(g_swap, axis=0)[2:-2, 0],
                      0.5 * g_swap[-3] - g_swap[-1],
                      0.5 * g_swap[-2] + g_swap[-1],
                  ),
                  axis=0,
              )
  
              out = out + anp.swapaxes(out_axis, 0, a)
  
          return out
  
      return vjp
  
imports: from __future__ import absolute_import
  from six import string_types
  from functools import partial
  import numpy
  from ..util import func
  from .None import numpy_wrapper
  from .numpy_boxes import ArrayBox
  from autograd.extend import primitive, vspace, defvjp, defvjp_argnum, SparseObject, VJPNode, register_notrace
label_desc: Use image node_id 3 for calling a global function with example usage: grad_gradient(ans, x) and returns: vjp, out