code: def grad_diff(ans, a, n=1, axis=-1):
      nd = anp.ndim(a)
      ans_shape = anp.shape(ans)
      sl1 = [slice(None)] * nd
      sl1[axis] = slice(None, 1)
  
      sl2 = [slice(None)] * nd
      sl2[axis] = slice(-1, None)
  
      def undiff(g):
          if g.shape[axis] > 0:
              return anp.concatenate(
                  (
                      -g[tuple(sl1)],
                      -anp.diff(g, axis=axis),
                      g[tuple(sl2)],
                  ),
                  axis=axis,
              )
          shape = list(ans_shape)
          shape[axis] = 1
          return anp.zeros(shape)
  
      def helper(g, n):
          if n == 0:
              return g
          return helper(undiff(g), n - 1)
  
      return lambda g: helper(g, n)
  
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 2 for calling a global function with example usage: grad_diff(ans, a, n, axis) and returns: function, anp, helper, anp, g