code: def gauss_spline(x, n):
      r"""Gaussian approximation to B-spline basis function of order n.
  
      Parameters
      ----------
      x : array_like
          a knot vector
      n : int
          The order of the spline. Must be nonnegative, i.e. n >= 0
  
      Returns
      -------
      res : ndarray
          B-spline basis function values approximated by a zero-mean Gaussian
          function.
  
      Notes
      -----
      The B-spline basis function can be approximated well by a zero-mean
      Gaussian function with standard-deviation equal to :math:`\sigma=(n+1)/12`
      for large `n` :
  
      .. math::  \frac{1}{\sqrt {2\pi\sigma^2}}exp(-\frac{x^2}{2\sigma})
  
      See [1]_, [2]_ for more information.
  
      References
      ----------
      .. [1] Bouma H., Vilanova A., Bescos J.O., ter Haar Romeny B.M., Gerritsen
         F.A. (2007) Fast and Accurate Gaussian Derivatives Based on B-Splines.
         In: Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational
         Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer
         Science, vol 4485. Springer, Berlin, Heidelberg
      .. [2] http://folk.uio.no/inf3330/scripting/doc/python/SciPy/tutorial/old/node24.html
      """  # NOQA
      x = cupy.asarray(x)
      return _gauss_spline_kernel(x, n)
  
imports: import cupy
  import cupyx.scipy.ndimage
  from cupyx.scipy.signal._iir_utils import apply_iir_sos
  from cupyx.scipy.signal._splines import _symiirorder1_nd, _symiirorder2_nd
  from cupyx.scipy.interpolate._bspline import BSpline
  import numpy
label_desc: Use image node_id 18 for calling a global function with example usage: gauss_spline(x, n) and returns: _gauss_spline_kernel