code: def gaussian_laplace(
      input, sigma, output=None, mode="reflect", cval=0.0, **kwargs
  ):
      """Multidimensional Laplace filter using gaussian second derivatives.
  
      Parameters
      ----------
      %(input)s
      sigma : scalar or sequence of scalars
          The standard deviations of the Gaussian filter are given for
          each axis as a sequence, or as a single number, in which case
          it is equal for all axes.
      %(output)s
      %(mode_multiple)s
      %(cval)s
      Extra keyword arguments will be passed to gaussian_filter().
  
      Examples
      --------
      >>> from scipy import ndimage, misc
      >>> import matplotlib.pyplot as plt
      >>> ascent = misc.ascent()
  
      >>> fig = plt.figure()
      >>> plt.gray()  # show the filtered result in grayscale
      >>> ax1 = fig.add_subplot(121)  # left side
      >>> ax2 = fig.add_subplot(122)  # right side
  
      >>> result = ndimage.gaussian_laplace(ascent, sigma=1)
      >>> ax1.imshow(result)
  
      >>> result = ndimage.gaussian_laplace(ascent, sigma=3)
      >>> ax2.imshow(result)
      >>> plt.show()
      """
      input = numpy.asarray(input)
  
      def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
          order = [0] * input.ndim
          order[axis] = 2
          return gaussian_filter(
              input, sigma, order, output, mode, cval, **kwargs
          )
  
      return generic_laplace(
          input,
          derivative2,
          output,
          mode,
          cval,
          extra_arguments=(sigma,),
          extra_keywords=kwargs,
      )
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  import numbers
  import numpy
  import operator
  from .None import _ni_support
  from .None import _nd_image
  from .None import _ni_docstrings
label_desc: Use image node_id 11 for calling a global function with example usage: gaussian_laplace(input, sigma, output, mode, cval) and returns: generic_laplace, gaussian_filter