code: def laplace(input, output=None, mode="reflect", cval=0.0):
      """N-dimensional Laplace filter based on approximate second derivatives.
  
      Parameters
      ----------
      %(input)s
      %(output)s
      %(mode_multiple)s
      %(cval)s
  
      Examples
      --------
      >>> from scipy import ndimage, misc
      >>> import matplotlib.pyplot as plt
      >>> 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
      >>> ascent = misc.ascent()
      >>> result = ndimage.laplace(ascent)
      >>> ax1.imshow(ascent)
      >>> ax2.imshow(result)
      >>> plt.show()
      """
  
      def derivative2(input, axis, output, mode, cval):
          return correlate1d(
              input, [1, -2, 1], axis, output, mode, cval, 0
          )
  
      return generic_laplace(input, derivative2, output, mode, cval)
  
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 10 for calling a global function with example usage: laplace(input, output, mode, cval) and returns: generic_laplace, correlate1d