human
Write me a the Python code you requested given the following:
Complete the below
@docfiller
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()'
    '''
assistant
#Here is the Python code you requested.
    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)