human
Solve the following python programming problem given the following code:
@ docfiller 
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)s
    %(cval)s
    Extra keyword arguments will be passed to gaussian_filter().
    ' 
    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, sigma, output, mode, cval, extra_arguments = (sigma,), extra_keywords = kwargs) 

    
    

    Fix the buggy line: return generic_laplace (input, sigma, output, mode, cval, extra_arguments = (sigma,), extra_keywords = kwargs)
assistant
#Here is Python code to fix the buggy line: return generic_laplace (input, sigma, output, mode, cval, extra_arguments = (sigma,), extra_keywords = kwargs)
.

return generic_laplace (input, def derivative2(, output, mode, cval, extra_arguments = (sigma,), extra_keywords = kwargs)