parameters: (input, derivative2, output=None, mode="reflect",
                      cval=0.0,
                      extra_arguments=(),
                      extra_keywords = None)
return_statement: return return_value
docstring: N-dimensional Laplace filter using a provided second derivative function
  
      Parameters
      ----------
      %(input)s
      derivative2 : callable
          Callable with the following signature::
  
              derivative2(input, axis, output, mode, cval,
                          *extra_arguments, **extra_keywords)
  
          See `extra_arguments`, `extra_keywords` below.
      %(output)s
      %(mode)s
      %(cval)s
      %(extra_keywords)s
      %(extra_arguments)s
docstring_summary: N-dimensional Laplace filter using a provided second derivative function
docstring_tokens: N - dimensional Laplace filter using a provided second derivative function
function: def generic_laplace(input, derivative2, output=None, mode="reflect",
                      cval=0.0,
                      extra_arguments=(),
                      extra_keywords = None):
      """N-dimensional Laplace filter using a provided second derivative function
  
      Parameters
      ----------
      %(input)s
      derivative2 : callable
          Callable with the following signature::
  
              derivative2(input, axis, output, mode, cval,
                          *extra_arguments, **extra_keywords)
  
          See `extra_arguments`, `extra_keywords` below.
      %(output)s
      %(mode)s
      %(cval)s
      %(extra_keywords)s
      %(extra_arguments)s
      """
      if extra_keywords is None:
          extra_keywords = {}
      input = numpy.asarray(input)
      output, return_value = _ni_support._get_output(output, input)
      axes = list(range(input.ndim))
      if len(axes) > 0:
          derivative2(input, axes[0], output, mode, cval,
                      *extra_arguments, **extra_keywords)
          for ii in range(1, len(axes)):
              tmp = derivative2(input, axes[ii], output.dtype, mode, cval,
                                *extra_arguments, **extra_keywords)
              output += tmp
      else:
          output[...] = input[...]
      return return_value
function_tokens: def generic_laplace ( input , derivative2 , output = None , mode = "reflect" , cval = 0.0 , extra_arguments = ( ) , extra_keywords = None ) : if extra_keywords is None : extra_keywords = { } input = numpy . asarray ( input ) output , return_value = _ni_support . _get_output ( output , input ) axes = list ( range ( input . ndim ) ) if len ( axes ) > 0 : derivative2 ( input , axes [ 0 ] , output , mode , cval , * extra_arguments , * * extra_keywords ) for ii in range ( 1 , len ( axes ) ) : tmp = derivative2 ( input , axes [ ii ] , output . dtype , mode , cval , * extra_arguments , * * extra_keywords ) output += tmp else : output [ ... ] = input [ ... ] return return_value