original_docstring: Multi-dimensional Laplace filter using Gaussian second derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          kwargs (dict, optional):
              dict of extra keyword arguments to pass ``gaussian_filter()``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_laplace`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      
docstring: Multi-dimensional Laplace filter using Gaussian second derivatives.
docstring_tokens: Multi - dimensional Laplace filter using Gaussian second derivatives .
code: def gaussian_laplace(input, sigma, output=None, mode="reflect",
                       cval=0.0, **kwargs):
      """Multi-dimensional Laplace filter using Gaussian second derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          kwargs (dict, optional):
              dict of extra keyword arguments to pass ``gaussian_filter()``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_laplace`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      """
      def derivative2(input, axis, output, mode, cval):
          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)
code_tokens: def gaussian_laplace ( input , sigma , output = None , mode = "reflect" , cval = 0.0 , ** kwargs ) : def derivative2 ( input , axis , output , mode , cval ) : 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 )
short_docstring: Multi-dimensional Laplace filter using Gaussian second derivatives.
short_docstring_tokens: Multi - dimensional Laplace filter using Gaussian second derivatives .
comment: """Multi-dimensional Laplace filter using Gaussian second derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          sigma (scalar or sequence of scalar): Standard deviations for each axis
              of Gaussian kernel. A single value applies to all axes.
          output (cupy.ndarray, dtype or None): The array in which to place the
              output. Default is is same dtype as the input.
          mode (str): The array borders are handled according to the given mode
              (``'reflect'``, ``'constant'``, ``'nearest'``, ``'mirror'``,
              ``'wrap'``). Default is ``'reflect'``.
          cval (scalar): Value to fill past edges of input if mode is
              ``'constant'``. Default is ``0.0``.
          kwargs (dict, optional):
              dict of extra keyword arguments to pass ``gaussian_filter()``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.gaussian_laplace`
  
      .. note::
          When the output data type is integral (or when no output is provided
          and input is integral) the results may not perfectly match the results
          from SciPy due to floating-point rounding of intermediate results.
      """