original_docstring: Multi-dimensional Laplace filter based on approximate second
      derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          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``.
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.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 based on approximate second
  derivatives.
docstring_tokens: Multi - dimensional Laplace filter based on approximate second derivatives .
code: def laplace(input, output=None, mode="reflect", cval=0.0):
      """Multi-dimensional Laplace filter based on approximate second
      derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          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``.
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.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.
      """
      weights = cupy.array([1, -2, 1], dtype=cupy.float64)
  
      def derivative2(input, axis, output, mode, cval):
          return correlate1d(input, weights, axis, output, mode, cval)
  
      return generic_laplace(input, derivative2, output, mode, cval)
code_tokens: def laplace ( input , output = None , mode = "reflect" , cval = 0.0 ) : weights = cupy . array ( [ 1 , - 2 , 1 ] , dtype = cupy . float64 ) def derivative2 ( input , axis , output , mode , cval ) : return correlate1d ( input , weights , axis , output , mode , cval ) return generic_laplace ( input , derivative2 , output , mode , cval )
short_docstring: Multi-dimensional Laplace filter based on approximate second
  derivatives.
short_docstring_tokens: Multi - dimensional Laplace filter based on approximate second derivatives .
comment: """Multi-dimensional Laplace filter based on approximate second
      derivatives.
  
      Args:
          input (cupy.ndarray): The input array.
          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``.
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.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.
      """