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_dtype = _util._init_weights_dtype(input)
      weights = cupy.array([1, -2, 1], dtype=weights_dtype)
  
      def derivative2(input, axis, output, mode, cval):
          return correlate1d(input, weights, axis, output, mode, cval)
  
      return generic_laplace(input, derivative2, output, mode, cval)
  
imports: import numpy
  import cupy
  from cupy import _core
  from cupy._core import internal
  from cupyx.scipy.ndimage import _util
  from cupyx.scipy.ndimage import _filters_core
  from cupyx.scipy.ndimage import _filters_generic
label_desc: Use image node_id 17 for calling a global function with example usage: laplace(input, output, mode, cval) and returns: generic_laplace, correlate1d