code: def generic_gradient_magnitude(
      input,
      derivative,
      output=None,
      mode="reflect",
      cval=0.0,
      extra_arguments=(),
      extra_keywords=None,
  ):
      """Multi-dimensional gradient magnitude filter using a provided derivative
      function.
  
      Args:
          input (cupy.ndarray): The input array.
          derivative (callable): Function or other callable with the following
              signature that is called once per axis::
  
                  derivative(input, axis, output, mode, cval,
                             *extra_arguments, **extra_keywords)
  
              where ``input`` and ``output`` are ``cupy.ndarray``, ``axis`` is an
              ``int`` from ``0`` to the number of dimensions, and ``mode``,
              ``cval``, ``extra_arguments``, ``extra_keywords`` are the values
              given to this function.
          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``.
          extra_arguments (sequence, optional):
              Sequence of extra positional arguments to pass to ``derivative2``.
          extra_keywords (dict, optional):
              dict of extra keyword arguments to pass ``derivative2``.
  
      Returns:
          cupy.ndarray: The result of the filtering.
  
      .. seealso:: :func:`scipy.ndimage.generic_gradient_magnitude`
  
      .. 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.
      """
      if extra_keywords is None:
          extra_keywords = {}
      ndim = input.ndim
      modes = _util._fix_sequence_arg(
          mode, ndim, "mode", _util._check_mode
      )
      output = _util._get_output(output, input)
      if ndim == 0:
          _core.elementwise_copy(input, output)
          return output
      derivative(
          input,
          0,
          output,
          modes[0],
          cval,
          *extra_arguments,
          **extra_keywords,
      )
      output *= output
      if ndim > 1:
          tmp = _util._get_output(output.dtype, input)
          for i in range(1, ndim):
              derivative(
                  input,
                  i,
                  tmp,
                  modes[i],
                  cval,
                  *extra_arguments,
                  **extra_keywords,
              )
              tmp *= tmp
              output += tmp
      return cupy.sqrt(output, output, casting="unsafe")
  
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 19 for calling a global function with example usage: generic_gradient_magnitude(input, derivative, output, mode, cval, extra_arguments, extra_keywords) and returns: cupy, output