code: def roberts_neg_diag(image, mask=None):
      """Find the cross edges of an image using the Roberts' Cross operator.
  
      The kernel is applied to the input image to produce separate measurements
      of the gradient component one orientation.
  
      Parameters
      ----------
      image : 2-D array
          Image to process.
      mask : 2-D array, optional
          An optional mask to limit the application to a certain area.
          Note that pixels surrounding masked regions are also masked to
          prevent masked regions from affecting the result.
  
      Returns
      -------
      output : 2-D array
          The Robert's edge map.
  
      Notes
      -----
      We use the following kernel::
  
        0   1
       -1   0
  
      """
      check_nD(image, 2)
      if image.dtype.kind == "f":
          float_dtype = _supported_float_type(image.dtype)
          image = image.astype(float_dtype, copy=False)
      else:
          image = img_as_float(image)
      result = convolve(image, ROBERTS_ND_WEIGHTS)
      return _mask_filter_result(result, mask)
  
imports: import numpy
  from scipy import ndimage
  from scipy.ndimage import binary_erosion, convolve
  from .._shared.utils import _supported_float_type, check_nD
  from ..restoration.uft import laplacian
  from ..util.dtype import img_as_float
label_desc: Use image node_id 16 for calling a global function with example usage: roberts_neg_diag(image, mask) and returns: _mask_filter_result