code: def roberts_pos_diag(image, mask=None):
      """Find the cross edges of an image using 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::
  
        1   0
        0  -1
  
      """
      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_PD_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 15 for calling a global function with example usage: roberts_pos_diag(image, mask) and returns: _mask_filter_result