code: def _round_safe(coords):
      """Round coords while ensuring successive values are less than 1 apart.
  
      When rounding coordinates for `line_nd`, we want coordinates that are less
      than 1 apart (always the case, by design) to remain less than one apart.
      However, NumPy rounds values to the nearest *even* integer, so:
  
      >>> np.round([0.5, 1.5, 2.5, 3.5, 4.5])
      array([0., 2., 2., 4., 4.])
  
      So, for our application, we detect whether the above case occurs, and use
      ``np.floor`` if so. It is sufficient to detect that the first coordinate
      falls on 0.5 and that the second coordinate is 1.0 apart, since we assume
      by construction that the inter-point distance is less than or equal to 1
      and that all successive points are equidistant.
  
      Parameters
      ----------
      coords : 1D array of float
          The coordinates array. We assume that all successive values are
          equidistant (``np.all(np.diff(coords) = coords[1] - coords[0])``)
          and that this distance is no more than 1
          (``np.abs(coords[1] - coords[0]) <= 1``).
  
      Returns
      -------
      rounded : 1D array of int
          The array correctly rounded for an indexing operation, such that no
          successive indices will be more than 1 apart.
  
      Examples
      --------
      >>> coords0 = np.array([0.5, 1.25, 2., 2.75, 3.5])
      >>> _round_safe(coords0)
      array([0, 1, 2, 3, 4])
      >>> coords1 = np.arange(0.5, 8, 1)
      >>> coords1
      array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5])
      >>> _round_safe(coords1)
      array([0, 1, 2, 3, 4, 5, 6, 7])
      """
      if (
          len(coords) > 1
          and coords[0] % 1 == 0.5
          and coords[1] - coords[0] == 1
      ):
          _round_function = np.floor
      else:
          _round_function = np.round
      return _round_function(coords).astype(int)
  
label_desc: Use image node_id 1 for calling a global function with example usage: _round_safe(coords) and returns: _round_function