docs: def _model_mask(self, wavelengths=None):
          """
          Apply pre-defined model masks.
          """
  
          if wavelengths is None:
              wavelengths = self.wavelengths
          wavelengths = np.array(wavelengths)
  
          mask = np.ones_like(wavelengths, dtype=bool)
          model_mask = self._configuration.get("masks", {}).get("model", [])
          logger.debug("Applying model mask: {0}".format(model_mask))
          for start, end in model_mask:
              idx = np.clip(wavelengths.searchsorted([start, end]) + [0, 1], 0,
                  wavelengths.size)
              mask[idx[0]:idx[1]] = False
          return mask