How to use the identify.FF_FINAL_THRESHOLD function in identify

out_img = cpi.Image(secondary_outline.astype(bool),
                                parent_image = image)
            workspace.image_set.add(self.outlines_name.value, out_img)
        object_count = np.max(segmented_out)
        #
        # Add the background measurements if made
        #
        measurements = workspace.measurements
        if has_threshold:
            if isinstance(local_threshold,np.ndarray):
                ave_threshold = np.mean(local_threshold)
            else:
                ave_threshold = local_threshold
            
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_FINAL_THRESHOLD%(objname),
                                         np.array([ave_threshold],
                                                     dtype=float))
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_ORIG_THRESHOLD%(objname),
                                         np.array([global_threshold],
                                                      dtype=float))
            wv = cpthresh.weighted_variance(img, mask, local_threshold)
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_WEIGHTED_VARIANCE%(objname),
                                         np.array([wv],dtype=float))
            entropies = cpthresh.sum_of_entropies(img, mask, local_threshold)
            measurements.add_measurement(cpmeas.IMAGE,
                                         cpmi.FF_SUM_OF_ENTROPIES%(objname),
                                         np.array([entropies],dtype=float))
        cpmi.add_object_count_measurements(measurements, objname, object_count)
        cpmi.add_object_location_measurements(measurements, objname,