How to use the identify.M_LOCATION_CENTER_X function in identify

result[~mask] = no_parent
                    else:
                        result[~mask] = no_parent[~mask]
                if np.any(mask):
                    result[mask] = self.backing_store[
                        idx.fwd_idx[parent_image_indexes[my_slice][mask]] +
                        parent_object_indexes[my_slice][mask]]
                return result

            def get_ancestor(self, index):
                return self.backing_store[ancestral_index[slyce(index)]]
        
        #
        # Recalculate the trajectories
        #
        x = wrapped(M_LOCATION_CENTER_X)
        y = wrapped(M_LOCATION_CENTER_Y)
        trajectory_x = wrapped(self.measurement_name(F_TRAJECTORY_X))
        trajectory_y = wrapped(self.measurement_name(F_TRAJECTORY_Y))
        integrated = wrapped(self.measurement_name(F_INTEGRATED_DISTANCE))
        dists = wrapped(self.measurement_name(F_DISTANCE_TRAVELED))
        displ = wrapped(self.measurement_name(F_DISPLACEMENT))
        linearity = wrapped(self.measurement_name(F_LINEARITY))
        lifetimes = wrapped(self.measurement_name(F_LIFETIME))
        label = wrapped(self.measurement_name(F_LABEL))
        final_age = wrapped(self.measurement_name(F_FINAL_AGE))
        
        age = {} # Dictionary of per-label ages  
        if self.wants_lifetime_filtering.value:
            minimum_lifetime = self.min_lifetime.value if self.wants_minimum_lifetime.value else -np.Inf
            maximum_lifetime = self.max_lifetime.value if self.wants_maximum_lifetime.value else np.Inf

para1 = self.max_gap_score.value #max upper-left
        para2 = self.max_merge_score.value #max upper-middle
        para3 = gap_termination_cost #value for upper-right
        para4 = self.max_split_score.value #max for middle-left
        para5 = split_termination_cost #value for middle-right
        para6 = gap_initiation_cost #value for lower-left
        para7 = merge_initiation_cost #value for lower-middle
        para8 = self.max_frame_distance.value #max frame difference

        image_numbers = self.get_image_numbers(workspace)
        m = workspace.measurements
        label = m.get_all_measurements(self.object_name.value, 
                                       self.measurement_name(F_LABEL))
        orig_label = label
        a = m.get_all_measurements(self.object_name.value, M_LOCATION_CENTER_X)
        b = m.get_all_measurements(self.object_name.value, M_LOCATION_CENTER_Y)
        Area = m.get_all_measurements(self.object_name.value, 
                                      self.measurement_name(F_AREA))
        #
        # Reduce the lists to only the ones in the group
        #
        for mlist in (label, a, b, Area):
            mlist = [mlist[image_number] for image_number in image_numbers]
        numFrames = len(b)

        #Calculates the maximum number of cells in a single frame

        i = 0
        mlength = 0
        while i

max_merge_score = self.max_merge_score.value
        max_split_score = self.max_split_score.value / 2 # to match legacy
        max_frame_difference = self.max_frame_distance.value

        m = workspace.measurements
        assert(isinstance(m, cpmeas.Measurements))
        image_numbers = self.get_group_image_numbers(workspace)
        object_name = self.object_name.value
        label, object_numbers, a, b, Area, \
             parent_object_numbers, parent_image_numbers = [
            [m.get_measurement(object_name, feature, i).astype(mtype)
             for i in image_numbers]
            for feature, mtype in (
                (self.measurement_name(F_LABEL), int),
                (cpmeas.OBJECT_NUMBER, int),
                (M_LOCATION_CENTER_X, float),
                (M_LOCATION_CENTER_Y, float),
                (self.measurement_name(F_AREA), float),
                (self.measurement_name(F_PARENT_OBJECT_NUMBER), int),
                (self.measurement_name(F_PARENT_IMAGE_NUMBER), int)
            )]
        group_indices, new_object_count, lost_object_count, merge_count, \
                     split_count = [
            np.array([m.get_measurement(cpmeas.IMAGE, feature, i)
                      for i in image_numbers], int)
            for feature in (cpmeas.GROUP_INDEX,
                            self.image_measurement_name(F_NEW_OBJECT_COUNT),
                            self.image_measurement_name(F_LOST_OBJECT_COUNT),
                            self.image_measurement_name(F_MERGE_COUNT),
                            self.image_measurement_name(F_SPLIT_COUNT))]
        #
        # Map image number to group index and vice versa