How to use the orange3.Orange.widgets.visualize.ownomogram.NomogramItem function in Orange3

attr_inds,
            name_items,
            points,
            max_width,
            point_text,
            name_offset,
        )
        probs_item, nomogram_foot = self.create_footer_nomogram(
            probs_text, d, minimums, max_width, name_offset
        )
        for item in self.feature_items.values():
            item.dot.point_dot = point_item.dot
            item.dot.probs_dot = probs_item.dot
            item.dot.vertical_line = self.hidden_vertical_line

        self.nomogram = nomogram = NomogramItem()
        nomogram.add_items([nomogram_head, self.nomogram_main, nomogram_foot])
        self.scene.addItem(nomogram)

        self.set_feature_marker_values()

        rect = QRectF(
            self.scene.itemsBoundingRect().x(),
            self.scene.itemsBoundingRect().y(),
            self.scene.itemsBoundingRect().width(),
            self.nomogram.preferredSize().height(),
        ).adjusted(10, 0, 20, 0)
        self.scene.setSceneRect(rect)

        # Clip top and bottom (60 and 150) parts from the main view
        self.view.setSceneRect(
            rect.x(), rect.y() + 80, rect.width() - 10, rect.height() - 160

cls_index = self.target_class_index
        min_p = min(p.min() for p in points)
        max_p = max(p.max() for p in points)
        values = self.get_ruler_values(min_p, max_p, max_width)
        min_p, max_p = min(values), max(values)
        diff_ = np.nan_to_num(max_p - min_p)
        scale_x = max_width / diff_ if diff_ else max_width

        nomogram_header = NomogramItem()
        point_item = RulerItem(
            point_text, values, scale_x, name_offset, -scale_x * min_p
        )
        point_item.setPreferredSize(point_item.preferredWidth(), 35)
        nomogram_header.add_items([point_item])

        self.nomogram_main = NomogramItem()
        cont_feature_item_class = (
            ContinuousFeature2DItem
            if self.cont_feature_dim_index
            else ContinuousFeatureItem
        )

        feature_items = [
            DiscreteFeatureItem(
                name_item, attr.values, point, scale_x, name_offset, -scale_x * min_p
            )
            if attr.is_discrete
            else cont_feature_item_class(
                name_item,
                self.log_reg_cont_data_extremes[i][cls_index],
                self.get_ruler_values(
                    point.min(), point.max(), scale_x * point.ptp(), False

if self.align == OWNomogram.ALIGN_LEFT:
            max_sum = max_sum - sum(minimums)
            min_sum = min_sum - sum(minimums)
            for i in range(len(k)):
                k[i] = k[i] - sum([min(q) for q in [p[i] for p in self.points]])
        if self.scale == OWNomogram.POINT_SCALE:
            min_sum *= d
            max_sum *= d
            d_ = d

        values = self.get_ruler_values(min_sum, max_sum, max_width)
        min_sum, max_sum = min(values), max(values)
        diff_ = np.nan_to_num(max_sum - min_sum)
        scale_x = max_width / diff_ if diff_ else max_width
        cls_var, cls_index = self.domain.class_var, self.target_class_index
        nomogram_footer = NomogramItem()

        def get_normalized_probabilities(val):
            if not self.normalize_probabilities:
                return 1 / (1 + np.exp(k[cls_index] - val / d_))
            totals = self.__get_totals_for_class_values(minimums)
            p_sum = np.sum(1 / (1 + np.exp(k - totals / d_)))
            return 1 / (1 + np.exp(k[cls_index] - val / d_)) / p_sum

        def get_points(prob):
            if not self.normalize_probabilities:
                return (k[cls_index] - np.log(1 / prob - 1)) * d_
            totals = self.__get_totals_for_class_values(minimums)
            p_sum = np.sum(1 / (1 + np.exp(k - totals / d_)))
            return (k[cls_index] - np.log(1 / (prob * p_sum) - 1)) * d_

        probs_item = ProbabilitiesRulerItem(

attr_inds,
        name_items,
        points,
        max_width,
        point_text,
        name_offset,
    ):
        cls_index = self.target_class_index
        min_p = min(p.min() for p in points)
        max_p = max(p.max() for p in points)
        values = self.get_ruler_values(min_p, max_p, max_width)
        min_p, max_p = min(values), max(values)
        diff_ = np.nan_to_num(max_p - min_p)
        scale_x = max_width / diff_ if diff_ else max_width

        nomogram_header = NomogramItem()
        point_item = RulerItem(
            point_text, values, scale_x, name_offset, -scale_x * min_p
        )
        point_item.setPreferredSize(point_item.preferredWidth(), 35)
        nomogram_header.add_items([point_item])

        self.nomogram_main = NomogramItem()
        cont_feature_item_class = (
            ContinuousFeature2DItem
            if self.cont_feature_dim_index
            else ContinuousFeatureItem
        )

        feature_items = [
            DiscreteFeatureItem(
                name_item, attr.values, point, scale_x, name_offset, -scale_x * min_p