How to use the lightgbm.create_tree_digraph function in lightgbm

def test_create_tree_digraph(self):
        constraints = [-1, 1] * int(self.X_train.shape[1] / 2)
        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, monotone_constraints=constraints)
        gbm.fit(self.X_train, self.y_train, verbose=False)

        self.assertRaises(IndexError, lgb.create_tree_digraph, gbm, tree_index=83)

        graph = lgb.create_tree_digraph(gbm, tree_index=3,
                                        show_info=['split_gain', 'internal_value', 'internal_weight'],
                                        name='Tree4', node_attr={'color': 'red'})
        graph.render(view=False)
        self.assertIsInstance(graph, graphviz.Digraph)
        self.assertEqual(graph.name, 'Tree4')
        self.assertEqual(graph.filename, 'Tree4.gv')
        self.assertEqual(len(graph.node_attr), 1)
        self.assertEqual(graph.node_attr['color'], 'red')
        self.assertEqual(len(graph.graph_attr), 0)
        self.assertEqual(len(graph.edge_attr), 0)
        graph_body = ''.join(graph.body)
        self.assertIn('leaf', graph_body)
        self.assertIn('gain', graph_body)
        self.assertIn('value', graph_body)

def test_create_tree_digraph(self):
        constraints = [-1, 1] * int(self.X_train.shape[1] / 2)
        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, monotone_constraints=constraints)
        gbm.fit(self.X_train, self.y_train, verbose=False)

        self.assertRaises(IndexError, lgb.create_tree_digraph, gbm, tree_index=83)

        graph = lgb.create_tree_digraph(gbm, tree_index=3,
                                        show_info=['split_gain', 'internal_value', 'internal_weight'],
                                        name='Tree4', node_attr={'color': 'red'})
        graph.render(view=False)
        self.assertIsInstance(graph, graphviz.Digraph)
        self.assertEqual(graph.name, 'Tree4')
        self.assertEqual(graph.filename, 'Tree4.gv')
        self.assertEqual(len(graph.node_attr), 1)
        self.assertEqual(graph.node_attr['color'], 'red')
        self.assertEqual(len(graph.graph_attr), 0)
        self.assertEqual(len(graph.edge_attr), 0)
        graph_body = ''.join(graph.body)
        self.assertIn('leaf', graph_body)
        self.assertIn('gain', graph_body)
        self.assertIn('value', graph_body)
        self.assertIn('weight', graph_body)
        self.assertIn('#ffdddd', graph_body)

plt.show()

print('Plotting feature importances...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()

print('Plotting split value histogram...')
ax = lgb.plot_split_value_histogram(gbm, feature='f26', bins='auto')
plt.show()

print('Plotting 54th tree...')  # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=['split_gain'])
plt.show()

print('Plotting 54th tree with graphviz...')
graph = lgb.create_tree_digraph(gbm, tree_index=53, name='Tree54')
graph.render(view=True)

if node.value is None:  # not a leaf node
                add_grower_node(node.left_child, name, decision='<=')
                add_grower_node(node.right_child, name, decision='>')

            if parent is not None:
                graph.edge(parent, name, decision)

        if isinstance(est_or_grower, BaseGradientBoostingMachine):
            add_predictor_node(0)
        elif isinstance(est_or_grower, pygbm.grower.TreeGrower):
            add_grower_node(est_or_grower.root)

    # make lightgbm tree
    if est_lightgbm is not None:
        import lightgbm as lb
        graph = lb.create_tree_digraph(
            est_lightgbm,
            tree_index=tree_index,
            show_info=['split_gain', 'internal_value', 'internal_count',
                       'leaf_count'],
            **kwargs)
    else:
        graph = Digraph(**kwargs)

    # make pygbm tree
    make_pygbm_tree()

    graph.render(view=view)