How to use the lightgbm.plot_tree function in lightgbm

def test_plot_tree(self):
        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
        gbm.fit(self.X_train, self.y_train, verbose=False)

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

        ax = lgb.plot_tree(gbm, tree_index=3, figsize=(15, 8), show_info=['split_gain'])
        self.assertIsInstance(ax, matplotlib.axes.Axes)
        w, h = ax.axes.get_figure().get_size_inches()
        self.assertEqual(int(w), 15)
        self.assertEqual(int(h), 8)

def test_plot_tree(self):
        gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
        gbm.fit(self.X_train, self.y_train, verbose=False)

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

        ax = lgb.plot_tree(gbm, tree_index=3, figsize=(15, 8), show_info=['split_gain'])
        self.assertIsInstance(ax, matplotlib.axes.Axes)
        w, h = ax.axes.get_figure().get_size_inches()
        self.assertEqual(int(w), 15)
        self.assertEqual(int(h), 8)

out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,
                )
            )

            display(SVG(graph.pipe(format="svg")))

        elif isinstance(self.model, xgb.XGBModel):
            return xgb.plot_tree(self.model)

        elif isinstance(self.model, lgb.sklearn.LGBMModel):
            return lgb.plot_tree(self.model)

        elif isinstance(self.model, cb.CatBoost):
            return self.model.plot_tree(tree_idx=tree_num, pool=self.pool)

        elif isinstance(self.model, sklearn.ensemble.BaseEnsemble):
            estimator = self.model.estimators_[tree_num]

            graph = Source(
                sklearn.tree.export_graphviz(
                    estimator,
                    out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,

out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,
                )
            )

            display(SVG(graph.pipe(format="svg")))

        elif isinstance(self.model, xgb.XGBModel):
            return xgb.plot_tree(self.model)

        elif isinstance(self.model, lgb.sklearn.LGBMModel):
            return lgb.plot_tree(self.model)

        elif isinstance(self.model, cb.CatBoost):
            return self.model.plot_tree(tree_idx=tree_num, pool=self.pool)

        elif isinstance(self.model, sklearn.ensemble.BaseEnsemble):
            estimator = self.model.estimators_[tree_num]

            graph = Source(
                sklearn.tree.export_graphviz(
                    estimator,
                    out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,

out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,
                )
            )

            display(SVG(graph.pipe(format="svg")))

        elif isinstance(self.model, xgb.XGBModel):
            return xgb.plot_tree(self.model)

        elif isinstance(self.model, lgb.sklearn.LGBMModel):
            return lgb.plot_tree(self.model)

        elif isinstance(self.model, sklearn.ensemble.BaseEnsemble):
            estimator = self.model.estimators_[tree_num]

            graph = Source(
                sklearn.tree.export_graphviz(
                    estimator,
                    out_file=None,
                    feature_names=self.features,
                    class_names=classes,
                    rounded=True,
                    precision=True,
                    filled=True,
                )
            )

verbose_eval=10)

print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1')
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)