code: def test_ax(self):
      np.random.seed(0)
      clf = RandomForestClassifier()
      clf.fit(self.X, self.y)
      fig, ax = plt.subplots(1, 1)
      out_ax = plot_feature_importances(clf)
      assert ax is not out_ax
      out_ax = plot_feature_importances(clf, ax=ax)
      assert ax is out_ax
  
imports: from __future__ import absolute_import
  import unittest
  from sklearn.datasets import load_iris
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import numpy
  import matplotlib.pyplot
  from scikitplot.estimators import plot_feature_importances
  from scikitplot.estimators import plot_learning_curve
label_desc: Use image node_id 8 for calling the TestFeatureImportances obj's underlying member method code with example usage: obj.test_ax() without return types