code: def test_ax_lift_curve():
      clf = LogisticRegression()
      clf.fit(X, y)
      probas = clf.predict_proba(X)
      fig, ax = plt.subplots(1, 1)
      out_ax = lift_curve(y, probas)
      assert ax is not out_ax
      out_ax = lift_curve(y, probas, ax=ax)
      assert ax is out_ax
  
file_path: sklearn-evaluation/tests/test_cumulative_gain_lift_curve.py
imports: import pytest
  import matplotlib.pyplot
  from matplotlib.testing.decorators import image_comparison
  from sklearn.linear_model import LogisticRegression
  from sklearn.datasets import load_breast_cancer, load_iris
  from sklearn_evaluation.plot import cumulative_gain, lift_curve
label_desc: Use image node_id 8 for calling a global function with example usage: test_ax_lift_curve() without return types