code: def test_rej_opt_clf_errors():
      y_proba = np.array([[0.5, 0.5], [0.3, 0.7], [0.55, 0.45]])
      # y_true = pd.Series([0, 0, 0], index=[1, 1, 0]).rename_axis(index='prot')
      y_true = pd.DataFrame(
          [[0, 1], [0, 1], [0, 0]], columns=["feat", "prot"]
      )
      y_true = y_true.set_index("prot", append=True).squeeze()
      roc = RejectOptionClassifier("prot")
      with pytest.raises(ValueError):  # binary classification
          roc.fit(y_proba, y_true)
      with pytest.raises(TypeError):  # missing prot attr
          roc.fit(y_proba, y_true, labels=[0, 1]).predict(y_proba)
      y_proba = pd.DataFrame(y_proba, index=y_true.index)
      y_postpred = roc.fit_predict(y_proba, labels=[0, 1])
      assert (y_postpred == [0, 1, 1]).all()
  
      cv = RejectOptionClassifierCV(
          "prot", scoring="statistical_parity", step=0.1, cv=3
      )
      with pytest.raises(
          RuntimeWarning
      ):  # sample_weight ignored warning
          cv.fit(y_proba, y_true, sample_weight=[10, 1, 1])
  
file_path: AIF360/tests/sklearn/test_reject_option_classification.py
imports: from itertools import product
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import make_scorer
  from aif360.algorithms.postprocessing import RejectOptionClassification
  from aif360.sklearn.postprocessing import RejectOptionClassifier, RejectOptionClassifierCV, PostProcessingMeta
  from aif360.sklearn.metrics import generalized_entropy_error
label_desc: Use image node_id 7 for calling a global function with example usage: test_rej_opt_clf_errors() without return types