def check_class_weight_balanced_and_bootstrap_multi_output(name):
    # Test class_weight works for multi-output"""
    ForestClassifier = FOREST_CLASSIFIERS[name]
    _y = np.vstack((y, np.array(y) - 2)).T
    clf = ForestClassifier(class_weight='balanced', random_state=0)
    clf.fit(X, _y)
    clf = ForestClassifier(class_weight=[{-1: 0.5, 1: 1.}, {-2: 1., 2: 1.}],
                           random_state=0)
    clf.fit(X, _y)
    # smoke test for subsample and balanced subsample
    clf = ForestClassifier(class_weight='balanced_subsample', random_state=0)
    clf.fit(X, _y)
    clf = ForestClassifier(class_weight='subsample', random_state=0)
    ignore_warnings(clf.fit)(X, _y)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py/check_class_weight_balanced_and_bootstrap_multi_output/operator `*` -> `-`