

def test_parallel_train():
    rng = check_random_state(12321)
    (n_samples, n_features) = (80, 30)
    X_train = rng.randn(n_samples, n_features)
    y_train = rng.randint(0, 2, n_samples)
    clfs = [RandomForestClassifier(n_estimators=20, n_jobs=n_jobs, random_state=12345).fit(X_train, probas) for n_jobs in [1, 2, 3, 8, 16, 32]]
    X_test = rng.randn(n_samples, n_features)
    probas = [clf.predict_proba(X_test) for clf in clfs]
    for (proba1, proba2) in zip(probas, probas[1:]):
        assert_array_almost_equal(proba1, proba2)

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_forest.py test_parallel_train/VarMisuse@74/99 `y_train`->`probas`