def METHOD_NAME():
    data = pd.DataFrame(np.random.rand(5, 1), columns=["0"])
    ones = np.ones(len(data))
    constant_model = SKLearnModel(
        model=DummyClassifier(strategy="constant", constant=1).fit(data, np.ones(len(data))),
        model_type=SupportedModelTypes.CLASSIFICATION,
    )
    ds = Dataset(df=data)
    with pytest.warns():
        validate_deterministic_model(constant_model, ds, ModelPredictionResults(raw=ones * 0.5))
    # Make sure there's no warning in other cases
    with warnings.catch_warnings():
        warnings.simplefilter("error")
        validate_deterministic_model(constant_model, ds, constant_model.predict(ds))
        validate_deterministic_model(constant_model, ds, ModelPredictionResults(raw=ones * 0.99999))