def METHOD_NAME():
    """Test on random example"""
    n_samples = 100
    n_features = 10
    n_tasks = 10
    X = np.random.randn(n_samples, n_features)
    y = np.random.randn(n_samples, n_tasks)
    dataset = dc.data.NumpyDataset(X, y)
    minmax_transformer = dc.trans.MinMaxTransformer(transform_y=True,
                                                    dataset=dataset)
    w, ids = dataset.w, dataset.ids
    dataset = minmax_transformer.transform(dataset)
    X_t, y_t, w_t, ids_t = (dataset.X, dataset.y, dataset.w, dataset.ids)
    # Check ids are unchanged before and after transformation
    for id_elt, id_t_elt in zip(ids, ids_t):
        assert id_elt == id_t_elt
    # Check X is unchanged since transform_y is true
    np.testing.assert_allclose(X, X_t)
    # Check w is unchanged since transform_y is true
    np.testing.assert_allclose(w, w_t)
    # Check minimum and maximum values of transformed y are 0 and 1
    np.testing.assert_allclose(y_t.min(), 0.)
    np.testing.assert_allclose(y_t.max(), 1.)
    # Test if dimensionality expansion is handled correctly by untransform
    y_t = np.expand_dims(y_t, axis=-1)
    y_restored = minmax_transformer.untransform(y_t)
    assert y_restored.shape == y.shape + (1,)
    np.testing.assert_allclose(np.squeeze(y_restored, axis=-1), y)