def METHOD_NAME(self):
    scaler = Scaler(MinMaxScaler())
    # shape = (10, 3, 2)
    vals = np.array([np.arange(6).reshape(3, 2)] * 10)
    # scalers should only consider True columns
    component_mask = np.array([True, False, True])
    s = TimeSeries.from_values(vals)
    ss = scaler.fit_transform(s, component_mask=component_mask)
    ss_vals = ss.all_values(copy=False)
    # test non-masked columns
    assert (ss_vals[:, 1, :] == vals[:, 1, :]).all()
    # test masked columns
    assert round(abs(ss_vals[:, [0, 2], :].max() - 1.0), 7) == 0
    assert round(abs(ss_vals[:, [0, 2], :].min() - 0.0), 7) == 0
    ssi = scaler.inverse_transform(ss, component_mask=component_mask)
    # Test inverse transform
    np.testing.assert_allclose(s.all_values(), ssi.all_values())