def METHOD_NAME():
    """Test results of MultiplexForecaster.
    Because MultiplexForecaster is in many ways a wrapper for an underlying
    forecaster - we can confirm that if the selected_forecaster is set that the
    MultiplexForecaster performs as expected.
    """
    from numpy.testing import assert_array_equal
    y = load_shampoo_sales()
    # Note - we select two forecasters which are deterministic.
    forecaster_tuples = [
        ("naive", NaiveForecaster()),
        ("theta", ThetaForecaster()),
    ]
    forecaster_names = [name for name, _ in forecaster_tuples]
    forecasters = [forecaster for _, forecaster in forecaster_tuples]
    multiplex_forecaster = MultiplexForecaster(forecasters=forecaster_tuples)
    fh_test = [1, 2, 3]
    # for each of the forecasters - check that the wrapped forecaster predictions
    # agree with the unwrapped forecaster predictions!
    for ind, name in enumerate(forecaster_names):
        # make a copy to ensure we don't reference the same objectL
        test_forecaster = forecasters[ind].clone()
        test_forecaster.fit(y)
        multiplex_forecaster.selected_forecaster = name
        # Note- MultiplexForecaster will make a copy of the forecaster before fitting.
        multiplex_forecaster.fit(y)
        y_pred_indiv = test_forecaster.predict(fh=fh_test)
        y_pred_multi = multiplex_forecaster.predict(fh=fh_test)
        assert_array_equal(y_pred_indiv, y_pred_multi)