def test_scaled_metrics(self, config):
        """Tests backtest for scaled metrics based on historical forecasts generated on a sequence
        `series` with last_points_only=False"""
        metric, m = config
        y = lt(length=20)
        hfc = lt(length=10, start=y.start_time() + 10 * y.freq)
        y = [y, y]
        hfc = [[hfc, hfc], [hfc]]

        model = NaiveDrift()
        bts = model.backtest(
            series=y,
            historical_forecasts=hfc,
            metric=metric,
            last_points_only=False,
            reduction=None,
            metric_kwargs={"m": m},
        )
        assert isinstance(bts, list) and len(bts) == 2

        bt_expected = metric(y[0], hfc[0][0], insample=y[0], m=m)
        for bt_list in bts:
            for bt in bt_list:
                np.testing.assert_array_almost_equal(bt, bt_expected)
Tests backtest for scaled metrics based on historical forecasts generated on a sequence
        `series` with last_points_only=False
darts/tests/models/forecasting/test_backtesting.py