def test_lagged_prediction_data_zero_lags(self, config):
        """
        Tests that `create_lagged_prediction_data` correctly handles case when
        `0` is included in `lags_future_covariates` (i.e. when we're using the values
        `future_covariates` at time `t` to predict the value of `target_series` at
        that same time point).
        """
        # Define `future` so that only value occurs at the same time as
        # the only possible label that can be extracted from `target_series`; the
        # only possible feature that can be created using these series utilises
        # the value of `future` at the same time as the label (i.e. a lag
        # of `0` away from the only feature time):
        series_type, use_moving_windows = config
        if series_type == "integer":
            target = linear_timeseries(start=0, length=1, start_value=0, end_value=1)
            future = linear_timeseries(start=1, length=1, start_value=1, end_value=2)
        else:
            target = linear_timeseries(
                start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1
            )
            future = linear_timeseries(
                start=pd.Timestamp("1/2/2000"), length=1, start_value=1, end_value=2
            )
        # X comprises of first value of `target` (i.e. 0) and only value in `future`:
        expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1)
        # Check correctness for 'moving windows' and 'time intersection' methods, as
        # well as for different `multi_models` values:
        X, times = create_lagged_prediction_data(
            target,
            future_covariates=future,
            lags=[-1],
            lags_future_covariates=[0],
            uses_static_covariates=False,
            use_moving_windows=use_moving_windows,
        )
        assert np.allclose(expected_X, X)
        assert len(times[0]) == 1
        assert times[0][0] == future.start_time()

        Tests that `create_lagged_prediction_data` correctly handles case when
        `0` is included in `lags_future_covariates` (i.e. when we're using the values
        `future_covariates` at time `t` to predict the value of `target_series` at
        that same time point).
        
darts/tests/utils/tabularization/test_create_lagged_prediction_data.py