def test_lagged_training_data_sequence_inputs(self, stride):
        """
        Tests that `create_lagged_training_data` correctly handles being
        passed a sequence of `TimeSeries` inputs, as opposed to individual
        `TimeSeries`.
        """
        # Define two simple tabularization problems:
        target_1 = past_1 = future_1 = linear_timeseries(start=0, end=5)
        target_2 = past_2 = future_2 = linear_timeseries(start=6, end=11)
        ts_tg = (target_1, target_2)
        ts_pc = (past_1, past_2)
        ts_fc = (future_1, future_2)
        lags = lags_past = lags_future = [-1]
        output_chunk_length = 1
        # Expected solution:
        expected_X_1 = np.concatenate(
            3 * [target_1.all_values(copy=False)[:-1, :, :]], axis=1
        )
        expected_X_2 = np.concatenate(
            3 * [target_2.all_values(copy=False)[:-1, :, :]], axis=1
        )
        expected_X = np.concatenate(
            [expected_X_1[::-stride][::-1], expected_X_2[::-stride][::-1]], axis=0
        )
        expected_y_1 = target_1.all_values(copy=False)[1:][::-stride][::-1]
        expected_y_2 = target_2.all_values(copy=False)[1:][::-stride][::-1]
        expected_y = np.concatenate([expected_y_1, expected_y_2], axis=0)
        expected_times_1 = target_1.time_index[1:][::-stride][::-1]
        expected_times_2 = target_2.time_index[1:][::-stride][::-1]

        kwargs = {
            "expected_X": expected_X,
            "expected_y": expected_y,
            "expected_times_x": [expected_times_1, expected_times_2],
            "expected_times_y": [expected_times_1, expected_times_2],
            "target": ts_tg,
            "past_cov": ts_pc,
            "future_cov": ts_fc,
            "lags": lags,
            "lags_past": lags_past,
            "lags_future": lags_future,
            "output_chunk_length": output_chunk_length,
            "output_chunk_shift": 0,
            "use_static_covariates": False,
            "multi_models": True,
            "max_samples_per_ts": None,
            "use_moving_windows": True,
            "stride": stride,
        }

        # concatenate=True
        self.helper_check_lagged_data(
            convert_lags_to_dict=False, concatenate=True, **kwargs
        )
        self.helper_check_lagged_data(
            convert_lags_to_dict=True, concatenate=True, **kwargs
        )

        # concatenate=False
        self.helper_check_lagged_data(
            convert_lags_to_dict=False, concatenate=False, **kwargs
        )
        self.helper_check_lagged_data(
            convert_lags_to_dict=True, concatenate=False, **kwargs
        )

        Tests that `create_lagged_training_data` correctly handles being
        passed a sequence of `TimeSeries` inputs, as opposed to individual
        `TimeSeries`.
        
darts/tests/utils/tabularization/test_create_lagged_training_data.py