def test_lagged_training_data_stochastic_series(self, stride):
        """
        Tests that `create_lagged_training_data` is correctly vectorised
        over the sample axes of the input `TimeSeries`.
        """
        # Define two simple tabularization problems:
        target_1 = past_1 = future_1 = linear_timeseries(start=0, end=5)
        target_2 = past_2 = future_2 = 2 * target_1
        target = target_1.concatenate(target_2, axis=2)
        past = past_1.concatenate(past_2, axis=2)
        future = future_1.concatenate(future_2, axis=2)
        lags = lags_past = lags_future = [-1]
        output_chunk_length = 1
        # Expected solution:
        expected_X = np.concatenate(
            3 * [target.all_values(copy=False)[:-1][::-stride][::-1]], axis=1
        )
        expected_y = target.all_values(copy=False)[1:][::-stride][::-1]
        expected_times = target.time_index[1:][::-stride][::-1]

        kwargs = {
            "expected_X": expected_X,
            "expected_y": expected_y,
            "expected_times_x": expected_times,
            "expected_times_y": expected_times,
            "target": target,
            "past_cov": past,
            "future_cov": future,
            "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,
        }

        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
        )

        Tests that `create_lagged_training_data` is correctly vectorised
        over the sample axes of the input `TimeSeries`.
        
darts/tests/utils/tabularization/test_create_lagged_training_data.py