def test_lagged_prediction_data_sequence_inputs(self):
        """
        Tests that `create_lagged_prediction_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)
        lags = lags_past = lags_future = [-1]
        # Expected solution:
        expected_X_1 = np.concatenate(3 * [target_1.all_values(copy=False)], axis=1)
        expected_X_2 = np.concatenate(3 * [target_2.all_values(copy=False)], axis=1)
        expected_X = np.concatenate([expected_X_1, expected_X_2], axis=0)
        # Need to account for fact that we can create feature for last time in series:
        expected_times_1 = target_1.append_values([0]).time_index[1:]
        expected_times_2 = target_2.append_values([0]).time_index[1:]
        # Check when `concatenate = True`:
        X, times = create_lagged_prediction_data(
            target_series=(target_1, target_2),
            past_covariates=(past_1, past_2),
            future_covariates=(future_1, future_2),
            lags=lags,
            lags_past_covariates=lags_past,
            lags_future_covariates=lags_future,
            uses_static_covariates=False,
        )
        assert np.allclose(X, expected_X)
        assert len(times) == 2
        assert times[0].equals(expected_times_1)
        assert times[1].equals(expected_times_2)
        # Check when `concatenate = False`:
        X, times = create_lagged_prediction_data(
            target_series=(target_1, target_2),
            past_covariates=(past_1, past_2),
            future_covariates=(future_1, future_2),
            lags=lags,
            lags_past_covariates=lags_past,
            lags_future_covariates=lags_future,
            uses_static_covariates=False,
            concatenate=False,
        )
        assert len(X) == 2
        assert np.allclose(X[0], expected_X_1)
        assert np.allclose(X[1], expected_X_2)
        assert len(times) == 2
        assert times[0].equals(expected_times_1)
        assert times[1].equals(expected_times_2)

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