def test_mixed_covariates_and_accuracy(self):
        """Performs tests usingpast and future covariates for a multivariate prediction of a
        sine wave together with a repeating linear curve. Both curves have the seasonal length.
        """
        season_length = 24
        n_repeat = 30
        (
            ts,
            ts_train,
            ts_val,
            covariates,
        ) = self.helper_generate_multivariate_case_data(season_length, n_repeat)

        kwargs_TFT_full_coverage = {
            "input_chunk_length": 12,
            "output_chunk_length": 12,
            "n_epochs": 10,
            "lstm_layers": 2,
            "hidden_size": 32,
            "likelihood": QuantileRegression(quantiles=[0.1, 0.5, 0.9]),
            "random_state": 42,
            "add_encoders": {"cyclic": {"future": "hour"}},
        }
        kwargs_TFT_full_coverage = dict(kwargs_TFT_full_coverage, **tfm_kwargs)

        self.helper_test_prediction_accuracy(
            season_length,
            ts,
            ts_train,
            ts_val,
            past_covariates=covariates,
            future_covariates=covariates,
            kwargs_tft=kwargs_TFT_full_coverage,
        )
Performs tests usingpast and future covariates for a multivariate prediction of a
        sine wave together with a repeating linear curve. Both curves have the seasonal length.
        
darts/tests/models/forecasting/test_TFT.py