def test_prediction_shape(self):
        """checks whether prediction has same number of variable as input series and
        whether prediction has correct length.
        Test cases:
            -   univariate
            -   multivariate
            -   multi-TS
        """
        season_length = 1
        n_repeat = 20

        # data comes as multivariate
        (
            ts,
            ts_train,
            ts_val,
            covariates,
        ) = self.helper_generate_multivariate_case_data(season_length, n_repeat)

        kwargs_TFT_quick_test = {
            "input_chunk_length": 1,
            "output_chunk_length": 1,
            "n_epochs": 1,
            "lstm_layers": 1,
            "hidden_size": 8,
            "loss_fn": MSELoss(),
            "random_state": 42,
        }
        kwargs_TFT_quick_test = dict(kwargs_TFT_quick_test, **tfm_kwargs)

        # univariate
        first_var = ts.columns[0]
        self.helper_test_prediction_shape(
            season_length,
            ts[first_var],
            ts_train[first_var],
            ts_val[first_var],
            future_covariates=covariates,
            kwargs_tft=kwargs_TFT_quick_test,
        )
        # univariate and short prediction length
        self.helper_test_prediction_shape(
            2,
            ts[first_var],
            ts_train[first_var],
            ts_val[first_var],
            future_covariates=covariates,
            kwargs_tft=kwargs_TFT_quick_test,
        )
        # multivariate
        self.helper_test_prediction_shape(
            season_length,
            ts,
            ts_train,
            ts_val,
            future_covariates=covariates,
            kwargs_tft=kwargs_TFT_quick_test,
        )
        # multi-TS
        kwargs_TFT_quick_test["add_encoders"] = {"cyclic": {"future": "hour"}}
        second_var = ts.columns[-1]
        self.helper_test_prediction_shape(
            season_length,
            [ts[first_var], ts[second_var]],
            [ts_train[first_var], ts_train[second_var]],
            [ts_val[first_var], ts_val[second_var]],
            future_covariates=None,
            kwargs_tft=kwargs_TFT_quick_test,
        )
checks whether prediction has same number of variable as input series and
        whether prediction has correct length.
        Test cases:
            -   univariate
            -   multivariate
            -   multi-TS
        
darts/tests/models/forecasting/test_TFT.py