def _static_covariates_importance(self) -> pd.DataFrame:
        """Returns the static covariates importance of the TFT model.

        The static covariate importances are calculated for the static inputs of the model (numeric and / or
        categorical). The static variable selection network is used to select the most important static covariates.
        It provides insights which variable are most significant for the prediction problem.
        See section 4.2, and 4.3 of the paper for more details.

        Returns
        -------
        pd.DataFrame
            The static covariates importance.
        """
        return self._get_importance(
            weight=self.model.model._static_covariate_var,
            names=self.model.model.static_variables,
        )
Returns the static covariates importance of the TFT model.

        The static covariate importances are calculated for the static inputs of the model (numeric and / or
        categorical). The static variable selection network is used to select the most important static covariates.
        It provides insights which variable are most significant for the prediction problem.
        See section 4.2, and 4.3 of the paper for more details.

        Returns
        -------
        pd.DataFrame
            The static covariates importance.
        
darts/explainability/tft_explainer.py