def _decoder_importance(self) -> pd.DataFrame:
        """Returns the decoder variable importance of the TFT model.

        The decoder_weights are calculated for the known future inputs of the model.
        The decoder_importance contains the weights of the decoder variable selection network.
        The decoder variable selection network is used to select the most important static and time dependent
        covariates. It provides insights which variable are most significant for the prediction problem.
        See section 4.2 of the paper for more details.

        Returns
        -------
        pd.DataFrame
            The importance of the decoder variables.
        """
        return self._get_importance(
            weight=self.model.model._decoder_sparse_weights,
            names=self.model.model.decoder_variables,
        )
Returns the decoder variable importance of the TFT model.

        The decoder_weights are calculated for the known future inputs of the model.
        The decoder_importance contains the weights of the decoder variable selection network.
        The decoder variable selection network is used to select the most important static and time dependent
        covariates. It provides insights which variable are most significant for the prediction problem.
        See section 4.2 of the paper for more details.

        Returns
        -------
        pd.DataFrame
            The importance of the decoder variables.
        
darts/explainability/tft_explainer.py