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

        The encoder_weights are calculated for the past inputs of the model.
        The encoder_importance contains the weights of the encoder variable selection network.
        The encoder 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 encoder variable importance.
        """
        return self._get_importance(
            weight=self.model.model._encoder_sparse_weights,
            names=self.model.model.encoder_variables,
        )
Returns the encoder variable importance of the TFT model.

        The encoder_weights are calculated for the past inputs of the model.
        The encoder_importance contains the weights of the encoder variable selection network.
        The encoder 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 encoder variable importance.
        
darts/explainability/tft_explainer.py