def variance(
        self, outputs: jnp.ndarray, calibrated: bool = True, **kwargs
    ) -> jnp.ndarray:
        """
        Estimate the variance of the one-hot encoded target variable given the output, with respect to the predictive
        distribution.

        Parameters
        ----------
        outputs : jnp.ndarray
            Model outputs.
        calibrated : bool
            Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the
            model must have been calibrated beforehand.

        Returns
        -------
        jnp.ndarray
            The estimated variance for each output.
        """
        return super().variance(outputs, calibrated, **kwargs)

        Estimate the variance of the one-hot encoded target variable given the output, with respect to the predictive
        distribution.

        Parameters
        ----------
        outputs : jnp.ndarray
            Model outputs.
        calibrated : bool
            Whether the outputs should be calibrated when computing this method. If `calibrated` is set to True, the
            model must have been calibrated beforehand.

        Returns
        -------
        jnp.ndarray
            The estimated variance for each output.
        
fortuna/output_calib_model/predictive/classification.py