def std(
        self,
        outputs: jnp.ndarray,
        variances: Optional[jnp.ndarray] = None,
        calibrated: bool = True,
    ) -> jnp.ndarray:
        """
        Estimate the standard deviation of the one-hot encoded target variable given the output, with respect to the
        predictive distribution.

        Parameters
        ----------
        outputs : jnp.ndarray
            Model outputs.
        variances: Optional[jnp.ndarray]
            Variance for each output.
        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 standard deviation for each output.
        """
        return super().std(outputs, variances, calibrated)

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

        Parameters
        ----------
        outputs : jnp.ndarray
            Model outputs.
        variances: Optional[jnp.ndarray]
            Variance for each output.
        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 standard deviation for each output.
        
fortuna/output_calib_model/predictive/classification.py