def std(
        self,
        inputs_loader: InputsLoader,
        variances: Optional[jnp.ndarray] = None,
        distribute: bool = True,
    ) -> jnp.ndarray:
        r"""
        Estimate the predictive standard deviation of the target variable, that is

        .. math::
            \text{Var}_{Y|x, D}[Y],

        where:
         - :math:`x` is an observed input variable;
         - :math:`Y` is a random target variable;
         - :math:`\mathcal{D}` is the observed training data set.

        Parameters
        ----------
        inputs_loader : InputsLoader
            A loader of input data points.
        variances: Optional[jnp.ndarray]
            An estimate of the predictive variance.passed, this will be taken from the attributes of this class.
        distribute: bool
            Whether to distribute computation over multiple devices, if available.

        Returns
        -------
        jnp.ndarray
            An estimate of the predictive standard deviation for each input.
        """
        if variances is None:
            variances = self.variance(
                inputs_loader=inputs_loader,
                distribute=distribute,
            )
        return jnp.sqrt(variances)

        Estimate the predictive standard deviation of the target variable, that is

        .. math::
            \text{Var}_{Y|x, D}[Y],

        where:
         - :math:`x` is an observed input variable;
         - :math:`Y` is a random target variable;
         - :math:`\mathcal{D}` is the observed training data set.

        Parameters
        ----------
        inputs_loader : InputsLoader
            A loader of input data points.
        variances: Optional[jnp.ndarray]
            An estimate of the predictive variance.passed, this will be taken from the attributes of this class.
        distribute: bool
            Whether to distribute computation over multiple devices, if available.

        Returns
        -------
        jnp.ndarray
            An estimate of the predictive standard deviation for each input.
        
fortuna/calib_model/predictive/base.py