def mean(
        self,
        inputs_loader: InputsLoader,
        distribute: bool = True,
    ) -> jnp.ndarray:
        r"""
        Estimate the predictive mean of the target variable, that is

        .. math::
            \mathbb{E}_{Y|x, \mathcal{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;
         - :math:`W` denotes the random model parameters.

        Parameters
        ----------
        inputs_loader : InputsLoader
            A loader of input data points.
        distribute: bool
            Whether to distribute computation over multiple devices, if available.

        Returns
        -------
        jnp.ndarray
            An estimate of the predictive mean for each input.
        """
        state = self.state.get()
        return self.likelihood.mean(
            params=state.params,
            inputs_loader=inputs_loader,
            mutable=state.mutable,
            distribute=distribute,
        )

        Estimate the predictive mean of the target variable, that is

        .. math::
            \mathbb{E}_{Y|x, \mathcal{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;
         - :math:`W` denotes the random model parameters.

        Parameters
        ----------
        inputs_loader : InputsLoader
            A loader of input data points.
        distribute: bool
            Whether to distribute computation over multiple devices, if available.

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