def aleatoric_variance(
        self,
        inputs_loader: InputsLoader,
        n_posterior_samples: int = 30,
        rng: Optional[jax.Array] = None,
        distribute: bool = True,
    ) -> jnp.ndarray:
        r"""
        Estimate the predictive aleatoric variance of the one-hot encoded target variable, that is

        .. math::
            \text{Var}_{W|\mathcal{D}}[\mathbb{E}_{\tilde{Y}|W, x}[\tilde{Y}]],

        where:
         - :math:`x` is an observed input variable;
         - :math:`\tilde{Y}` is a one-hot encoded 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.
        n_posterior_samples : int
            Number of samples to draw from the posterior distribution for each input.
        rng : Optional[jax.Array]
            A random number generator. If not 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 aleatoric variance for each input.
        """
        return super().aleatoric_variance(
            inputs_loader, n_posterior_samples, rng, distribute
        )

        Estimate the predictive aleatoric variance of the one-hot encoded target variable, that is

        .. math::
            \text{Var}_{W|\mathcal{D}}[\mathbb{E}_{\tilde{Y}|W, x}[\tilde{Y}]],

        where:
         - :math:`x` is an observed input variable;
         - :math:`\tilde{Y}` is a one-hot encoded 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.
        n_posterior_samples : int
            Number of samples to draw from the posterior distribution for each input.
        rng : Optional[jax.Array]
            A random number generator. If not 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 aleatoric variance for each input.
        
fortuna/prob_model/predictive/classification.py