def quantile(
        self,
        q: Union[float, Array, List],
        inputs_loader: InputsLoader,
        n_target_samples: Optional[int] = 30,
        rng: Optional[jax.Array] = None,
        distribute: bool = True,
    ) -> Union[float, jnp.ndarray]:
        r"""
        Estimate the `q`-th quantiles of the predictive probability density function.

        Parameters
        ----------
        q : Union[float, Array, List]
            Quantile or sequence of quantiles to compute. Each of these must be between 0 and 1, extremes included.
        inputs_loader : InputsLoader
            A loader of input data points.
        n_target_samples : int
            Number of target samples to sample for each input data point.
        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
            Quantile estimate for each quantile and each input. If multiple quantiles `q` are given, the result's
            first axis is over different quantiles.
        """
        if type(q) == list:
            q = jnp.array(q)
        samples = self.sample(
            inputs_loader=inputs_loader,
            n_target_samples=n_target_samples,
            rng=rng,
            distribute=distribute,
        )
        return jnp.quantile(samples, q, axis=0)

        Estimate the `q`-th quantiles of the predictive probability density function.

        Parameters
        ----------
        q : Union[float, Array, List]
            Quantile or sequence of quantiles to compute. Each of these must be between 0 and 1, extremes included.
        inputs_loader : InputsLoader
            A loader of input data points.
        n_target_samples : int
            Number of target samples to sample for each input data point.
        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
            Quantile estimate for each quantile and each input. If multiple quantiles `q` are given, the result's
            first axis is over different quantiles.
        
fortuna/prob_model/predictive/regression.py