def score(
        self,
        val_preds: Array,
        val_uncertainties: Array,
        val_targets: Array,
    ) -> jnp.ndarray:
        """
        Compute the conformal scores.

        Parameters
        ----------
        val_preds: Array
            A two-dimensional array of predictions over the validation data points.
        val_uncertainties: Array
            A two-dimensional array of uncertainty estimates (e.g. the standard deviation). The first
            dimension is over the validation inputs. The second must have only one component.
        val_targets: Array
            A two-dimensional array of validation target variables.

        Returns
        -------
        jnp.ndarray
            Scores.
        """
        if val_preds.ndim != 2 or val_preds.shape[1] != 1:
            raise ValueError(
                """`val_preds` must be a two-dimensional array. The second dimension must have only one
            component."""
            )
        if val_uncertainties.ndim != 2 or val_uncertainties.shape[1] != 1:
            raise ValueError(
                """`val_uncertainties` must be a two-dimensional array. The second dimension must have only
            one component."""
            )
        if (val_uncertainties <= 0).any():
            raise ValueError(
                """All elements in `val_uncertainties` must be strictly positive."""
            )
        if val_targets.shape[1] != 1:
            raise ValueError(
                """The second dimension of the array(s) in `val_targets` must have only one component."""
            )
        return (jnp.abs(val_targets - val_preds) / val_uncertainties).squeeze(1)

        Compute the conformal scores.

        Parameters
        ----------
        val_preds: Array
            A two-dimensional array of predictions over the validation data points.
        val_uncertainties: Array
            A two-dimensional array of uncertainty estimates (e.g. the standard deviation). The first
            dimension is over the validation inputs. The second must have only one component.
        val_targets: Array
            A two-dimensional array of validation target variables.

        Returns
        -------
        jnp.ndarray
            Scores.
        
fortuna/conformal/regression/onedim_uncertainty.py