def METHOD_NAME(
    pos_fidelity: Tensor,
    neg_fidelity: Tensor,
    x: Tensor,
) -> Tensor:
    r"""Returns the AUC for the fidelity curve as described in the
    `"GraphFramEx: Towards Systematic Evaluation of Explainability Methods for
    Graph Neural Networks" <https://arxiv.org/abs/2206.09677>`_ paper.
    More precisely, returns the AUC of
    .. math::
        f(x) = \frac{\textrm{fid}_{+}}{1 - \textrm{fid}_{-}}
    Args:
        pos_fidelity (torch.Tensor): The positive fidelity
            :math:`\textrm{fid}_{+}`.
        neg_fidelity (torch.Tensor): The negative fidelity
            :math:`\textrm{fid}_{-}`.
        x (torch.Tensor): Tensor containing the points on the :math:`x`-axis.
            Needs to be sorted in ascending order.
    """
    if torch.any(neg_fidelity == 1):
        raise ValueError("There exists negative fidelity values containing 1, "
                         "leading to a division by zero")
    y = pos_fidelity / (1. - neg_fidelity)
    return auc(x, y)