code: def fidelity_curve_auc(
      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 / (1.0 - neg_fidelity)
      return auc(x, y)
  
file_path: pytorch_geometric/torch_geometric/explain/metric/fidelity.py
imports: from typing import Tuple
  import torch
  from torch import Tensor
  from torch_geometric.explain import Explainer, Explanation
  from torch_geometric.explain.config import ExplanationType, ModelMode
label_desc: Use image node_id 3 for calling a global function with example usage: fidelity_curve_auc(pos_fidelity, neg_fidelity, x) and returns: auc