code: def fidelity_inv(
      ori_probs: torch.Tensor, important_probs: torch.Tensor
  ) -> float:
      r"""
      Return the Fidelity- value according to collected data.
  
      Args:
          ori_probs (torch.Tensor): It is a vector providing original probabilities for Fidelity- computation.
          important_probs (torch.Tensor): It is a vector providing probabilities with only important features
              for Fidelity- computation.
  
      :rtype: float
  
      .. note::
          Please refer to `Explainability in Graph Neural Networks: A Taxonomic Survey
          <https://arxiv.org/abs/2012.15445>`_ for details.
      """
  
      drop_probability = ori_probs - important_probs
  
      return drop_probability.mean().item()
  
imports: import torch
  import torch.nn
  from typing import List, Union
  from torch import Tensor
  import numpy
  from torch_geometric.data.data import Data
  from torch_geometric.nn import MessagePassing
label_desc: Use image node_id 3 for calling a global function with example usage: fidelity_inv(ori_probs, important_probs) and returns: drop_probability