code: def unfaithfulness(
      explainer: Explainer,
      explanation: Explanation,
      top_k: Optional[int] = None,
  ) -> float:
      r"""Evaluates how faithful an :class:`~torch_geometric.explain.Explanation`
      is to an underyling GNN predictor, as described in the
      `"Evaluating Explainability for Graph Neural Networks"
      <https://arxiv.org/abs/2208.09339>`_ paper.
  
      In particular, the graph explanation unfaithfulness metric is defined as
  
      .. math::
          \textrm{GEF}(y, \hat{y}) = 1 - \exp(- \textrm{KL}(y || \hat{y}))
  
      where :math:`y` refers to the prediction probability vector obtained from
      the original graph, and :math:`\hat{y}` refers to the prediction
      probability vector obtained from the masked subgraph.
      Finally, the Kullback-Leibler (KL) divergence score quantifies the distance
      between the two probability distributions.
  
      Args:
          explainer (Explainer): The explainer to evaluate.
          explanation (Explanation): The explanation to evaluate.
          top_k (int, optional): If set, will only keep the original values of
              the top-:math:`k` node features identified by an explanation.
              If set to :obj:`None`, will use :obj:`explanation.node_mask` as it
              is for masking node features. (default: :obj:`None`)
      """
      if explainer.model_config.mode == ModelMode.regression:
          raise ValueError(
              "Fidelity not defined for 'regression' models"
          )
  
      if (
          top_k is not None
          and explainer.node_mask_type == MaskType.object
      ):
          raise ValueError(
              "Cannot apply top-k feature selection based on a "
              "node mask of type 'object'"
          )
  
      node_mask = explanation.get("node_mask")
      edge_mask = explanation.get("edge_mask")
      x, edge_index = explanation.x, explanation.edge_index
      kwargs = {
          key: explanation[key] for key in explanation._model_args
      }
  
      y = explanation.get("prediction")
      if y is None:  # == ExplanationType.phenomenon
          y = explainer.get_prediction(x, edge_index, **kwargs)
  
      if node_mask is not None and top_k is not None:
          feat_importance = node_mask.sum(dim=0)
          _, top_k_index = feat_importance.topk(top_k)
          node_mask = torch.zeros_like(node_mask)
          node_mask[:, top_k_index] = 1.0
  
      y_hat = explainer.get_masked_prediction(
          x, edge_index, node_mask, edge_mask, **kwargs
      )
  
      if explanation.get("index") is not None:
          y, y_hat = y[explanation.index], y_hat[explanation.index]
  
      if explainer.model_config.return_type == ModelReturnType.raw:
          y, y_hat = y.softmax(dim=-1), y_hat.softmax(dim=-1)
      elif (
          explainer.model_config.return_type
          == ModelReturnType.log_probs
      ):
          y, y_hat = y.exp(), y_hat.exp()
  
      kl_div = F.kl_div(y.log(), y_hat, reduction="batchmean")
      return 1 - float(torch.exp(-kl_div))
  
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch_geometric.explain import Explainer, Explanation
  from torch_geometric.explain.config import MaskType, ModelMode, ModelReturnType
label_desc: Use image node_id 1 for calling a global function with example usage: unfaithfulness(explainer, explanation, top_k) and returns: unknown