code: def eval_related_pred(
      self, x: Tensor, edge_index: Tensor, masks: List[Tensor], **kwargs
  ):
      # place to add accuracy
      node_idx = kwargs.get("node_idx")
      pred_label = kwargs.get("pred_label")
      node_idx = (
          0 if node_idx is None else node_idx
      )  # graph level: 0, node level: node_idx
  
      related_preds = []
  
      for label, edge_mask in enumerate(masks):
          if self.hard_edge_mask is not None:
              sparsity = 1.0 - (
                  edge_mask[self.hard_edge_mask] != 0
              ).sum() / edge_mask[self.hard_edge_mask].size(0)
          else:
              sparsity = 1.0 - (edge_mask != 0).sum() / edge_mask.size(
                  0
              )
  
          # origin pred
          for mask in self.edge_mask:
              mask.data = torch.ones(
                  edge_mask.size(), device=self.device
              )
          ori_pred = self.model(x=x, edge_index=edge_index, **kwargs)
  
          for mask in self.edge_mask:
              mask.data = edge_mask
          masked_pred = self.model(x=x, edge_index=edge_index, **kwargs)
  
          # mask out important elements for fidelity calculation
          for mask in self.edge_mask:
              mask.data = 1.0 - edge_mask
          maskout_pred = self.model(
              x=x, edge_index=edge_index, **kwargs
          )
  
          # zero_mask
          for mask in self.edge_mask:
              mask.data = torch.zeros(
                  edge_mask.size(), device=self.device
              )
          zero_mask_pred = self.model(
              x=x, edge_index=edge_index, **kwargs
          )
  
          # Store related predictions for further evaluation.
          related_preds.append(
              {
                  "zero": zero_mask_pred[node_idx],
                  "masked": masked_pred[node_idx],
                  "maskout": maskout_pred[node_idx],
                  "origin": ori_pred[node_idx],
                  "sparsity": sparsity,
              }
          )
  
          # Adding proper activation function to the models' outputs.
          if pred_label:
              label = pred_label
          tmp_result_dict = {}
          for key, pred in related_preds[label].items():
              if key in ["sparsity"]:
                  tmp_result_dict[key] = pred.item()
              else:
                  tmp_result_dict[key] = (
                      pred.reshape(-1).softmax(0)[label].item()
                  )
          related_preds[label] = tmp_result_dict
  
      return related_preds
  
imports: from typing import List, Tuple, Dict
  from math import sqrt
  import torch
  from torch import Tensor
  import torch.nn
  import matplotlib.pyplot
  import networkx
  from torch_geometric.nn import MessagePassing
  from torch_geometric.utils.loop import add_self_loops, remove_self_loops
  from torch_geometric.data import Data, Batch
  from torch_geometric.utils import to_networkx
  from ..models.utils import subgraph
  from rdkit import Chem
  from matplotlib.axes import Axes
  import numpy
  from ..models import GNNPool
label_desc: Use image node_id 4 for calling the WalkBase obj's underlying member method code with example usage: obj.eval_related_pred(x, edge_index, masks) and returns: related_preds