code: def explain_edges_with_loop(
      self, x: Tensor, walks: Dict[Tensor, Tensor], ex_label
  ):
      walks_ids = walks["ids"]
      walks_score = walks["score"][
          : walks_ids.shape[0], ex_label
      ].reshape(-1)
      if (
          walks_ids.max() <= self.num_edges - 1
      ):  # num_edges includes the self-loop
          idx_ensemble = torch.cat(
              [
                  (walks_ids == i).int().sum(dim=1).unsqueeze(0)
                  for i in range(self.num_edges)
              ],
              dim=0,
          )
      else:
          idx_ensemble = torch.cat(
              [
                  (walks_ids == i).int().sum(dim=1).unsqueeze(0)
                  for i in range(self.num_edges + self.num_nodes)
              ],
              dim=0,
          )
      hard_edge_attr_mask = (idx_ensemble.sum(1) > 0).long()
      hard_edge_attr_mask_value = torch.tensor(
          [float("inf"), 0], dtype=torch.float, device=self.device
      )[hard_edge_attr_mask]
      edge_attr = (idx_ensemble * (walks_score.unsqueeze(0))).sum(1)
      # idx_ensemble1 = torch.cat(
      #     [(walks_ids == i).int().sum(dim=1).unsqueeze(1) for i in range(self.num_edges + self.num_nodes)], dim=1)
      # edge_attr1 = (idx_ensemble1 * (walks_score.unsqueeze(1))).sum(0)
  
      return edge_attr - hard_edge_attr_mask_value
  
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 5 for calling the WalkBase obj's underlying member method code with example usage: obj.explain_edges_with_loop(x, walks, ex_label) and returns: unknown