code: def normalized_cut_2d(edge_index, pos):
      row, col = edge_index
      edge_attr = torch.norm(pos[row] - pos[col], p=2, dim=1)
      return normalized_cut(
          edge_index, edge_attr, num_nodes=pos.size(0)
      )
  
file_path: pytorch_geometric/examples/contrib/pgm_explainer_graph_classification.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.contrib.explain import PGMExplainer
  from torch_geometric.datasets import MNISTSuperpixels
  from torch_geometric.explain import Explainer
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, global_mean_pool, graclus, max_pool, max_pool_x
  from torch_geometric.utils import normalized_cut
label_desc: Use image node_id 1 for calling a global function with example usage: normalized_cut_2d(edge_index, pos) and returns: normalized_cut