code: def edges_to_tensor(edges):
      # This function transform a set of edges to edge index tensor
      edge_index = torch.tensor(list(edges), dtype=torch.long)
      edge_index = torch.cat(
          [edge_index, torch.flip(edge_index, [1])], dim=0
      )
      edge_index = edge_index.permute(1, 0)
      return edge_index
  
imports: import copy
  import torch
  import random
  import argparse
  import numpy
  import networkx
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn import SAGEConv
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.graph import Graph
  from model import *
label_desc: Use image node_id 4 for calling a global function with example usage: edges_to_tensor(edges) and returns: edge_index