code: def decode(self, z, pos_edge_index, neg_edge_index):
      edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
      logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
      return logits
  
imports: import time
  import torch
  import torch.nn.functional
  import numpy
  import random
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import GCNConv, GATConv, SAGEConv
  import torch_geometric.transforms
  from torch_geometric.utils import train_test_split_edges
  from torch_geometric.utils import negative_sampling
  import os.path
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 3 for calling the GCN obj's underlying member method code with example usage: obj.decode(z, pos_edge_index, neg_edge_index) and returns: logits