code: def train():
      model.train()
      optimizer.zero_grad()
      z = model(x, train_pos_edge_index, train_neg_edge_index)
      loss = model.loss(z, train_pos_edge_index, train_neg_edge_index)
      loss.backward()
      optimizer.step()
      return loss.item()
  
imports: import os.path
  import torch
  from torch_geometric.datasets import BitcoinOTC
  from torch_geometric.nn import SignedGCN
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: loss