code: def train():
      model.train()
      optimizer.zero_grad()
      z = model.encode(train_data.x, train_data.edge_index)
      loss = model.recon_loss(z, train_data.pos_edge_label_index)
      if args.variational:
          loss = loss + (1 / train_data.num_nodes) * model.kl_loss()
      loss.backward()
      optimizer.step()
      return float(loss)
  
imports: import argparse
  import os.path
  import time
  import torch
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import GAE, VGAE, GCNConv
label_desc: Use image node_id 1 for calling a global function with example usage: train() and returns: float