code: def train():
      model.train()
      optimizer.zero_grad()
  
      z = model.encode(data.edge_index, data.edge_type)
  
      pos_out = model.decode(
          z, data.train_edge_index, data.train_edge_type
      )
  
      neg_edge_index = negative_sampling(
          data.train_edge_index, data.num_nodes
      )
      neg_out = model.decode(z, neg_edge_index, data.train_edge_type)
  
      out = torch.cat([pos_out, neg_out])
      gt = torch.cat(
          [torch.ones_like(pos_out), torch.zeros_like(neg_out)]
      )
      cross_entropy_loss = F.binary_cross_entropy_with_logits(out, gt)
      reg_loss = z.pow(2).mean() + model.decoder.rel_emb.pow(2).mean()
      loss = cross_entropy_loss + 1e-2 * reg_loss
  
      loss.backward()
      torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
  
      return float(loss)
  
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from torch.nn import Parameter
  from tqdm import tqdm
  from torch_geometric.datasets import RelLinkPredDataset
  from torch_geometric.nn import GAE, RGCNConv
label_desc: Use image node_id 2 for calling a global function with example usage: train() and returns: float