code: def train(
      model, predictor, x, adj_t, split_edge, optimizer, batch_size
  ):
      row, col, _ = adj_t.coo()
      edge_index = torch.stack([col, row], dim=0)
  
      model.train()
      predictor.train()
  
      pos_train_edge = split_edge["train"]["edge"].to(x.device)
  
      total_loss = total_examples = 0
      for perm in DataLoader(
          range(pos_train_edge.size(0)), batch_size, shuffle=True
      ):
          optimizer.zero_grad()
  
          h = model(x, adj_t)
  
          edge = pos_train_edge[perm].t()
  
          pos_out = predictor(h[edge[0]], h[edge[1]])
          pos_loss = -torch.log(pos_out + 1e-15).mean()
  
          edge = negative_sampling(
              edge_index,
              num_nodes=x.size(0),
              num_neg_samples=perm.size(0),
              method="dense",
          )
  
          neg_out = predictor(h[edge[0]], h[edge[1]])
          neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
  
          loss = pos_loss + neg_loss
          loss.backward()
  
          torch.nn.utils.clip_grad_norm_(x, 1.0)
          torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
          torch.nn.utils.clip_grad_norm_(predictor.parameters(), 1.0)
  
          optimizer.step()
  
          num_examples = pos_out.size(0)
          total_loss += loss.item() * num_examples
          total_examples += num_examples
  
      return total_loss / total_examples
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.utils import negative_sampling
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, SAGEConv
  from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, predictor, x, adj_t, split_edge, optimizer, batch_size) and returns: unknown