code: def train(epoch):
      model.train()
  
      pbar = tqdm(total=len(train_loader))
      pbar.set_description(f"Training epoch: {epoch:03d}")
  
      total_loss = total_examples = 0
      for data in train_loader:
          optimizer.zero_grad()
  
          # Memory-efficient aggregations:
          data = transform(data)
          out = model(data.x, data.adj_t)[data.train_mask]
          loss = F.cross_entropy(out, data.y[data.train_mask].view(-1))
          loss.backward()
          optimizer.step()
  
          total_loss += float(loss) * int(data.train_mask.sum())
          total_examples += int(data.train_mask.sum())
          pbar.update(1)
  
      pbar.close()
  
      return total_loss / total_examples
  
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch.nn import LayerNorm, Linear
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.loader import RandomNodeLoader
  from torch_geometric.nn import GroupAddRev, SAGEConv
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 1 for calling a global function with example usage: train(epoch) and returns: unknown