code: def train(model, loader, optimizer, device):
      model.train()
  
      total_loss = total_examples = 0
      total_correct = total_examples = 0
      for data in loader:
          data = data.to(device)
          if data.train_mask.sum() == 0:
              continue
          optimizer.zero_grad()
          out = model(data.x, data.edge_index)[data.train_mask]
          y = data.y.squeeze(1)[data.train_mask]
          loss = F.nll_loss(out, y)
          loss.backward()
          optimizer.step()
  
          num_examples = data.train_mask.sum().item()
          total_loss += loss.item() * num_examples
          total_examples += num_examples
  
          total_correct += out.argmax(dim=-1).eq(y).sum().item()
          total_examples += y.size(0)
  
      return total_loss / total_examples, total_correct / total_examples
  
file_path: ogb/examples/nodeproppred/products/cluster_gcn.py
imports: import argparse
  import torch
  from tqdm.auto import tqdm
  import torch.nn.functional
  from torch_geometric.data import ClusterData, ClusterLoader, NeighborSampler
  from torch_geometric.nn import SAGEConv
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, loader, optimizer, device) and returns: unknown,  unknown