code: def train(model, loader, optimizer, device):
      model.train()
  
      total_loss = 0
      for data in loader:
          data = data.to(device)
          optimizer.zero_grad()
          out = model(data.x, data.edge_index)
          y = data.y.squeeze(1)
          loss = F.nll_loss(out[data.train_mask], y[data.train_mask])
          loss.backward()
          optimizer.step()
          total_loss += loss.item()
  
      return total_loss / len(loader)
  
file_path: ogb/examples/nodeproppred/products/graph_saint.py
imports: import argparse
  import torch
  from tqdm.auto import tqdm
  import torch.nn.functional
  from torch_geometric.data import GraphSAINTRandomWalkSampler, NeighborSampler
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils import subgraph
  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