code: def train(model, xs, y_true, optimizer):
      model.train()
  
      optimizer.zero_grad()
      out = model(xs)
      loss = F.nll_loss(out, y_true.squeeze(1))
      loss.backward()
      optimizer.step()
  
      return loss.item()
  
file_path: ogb/examples/nodeproppred/products/sign.py
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.transforms import SIGN
  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, xs, y_true, optimizer) and returns: loss