code: def train(model, x, y_true, train_idx, optimizer):
      model.train()
  
      optimizer.zero_grad()
      out = model(x[train_idx])
      loss = F.nll_loss(out, y_true.squeeze(1)[train_idx])
      loss.backward()
      optimizer.step()
  
      return loss.item()
  
file_path: ogb/examples/nodeproppred/arxiv/mlp.py
imports: import argparse
  import torch
  import torch.nn.functional
  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, x, y_true, train_idx, optimizer) and returns: loss