code: def train(model, x_train, y_train, batch_size, optimizer):
      model.train()
  
      total_loss = 0
      for idx in DataLoader(
          range(y_train.size(0)), batch_size, shuffle=True
      ):
          optimizer.zero_grad()
          loss = F.cross_entropy(model(x_train[idx]), y_train[idx])
          loss.backward()
          optimizer.step()
  
          total_loss += float(loss) * idx.numel()
  
      return total_loss / y_train.size(0)
  
file_path: ogb/examples/lsc/mag240m/mlp.py
imports: import time
  import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch.nn import ModuleList, Linear, BatchNorm1d, Identity
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  from root import ROOT
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, x_train, y_train, batch_size, optimizer) and returns: unknown