code: def train_model(
      epoch,
      model,
      optimizer,
      train_x,
      train_y,
      valid_x,
      valid_y,
      test_x,
      test_y,
      best_valid,
      test_err,
  ):
      model.train()
      args = model.args
      N = len(train_x)
      niter = epoch * len(train_x)
      criterion = nn.CrossEntropyLoss()
  
      cnt = 0
      for x, y in zip(train_x, train_y):
          niter += 1
          cnt += 1
          model.zero_grad()
          output = model(x)
          loss = criterion(output, y)
          loss.backward()
          optimizer.step()
  
      valid_err = eval_model(niter, model, valid_x, valid_y)
  
      sys.stdout.write(
          "Epoch={} iter={} lr={:.6f} train_loss={:.6f} valid_err={:.6f}\n".format(
              epoch,
              niter,
              optimizer.param_groups[0]["lr"],
              loss.item(),
              valid_err,
          )
      )
  
      if valid_err < best_valid:
          best_valid = valid_err
          test_err = eval_model(niter, model, test_x, test_y)
      sys.stdout.write("\n")
      return best_valid, test_err
  
imports: import os
  import sys
  import argparse
  import time
  import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.autograd import Variable
  from sru import *
  import dataloader
  import modules
label_desc: Use image node_id 2 for calling a global function with example usage: train_model(epoch, model, optimizer, train_x, train_y, valid_x, valid_y, test_x, test_y, best_valid, test_err) and returns: best_valid,  test_err