code: def train_epoch(
      config,
      model,
      train_ld,
      train_batches,
      optimizer,
      loss,
      scheduler,
      validate_func,
      valid_ld,
      metrics,
      validate_batches,
      validate_steps,
  ):
      import time
  
      total_loss = 0
      total_samp = 0
      total_iter = 0
      total_time = 0
      previous_iteration_time = None
      train_iter = iter(train_ld)
      for j in range(train_batches):
          # Iterate again from the beginning if running out of batches.
          if j > 0 and j % len(train_ld) == 0:
              train_iter = iter(train_ld)
          current_time = time.time()
          if previous_iteration_time:
              iteration_time = current_time - previous_iteration_time
          else:
              iteration_time = 0
          previous_iteration_time = current_time
          x, y = next(train_iter)
          o = model(x, y)
          l = loss(o, y)
          l_np = l.detach().cpu().numpy()
          y_np = y.detach().cpu().numpy()
          optimizer.zero_grad()
          l.backward()
          optimizer.step()
          if scheduler:
              scheduler.step()
  
          batch_samples = y_np.shape[0]
          total_time += iteration_time
          total_loss += l_np * batch_samples
          total_iter += 1
          total_samp += batch_samples
  
          should_print = (
              "print_freq" in config
              and ((j + 1) % config["print_freq"] == 0)
          ) or (j + 1 == train_batches)
          if should_print:
              average_batch_time = 1000.0 * total_time / total_iter
              total_time = 0
              average_loss = total_loss / total_samp
              total_loss = 0
  
              print(
                  "Finished training it {}/{} of epoch {}, {:.2f} ms/it, ".format(
                      j + 1,
                      train_batches,
                      config["epoch"],
                      average_batch_time,
                  )
                  + "loss {:.6f}, ".format(average_loss)
              )
              total_iter = 0
              total_samp = 0
  
          should_validate = valid_ld and (
              (validate_steps > 0 and (j + 1) % validate_steps == 0)
              or (j + 1 == train_batches)
          )
          if should_validate:
              validate_func(
                  config, model, valid_ld, metrics, validate_batches
              )
  
imports: import os
  import types
  import subprocess
  import cloudpickle
  from pyspark.sql import DataFrame
  from torch.utils.data import Dataset, DataLoader
  from zoo.util.utils import get_node_ip
  from zoo.orca.learn.mpi.mpi_runner import MPIRunner
  from zoo.orca.learn.mpi.utils import *
label_desc: Use image node_id 4 for calling a global function with example usage: train_epoch(config, model, train_ld, train_batches, optimizer, loss, scheduler, validate_func, valid_ld, metrics, validate_batches, validate_steps) without return types