code: def train(
      config,
      epochs,
      model,
      train_ld,
      train_batches,
      optimizer,
      loss,
      scheduler,
      validate_func,
      valid_ld,
      metrics,
      validate_batches,
      validate_steps,
  ):
      import torch
      import time
  
      total_loss = 0
      total_samp = 0
      total_iter = 0
      total_time = 0
      previous_iteration_time = None
      step = 0
      for i in range(epochs):
          model.train()
          if config["use_ipex"]:
              import intel_extension_for_pytorch as ipex
  
              if config["bf16"]:
                  model, optimizer = ipex.optimize(
                      model, optimizer=optimizer, dtype=torch.bfloat16
                  )
              else:
                  model, optimizer = ipex.optimize(
                      model, optimizer=optimizer
                  )
          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)
              if config["bf16"]:
                  with torch.cpu.amp.autocast():
                      o = model(x, y)
                      l = loss(o, y)
                      l_np = l.detach().cpu().numpy()
                      y_np = y.detach().cpu().numpy()
              else:
                  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
              step += 1
              should_print = (
                  "print_freq" in config
                  and (step % 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, i, average_batch_time
                      )
                      + "loss {:.6f}, ".format(average_loss)
                  )
                  total_iter = 0
                  total_samp = 0
  
              should_validate = valid_ld and (
                  (validate_steps > 0 and (step % validate_steps == 0))
                  or (j + 1 == train_batches)
              )
              if should_validate:
                  validate_func(
                      config, model, valid_ld, metrics, validate_batches
                  )
  
imports: import os
  import subprocess
  import types
  import cloudpickle
  from pyspark.rdd import RDD
  from pyspark.sql import DataFrame
  from torch.utils.data import Dataset, DataLoader
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.orca.learn.mpi.mpi_runner import MPIRunner
  from bigdl.orca.learn.mpi.utils import *
label_desc: Use image node_id 4 for calling a global function with example usage: train(config, epochs, model, train_ld, train_batches, optimizer, loss, scheduler, validate_func, valid_ld, metrics, validate_batches, validate_steps) without return types