code: def train_loop(
      args, dataloader, model, loss_fn, optimizer, epoch, total_loss
  ):
      finish_batch_num = (epoch - 1) * len(dataloader)
  
      # Set to train mode
      model.train()
      total_dataset = 0
      for batch, (X, y) in enumerate(dataloader, start=1):
          optimizer.zero_grad(set_to_none=True)
          with torch.autocast("cpu", enabled=False):
              X, y = X.to(device), y.to(device)
              pred = model(X)
              # Calculate loss
              loss = loss_fn(pred, y)
              loss.backward()
  
          optimizer.step()
  
          total_loss += loss.item()
          total_dataset += args.batch_size
          if batch % args.log_interval == 0:
              msg = "Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format(
                  epoch,
                  batch,
                  len(dataloader),
                  100.0 * batch / len(dataloader),
                  loss.item(),
              )
              logging.info(msg)
  
      return total_loss, total_dataset
  
imports: import torch
  import argparse
  import os
  import logging
  import time
  from torch import nn
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  import torch.distributed
  from transformers import BertTokenizer, BertModel, AdamW
  import intel_extension_for_pytorch
label_desc: Use image node_id 4 for calling a global function with example usage: train_loop(args, dataloader, model, loss_fn, optimizer, epoch, total_loss) and returns: total_loss,  total_dataset