code: def train_loop(
      args, dataloader, model, loss_fn, optimizer, epoch, total_loss
  ):
      # Set to train mode
      model.train()
      total_dataset = 0
      optimizer.zero_grad(set_to_none=True)
      enumerator = enumerate(dataloader, start=1)
      for batch, (X, y) in enumerator:
          my_context = (
              model.no_sync
              if WORLD_SIZE > 1
              and args.mini_batch > 0
              and batch % args.mini_batch != 0
              else nullcontext
          )
          with my_context():
              X, y = X.to(device), y.to(device)
              # Forward pass
              pred = model(X)
              loss = loss_fn(pred, y)
              loss.backward()
              total_loss += loss.item()
          if args.mini_batch == 0 or batch % args.mini_batch == 0:
              optimizer.step()
              optimizer.zero_grad(set_to_none=True)
  
          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 contextlib import nullcontext
  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
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