code: def train_loop(
      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:
          X, y = X.to(device), y.to(device)
          pred = model(X)
          loss = loss_fn(pred, y)
          loss.backward()
          total_loss += loss.item()
          optimizer.step()
          optimizer.zero_grad(set_to_none=True)
  
          total_dataset += 16
          if batch % 20 == 0:
              msg = "Train Epoch: {} loss={:.4f}".format(
                  epoch, loss.item()
              )
              logging.info(msg)
  
      return total_loss, total_dataset
  
imports: from torch.utils.data import DataLoader
  import torch
  import torch.optim
  from contextlib import nullcontext
  import torch.nn.functional
  import os
  import torch.nn
  import time
  from bigdl.nano.pytorch.patching import patch_encryption
  from bigdl.nano.pytorch.encryption import EncryptedDataset
  import logging
  from bigdl.ppml.kms.ehsm.client import generate_primary_key, generate_data_key, get_data_key_plaintext
  from datasets.load import load_from_disk
  import argparse
  from transformers import BertTokenizer, BertModel, AdamW
label_desc: Use image node_id 5 for calling a global function with example usage: train_loop(dataloader, model, loss_fn, optimizer, epoch, total_loss) and returns: total_loss,  total_dataset