code: def train(
      args, model, train_loader, optimizer, privacy_engine, epoch
  ):
      criterion = nn.CrossEntropyLoss()
      losses = []
      accuracies = []
      device = torch.device(args.device)
      model = model.train().to(device)
  
      for data, label in tqdm(train_loader):
          data = data.to(device)
          label = label.to(device)
  
          optimizer.zero_grad()
          predictions = model(data).squeeze(1)
          loss = criterion(predictions, label)
          acc = binary_accuracy(predictions, label)
  
          loss.backward()
          optimizer.step()
  
          losses.append(loss.item())
          accuracies.append(acc.item())
  
      if not args.disable_dp:
          epsilon = privacy_engine.accountant.get_epsilon(
              delta=args.delta
          )
          print(
              f"Train Epoch: {epoch} \t"
              f"Train Loss: {np.mean(losses):.6f} "
              f"Train Accuracy: {np.mean(accuracies):.6f} "
              f"(ε = {epsilon:.2f}, δ = {args.delta})"
          )
      else:
          print(
              f"Train Epoch: {epoch} \t Loss: {np.mean(losses):.6f} ] \t Accuracy: {np.mean(accuracies):.6f}"
          )
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from datasets import load_dataset
  from opacus import PrivacyEngine
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import BertTokenizerFast
label_desc: Use image node_id 3 for calling a global function with example usage: train(args, model, train_loader, optimizer, privacy_engine, epoch) without return types