code: def train(
      args,
      model,
      device,
      train_loader,
      optimizer,
      privacy_engine,
      epoch,
  ):
      model.train()
      criterion = nn.CrossEntropyLoss()
      losses = []
      for _batch_idx, (data, target) in enumerate(tqdm(train_loader)):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = criterion(output, target)
          loss.backward()
          optimizer.step()
          losses.append(loss.item())
  
      if not args.disable_dp:
          epsilon = privacy_engine.accountant.get_epsilon(
              delta=args.delta
          )
          print(
              f"Train Epoch: {epoch} \t"
              f"Loss: {np.mean(losses):.6f} "
              f"(ε = {epsilon:.2f}, δ = {args.delta})"
          )
      else:
          print(f"Train Epoch: {epoch} \t Loss: {np.mean(losses):.6f}")
  
file_path: opacus/examples/mnist.py
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from opacus import PrivacyEngine
  from torchvision import datasets, transforms
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: train(args, model, device, train_loader, optimizer, privacy_engine, epoch) without return types