code: def train(args, model, device, train_loader, optimizer, epoch):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              print(
                  "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                      epoch,
                      batch_idx * len(data),
                      len(train_loader.dataset),
                      100.0 * batch_idx / len(train_loader),
                      loss.item(),
                  )
              )
              if args.dry_run:
                  break
  
file_path: auptimizer/Examples/compression/mnist_pytorch/mnist_amc.py
imports: from __future__ import print_function
  import argparse
  import sys
  import time
  import numpy
  from PIL import Image
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchvision
  from torchvision import datasets, transforms
  from torch.optim.lr_scheduler import StepLR
  import aup
label_desc: Use image node_id 1 for calling a global function with example usage: train(args, model, device, train_loader, optimizer, epoch) without return types