code: def train_one_epoch(
      self, adjust_lr_func, train_log_func, label_smoothing=0.1
  ):
      batch_time = AverageMeter("batch_time")
      data_time = AverageMeter("data_time")
      losses = AverageMeter("losses")
      top1 = AverageMeter("top1")
      top5 = AverageMeter("top5")
      self.model.train()
      end = time.time()
      for i, (images, labels) in enumerate(self.train_loader):
          data_time.update(time.time() - end)
          new_lr = adjust_lr_func(i)
          images, labels = images.to(self.device), labels.to(
              self.device
          )
          output = self.model(images)
          if label_smoothing > 0:
              loss = cross_entropy_with_label_smoothing(
                  output, labels, label_smoothing
              )
          else:
              loss = self.criterion(output, labels)
          acc1, acc5 = accuracy(output, labels, topk=(1, 5))
          losses.update(loss, images.size(0))
          top1.update(acc1[0], images.size(0))
          top5.update(acc5[0], images.size(0))
  
          # compute gradient and do SGD step
          self.model.zero_grad()  # or self.optimizer.zero_grad()
          loss.backward()
          self.optimizer.step()
  
          # measure elapsed time
          batch_time.update(time.time() - end)
          end = time.time()
  
          if i % 10 == 0 or i + 1 == len(self.train_loader):
              batch_log = train_log_func(
                  i, batch_time, data_time, losses, top1, top5, new_lr
              )
              print(batch_log)
      return top1, top5
  
file_path: nni/examples/nas/legacy/oneshot/proxylessnas/retrain.py
imports: import time
  import math
  from datetime import timedelta
  import torch
  from torch import nn
  from nni.nas.pytorch.utils import AverageMeter
label_desc: Use image node_id 3 for calling the Retrain obj's underlying member method code with example usage: obj.train_one_epoch(adjust_lr_func, train_log_func, label_smoothing) and returns: top1,  top5