code: def _train(self, model, optimizer):
      model.train()
      for batch_idx, (data, target) in enumerate(self.train_loader):
          data, target = data.to(self.device), target.to(self.device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % 100 == 0:
              print(
                  "{:2.0f}%  Loss {}".format(
                      100 * batch_idx / len(self.train_loader),
                      loss.item(),
                  )
              )
  
imports: import os
  import torch
  import torch.nn.functional
  from torchvision.models.vgg import vgg16
  from torchvision import datasets, transforms
  import unittest
  from unittest import TestCase, main
  from nni.compression.quantization import QATQuantizer
  from nni.compression.quantization_speedup import ModelSpeedupTensorRT
label_desc: Use image node_id 2 for calling the QuantizationSpeedupTestCase obj's underlying member method code with example usage: obj._train(model, optimizer) without return types