code: def _test(self, model):
      model.eval()
      test_loss = 0
      correct = 0
      with torch.no_grad():
          for data, target in self.test_loader:
              data, target = data.to(self.device), target.to(
                  self.device
              )
              output = model(data)
              test_loss += F.nll_loss(
                  output, target, reduction="sum"
              ).item()
              pred = output.argmax(dim=1, keepdim=True)
              correct += pred.eq(target.view_as(pred)).sum().item()
      test_loss /= len(self.test_loader.dataset)
  
      print(
          "Loss: {}  Accuracy: {}%)\n".format(
              test_loss, 100 * correct / len(self.test_loader.dataset)
          )
      )
  
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 3 for calling the QuantizationSpeedupTestCase obj's underlying member method code with example usage: obj._test(model) without return types