code: def _test_trt(self, engine):
      test_loss = 0
      correct = 0
      time_elasped = 0
      for data, target in self.test_loader:
          output, time = engine.inference(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()
          time_elasped += time
      test_loss /= len(self.test_loader.dataset)
  
      print(
          "Loss: {}  Accuracy: {}%".format(
              test_loss, 100 * correct / len(self.test_loader.dataset)
          )
      )
      print(
          "Inference elapsed_time (whole dataset): {}s".format(
              time_elasped
          )
      )
  
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 4 for calling the QuantizationSpeedupTestCase obj's underlying member method code with example usage: obj._test_trt(engine) without return types