code: def __init__(self, methodName: str) -> None:
      super().__init__(methodName=methodName)
      self.device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      trans = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,)),
          ]
      )
      self.train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "data", train=True, download=True, transform=trans
          ),
          batch_size=64,
          shuffle=True,
      )
      self.test_loader = torch.utils.data.DataLoader(
          datasets.MNIST("data", train=False, transform=trans),
          batch_size=1000,
          shuffle=True,
      )
  
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 1 to create a new QuantizationSpeedupTestCase object from inherited base classes: TestCase with example: obj = QuantizationSpeedupTestCase(methodName)