code: def test_export_load_quantized_model_vgg16(self):
      model = vgg16()
  
      configure_list = {
          "features.0": {"weight_bits": 8, "output_bits": 8},
          "features.1": {"weight_bits": 32, "output_bits": 32},
          "features.2": {"weight_bits": 16, "output_bits": 16},
          "features.4": {"weight_bits": 8, "output_bits": 8},
          "features.7": {"weight_bits": 8, "output_bits": 8},
          "features.8": {"weight_bits": 8, "output_bits": 8},
          "features.11": {"weight_bits": 8, "output_bits": 8},
      }
  
      model.to(self.device)
  
      batch_size = 1
      input_shape = (batch_size, 3, 224, 224)
      dummy_input = torch.randn(input_shape).to(self.device)
  
      output_torch = model(dummy_input)
  
      engine = ModelSpeedupTensorRT(
          model,
          input_shape,
          config=configure_list,
          calib_data_loader=dummy_input,
          batchsize=batch_size,
      )
      engine.compress()
      output, _ = engine.inference(dummy_input)
  
      # verify result shape
      assert output.shape == output_torch.shape
  
      export_path = "vgg16_trt.engine"
      calibration_path = "calibration.cache"
      engine.export_quantized_model(export_path)
      engine.load_quantized_model(export_path)
      output, _ = engine.inference(dummy_input)
  
      assert output.shape == output_torch.shape
  
      os.remove(export_path)
      os.remove(calibration_path)
  
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 7 for calling the QuantizationSpeedupTestCase obj's underlying member method code with example usage: obj.test_export_load_quantized_model_vgg16() without return types