code: def test_speedup_integration(self):
      # skip this test on windows(7GB mem available) due to memory limit
      # Note: hack trick, may be updated in the future
      if "win" in sys.platform or "Win" in sys.platform:
          print(
              "Skip test_speedup_integration on windows due to memory limit!"
          )
          return
  
      Gen_cfg_funcs = [
          generate_random_sparsity,
          generate_random_sparsity_v2,
      ]
  
      for model_name in [
          "resnet18",
          "mobilenet_v2",
          "squeezenet1_1",
          "densenet121",
          "densenet169",
          # 'inception_v3' inception is too large and may fail the pipeline
          "resnet50",
      ]:
          for gen_cfg_func in Gen_cfg_funcs:
              kwargs = {"pretrained": True}
              if model_name == "resnet50":
                  # testing multiple groups
                  kwargs = {"pretrained": False, "groups": 4}
              Model = getattr(models, model_name)
              net = Model(**kwargs).to(device)
              speedup_model = Model(**kwargs).to(device)
              net.eval()  # this line is necessary
              speedup_model.eval()
              # random generate the prune config for the pruner
              cfgs = gen_cfg_func(net)
              print(
                  "Testing {} with compression config \n {}".format(
                      model_name, cfgs
                  )
              )
              pruner = L1FilterPruner(net, cfgs)
              pruner.compress()
              pruner.export_model(MODEL_FILE, MASK_FILE)
              pruner._unwrap_model()
              state_dict = torch.load(MODEL_FILE)
              speedup_model.load_state_dict(state_dict)
              zero_bn_bias(net)
              zero_bn_bias(speedup_model)
  
              data = torch.ones(BATCH_SIZE, 3, 128, 128).to(device)
              ms = ModelSpeedup(
                  speedup_model, data, masks_file=MASK_FILE
              )
              ms.speedup_model()
  
              speedup_model.eval()
  
              ori_out = net(data)
              speeded_out = speedup_model(data)
              ori_sum = torch.sum(ori_out).item()
              speeded_sum = torch.sum(speeded_out).item()
              print(
                  "Sum of the output of %s (before speedup):"
                  % model_name,
                  ori_sum,
              )
              print(
                  "Sum of the output of %s (after speedup):"
                  % model_name,
                  speeded_sum,
              )
              assert (
                  abs(ori_sum - speeded_sum) / abs(ori_sum)
                  < RELATIVE_THRESHOLD
              ) or (abs(ori_sum - speeded_sum) < ABSOLUTE_THRESHOLD)
  
imports: import os
  import psutil
  import sys
  import numpy
  import torch
  import torchvision.models
  import torch.nn
  import torch.nn.functional
  from torchvision.models.vgg import vgg16
  from torchvision.models.resnet import resnet18
  import unittest
  from unittest import TestCase, main
  from aup.compression.torch import L1FilterPruner, apply_compression_results, ModelSpeedup
  from aup.compression.torch.pruning.weight_masker import WeightMasker
  from aup.compression.torch.pruning.one_shot import _StructuredFilterPruner
label_desc: Use image node_id 4 for calling the SpeedupTestCase obj's underlying member method code with example usage: obj.test_speedup_integration() without return types