def METHOD_NAME(self):
    torch.manual_seed(100)
    model = TorchModel1()
    dummy_input = torch.rand(3, 1, 28, 28)
    config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.5}]
    pruner = L1NormPruner(model=model, config_list=config_list)
    pruned_model, masks = pruner.compress()
    pruner.unwrap_model()
    sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
    # torch.manual_seed(100)
    speedup_model = ModelSpeedup(model, dummy_input, masks).speedup_model()
    print('before:\n', model)
    print('after:\n', repr(speedup_model))
    real_sparsity_list = compute_sparsity_compact2origin(TorchModel1(), speedup_model, config_list)
    print('sparsity_list:', sparsity_list)
    assert 0.45 < sparsity_list[0]['total_sparsity'] < 0.55
    print('real_sparsity_list:', real_sparsity_list)
    assert 0.45 < real_sparsity_list[0]['total_sparsity'] < 0.75
    print('the shape of output of the infer:', speedup_model(dummy_input).shape)
    assert speedup_model(dummy_input).shape == torch.Size((5, 5))