code: def test_se_densenet(pretrained=False):
      X = torch.Tensor(32, 3, 224, 224)
  
      if pretrained:
          model = se_densenet121(pretrained=pretrained)
          model.load_state_dict(
              model_zoo.load_url(
                  "https://download.pytorch.org/models/densenet121-a639ec97.pth"
              ),
              strict=False,
          )
  
      else:
          model = se_densenet121(pretrained=pretrained)
  
      print(model)
      if torch.cuda.is_available():
          X = X.cuda()
          model = model.cuda()
      model.eval()
      with torch.no_grad():
          output = model(X)
          print(output.shape)
  
imports: import re
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
  from collections import OrderedDict
  from .se_module import SELayer
label_desc: Use image node_id 5 for calling a global function with example usage: test_se_densenet(pretrained) without return types