code: def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
      if pretrained:
          kwargs["init_weights"] = False
      model = VGG(
          make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs
      )
      if pretrained:
          state_dict = load_state_dict_from_url(
              model_urls[arch], progress=progress
          )
          model.load_state_dict(state_dict)
      return model
  
file_path: robustness/robustness/imagenet_models/vgg.py
imports: import torch.nn
  import torch
  from torch.hub import load_state_dict_from_url
  from ..tools.custom_modules import FakeReLUM
label_desc: Use image node_id 2 for calling a global function with example usage: _vgg(arch, cfg, batch_norm, pretrained, progress) and returns: model