code: def vgg16(pretrained=False, progress=True, **kwargs):
      r"""VGG 16-layer model (configuration "D")
      `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>'_
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
      """
      return _vgg("vgg16", "D", False, pretrained, progress, **kwargs)
  
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 7 for calling a global function with example usage: vgg16(pretrained, progress) and returns: _vgg