code: def vgg(
      depth: int, batch_normalization=False, pretrained=False
  ) -> Module:
      """
      Wrapper for VGG net of verious depths availble in the pytorchcv package.
      VGG is only availabe for imagenet.
  
      :param depth: Depth of the model, one of (11, 13, 16, 19)
      :param batch_normalization: include batch normalizaion layers
      :param pretrained: loads model pretrained on imagnet
      """
      available_depths = [11, 13, 16, 19]
      if depth not in available_depths:
          raise ValueError(
              f"Depth {depth} not available, "
              f"availble depths are {available_depths}"
          )
  
      name = f"vgg{depth}"
      if batch_normalization:
          name = f"bn_{name}"
  
      return ptcv_get_model(name, pretrained=pretrained)
  
imports: from pytorchcv.model_provider import get_model
  from torch.nn import Module
label_desc: Use image node_id 1 for calling a global function with example usage: vgg(depth, batch_normalization, pretrained) and returns: ptcv_get_model