code: def _create_vgg(variant: str, pretrained: bool, **kwargs: Any) -> VGG:
      cfg = variant.split("_")[0]
      # NOTE: VGG is one of few models with stride==1 features w/ 6 out_indices [0..5]
      out_indices = kwargs.pop("out_indices", (0, 1, 2, 3, 4, 5))
      model = build_model_with_cfg(
          VGG,
          variant,
          pretrained,
          model_cfg=cfgs[cfg],
          feature_cfg=dict(
              flatten_sequential=True, out_indices=out_indices
          ),
          pretrained_filter_fn=_filter_fn,
          **kwargs,
      )
      return model
  
imports: from typing import Union, List, Dict, Any, cast
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling a global function with example usage: _create_vgg(variant, pretrained) and returns: model