code: def group_matcher(self, coarse=False):
      # this treats BN layers as separate groups for bn variants, a lot of effort to fix that
      return dict(stem=r"^features\.0", blocks=r"^features\.(\d+)")
  
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 the VGG obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: dict