code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^conv[12]|bn[12]",
          block_types=r"^(?:layer|stage|transition)(\d+)"
          if coarse
          else [
              (r"^layer(\d+)\.(\d+)", None),
              (r"^stage(\d+)\.(\d+)", None),
              (r"^transition(\d+)", (99999,)),
          ],
      )
      return matcher
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 7 for calling the HighResolutionNet obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher