code: def group_matcher(self, coarse=False):
      return dict(
          stem=r"^stem",
          blocks=[(r"^stages\.(\d+)", None), (r"^norm", (99999,))]
          if coarse
          else [
              (r"^stages\.(\d+)\.blocks\.(\d+)", None),
              (r"^stages\.(\d+)\.downsample", (0,)),
              (r"^norm", (99999,)),
          ],
      )
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 3 for calling the Sequencer2d obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: dict