code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^base_layer",
          blocks=r"^level(\d+)"
          if coarse
          else [
              # an unusual arch, this achieves somewhat more granularity without getting super messy
              (r"^level(\d+)\.tree(\d+)", None),
              (r"^level(\d+)\.root", (2,)),
              (r"^level(\d+)", (1,)),
          ],
      )
      return matcher
  
imports: import math
  from typing import List, Optional
  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
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 3 for calling the DLA obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher