code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^features\.conv1",
          blocks=[
              (
                  r"^features\.conv(\d+)"
                  if coarse
                  else r"^features\.conv(\d+)_(\d+)",
                  None,
              ),
              (r"^features\.conv5_bn_ac", (99999,)),
          ],
      )
      return matcher
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier, get_norm_act_layer
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the DPN obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher