code: def group_matcher(self, coarse=False):
      return dict(
          stem=r"^stem",
          blocks=r"^stages\.(\d+)"
          if coarse
          else [
              (r"^stages\.(\d+)\.downsample", (0,)),  # blocks
              (r"^stages\.(\d+)\.blocks\.(\d+)", None),
              (r"^norm_pre", (99999,)),
          ],
      )
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the EdgeNeXt obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: dict