class_docstr: Transformer in Transformer - https://arxiv.org/abs/2103.00112
      
code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^cls_token|patch_pos|pixel_pos|pixel_embed|norm[12]_proj|proj",  # stem and embed / pos
          blocks=[
              (r"^blocks\.(\d+)", None),
              (r"^norm", (99999,)),
          ],
      )
      return matcher
  
imports: import math
  import torch
  import torch.nn
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import Mlp, DropPath, trunc_normal_, _assert, to_2tuple
  from ._builder import build_model_with_cfg
  from ._registry import register_model
  from .vision_transformer import resize_pos_embed
label_desc: Use image node_id 4 for calling the TNT obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher