class_docstr: CoaT class. 
class_docstr_tok: ["CoaT","class","."]
code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem1=r"^cls_token1|patch_embed1|crpe1|cpe1",
          serial_blocks1=r"^serial_blocks1\.(\d+)",
          stem2=r"^cls_token2|patch_embed2|crpe2|cpe2",
          serial_blocks2=r"^serial_blocks2\.(\d+)",
          stem3=r"^cls_token3|patch_embed3|crpe3|cpe3",
          serial_blocks3=r"^serial_blocks3\.(\d+)",
          stem4=r"^cls_token4|patch_embed4|crpe4|cpe4",
          serial_blocks4=r"^serial_blocks4\.(\d+)",
          parallel_blocks=[  # FIXME (partially?) overlap parallel w/ serial blocks??
              (r"^parallel_blocks\.(\d+)", None),
              (r"^norm|aggregate", (99999,)),
          ],
      )
      return matcher
  
imports: from functools import partial
  from typing import Tuple, List, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 5 for calling the CoaT obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher