class_docstr: Vision Transformer with support for patch or hybrid CNN input stage
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","or","hybrid","CNN","input","stage"]
code: def group_matcher(self, coarse=False):
      return dict(
          stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
          blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
      )
  
imports: from functools import partial
  from typing import List
  from typing import Tuple
  import torch
  import torch.hub
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Block
label_desc: Use image node_id 4 for calling the CrossVit obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: dict