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):
      matcher = dict(
          stem=r"^cls_token|pos_embed|patch_embed|rel_pos_bias",  # stem and embed
          blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
      )
      return matcher
  
imports: import math
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, SwiGLU, LayerNorm, DropPath, trunc_normal_, use_fused_attn
  from timm.layers import resample_patch_embed, resample_abs_pos_embed, resize_rel_pos_bias_table
  from ._builder import build_model_with_cfg
  from ._registry import generate_default_cfgs, register_model
  from .vision_transformer import checkpoint_filter_fn
label_desc: Use image node_id 6 for calling the Beit obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher