class_docstr: Twins Vision Transfomer (Revisiting Spatial Attention)
  
  Adapted from PVT (PyramidVisionTransformer) class at https://github.com/whai362/PVT.git
code: def group_matcher(self, coarse=False):
      matcher = dict(
          stem=r"^patch_embeds.0",  # stem and embed
          blocks=[
              (r"^(?:blocks|patch_embeds|pos_block)\.(\d+)", None),
              ("^norm", (99999,)),
          ]
          if coarse
          else [
              (r"^blocks\.(\d+)\.(\d+)", None),
              (r"^(?:patch_embeds|pos_block)\.(\d+)", (0,)),
              (r"^norm", (99999,)),
          ],
      )
      return matcher
  
imports: import math
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import Mlp, DropPath, to_2tuple, trunc_normal_, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._registry import register_model, generate_default_cfgs
  from .vision_transformer import Attention
label_desc: Use image node_id 3 for calling the Twins obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: matcher