class_docstr: Swin Transformer V2
  
  A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
      - https://arxiv.org/abs/2111.09883
code: def group_matcher(self, coarse=False):
      return dict(
          stem=r"^absolute_pos_embed|patch_embed",  # stem and embed
          blocks=r"^layers\.(\d+)"
          if coarse
          else [
              (r"^layers\.(\d+).downsample", (0,)),
              (r"^layers\.(\d+)\.\w+\.(\d+)", None),
              (r"^norm", (99999,)),
          ],
      )
  
imports: import math
  from typing import Callable, Optional, Tuple, Union, Set, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, ClassifierHead, resample_patch_embed
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 4 for calling the SwinTransformerV2 obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: dict