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 forward_features(self, x):
      x = self.patch_embed(x)
      x = self.layers(x)
      x = self.norm(x)
      return x
  
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 8 for calling the SwinTransformerV2 obj's underlying member method code with example usage: obj.forward_features(x) and returns: x