class_docstr: Swin Transformer Block.
      
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      B, H, W, C = x.shape
      x = x + self.drop_path1(self.norm1(self._attn(x)))
      x = x.reshape(B, -1, C)
      x = x + self.drop_path2(self.norm2(self.mlp(x)))
      x = x.reshape(B, H, W, C)
      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 4 for calling the SwinTransformerV2Block obj's underlying member method code with example usage: obj.forward(x) and returns: x