class_docstr: Swin Transformer Block.
      
code: def _attn(self, x: torch.Tensor) -> torch.Tensor:
      B, H, W, C = x.shape
  
      # cyclic shift
      has_shift = any(self.shift_size)
      if has_shift:
          shifted_x = torch.roll(
              x,
              shifts=(-self.shift_size[0], -self.shift_size[1]),
              dims=(1, 2),
          )
      else:
          shifted_x = x
  
      # partition windows
      x_windows = window_partition(
          shifted_x, self.window_size
      )  # nW*B, window_size, window_size, C
      x_windows = x_windows.view(
          -1, self.window_area, C
      )  # nW*B, window_size*window_size, C
  
      # W-MSA/SW-MSA
      attn_windows = self.attn(
          x_windows, mask=self.attn_mask
      )  # nW*B, window_size*window_size, C
  
      # merge windows
      attn_windows = attn_windows.view(
          -1, self.window_size[0], self.window_size[1], C
      )
      shifted_x = window_reverse(
          attn_windows, self.window_size, self.input_resolution
      )  # B H' W' C
  
      # reverse cyclic shift
      if has_shift:
          x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
      else:
          x = shifted_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 3 for calling the SwinTransformerV2Block obj's underlying member method code with example usage: obj._attn(x) and returns: x