class_docstr: Swin Transformer Block.
  
  Args:
      dim (int): Number of input channels.
      input_resolution (tuple[int]): Input resulotion.
      num_heads (int): Number of attention heads.
      window_size (int): Window size.
      shift_size (int): Shift size for SW-MSA.
      mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
      qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
      qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
      drop (float, optional): Dropout rate. Default: 0.0
      attn_drop (float, optional): Attention dropout rate. Default: 0.0
      drop_path (float, optional): Stochastic depth rate. Default: 0.0
      act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
      norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
code: def extra_repr(self) -> str:
      return (
          f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
          f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
      )
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 3 for calling the SwinTransformerBlock obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self, str, self, str, self, str, self, str, self, str, self