class_docstr: Window based multi-head self attention (W-MSA) module with relative position bias.
  It supports shifted and non-shifted windows.
class_docstr_tok: ["Window","based","multi-head","self","attention","(","W-MSA",")","module","with","relative","position","bias",".","It","supports","shifted","and","non-shifted","windows","."]
code: def _get_rel_pos_bias(self) -> torch.Tensor:
      relative_position_bias = self.relative_position_bias_table[
          self.relative_position_index.view(-1)
      ].view(
          self.window_area, self.window_area, -1
      )  # Wh*Ww,Wh*Ww,nH
      relative_position_bias = relative_position_bias.permute(
          2, 0, 1
      ).contiguous()  # nH, Wh*Ww, Wh*Ww
      return relative_position_bias.unsqueeze(0)
  
imports: import logging
  import math
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, ClassifierHead, to_2tuple, to_ntuple, trunc_normal_, _assert, use_fused_attn, resize_rel_pos_bias_table, resample_patch_embed
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
  from .vision_transformer import get_init_weights_vit
label_desc: Use image node_id 2 for calling the WindowAttention obj's underlying member method code with example usage: obj._get_rel_pos_bias() and returns: relative_position_bias