class_docstr: Self-attention module in Swin Transformer
      
code: def relative_embedding(self):
      cord = torch.tensor(
          np.array(
              [
                  [i, j]
                  for i in range(self.window_size)
                  for j in range(self.window_size)
              ]
          )
      )
      relation = (
          cord[:, None, :] - cord[None, :, :] + self.window_size - 1
      )
      # negative is allowed
      return self.relative_position_params[
          :, relation[:, :, 0].long(), relation[:, :, 1].long()
      ]
  
imports: import numpy
  import torch
  import torch.nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
  from timm.models.layers import trunc_normal_, DropPath
label_desc: Use image node_id 4 for calling the WMSA obj's underlying member method code with example usage: obj.relative_embedding() and returns: self