code: def forward(self, training_window_size):
      if training_window_size == self.window_size:
          relative_position_bias = self.relative_position_bias_table[
              self.relative_position_index.view(-1)
          ].view(
              self.window_size[0] * self.window_size[1] + 1,
              self.window_size[0] * self.window_size[1] + 1,
              -1,
          )  # Wh*Ww,Wh*Ww,nH
          relative_position_bias = relative_position_bias.permute(
              2, 0, 1
          ).contiguous()  # nH, Wh*Ww, Wh*Ww
      else:
          training_window_size = tuple(training_window_size.tolist())
          new_num_relative_distance = (
              2 * training_window_size[0] - 1
          ) * (2 * training_window_size[1] - 1) + 3
          # new_num_relative_dis 为 所有可能的相对位置选项，包含cls-cls，tok-cls，与cls-tok
          new_relative_position_bias_table = F.interpolate(
              self.relative_position_bias_table[:-3, :]
              .permute(1, 0)
              .view(
                  1,
                  self.num_heads,
                  2 * self.window_size[0] - 1,
                  2 * self.window_size[1] - 1,
              ),
              size=(
                  2 * training_window_size[0] - 1,
                  2 * training_window_size[1] - 1,
              ),
              mode="bicubic",
              align_corners=False,
          )
          new_relative_position_bias_table = (
              new_relative_position_bias_table.view(
                  self.num_heads, new_num_relative_distance - 3
              ).permute(1, 0)
          )
          new_relative_position_bias_table = torch.cat(
              [
                  new_relative_position_bias_table,
                  self.relative_position_bias_table[-3::],
              ],
              dim=0,
          )
  
          # get pair-wise relative position index for each token inside the window
          coords_h = torch.arange(training_window_size[0])
          coords_w = torch.arange(training_window_size[1])
          coords = torch.stack(
              torch.meshgrid([coords_h, coords_w])
          )  # 2, Wh, Ww
          coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
          relative_coords = (
              coords_flatten[:, :, None] - coords_flatten[:, None, :]
          )  # 2, Wh*Ww, Wh*Ww
          relative_coords = relative_coords.permute(
              1, 2, 0
          ).contiguous()  # Wh*Ww, Wh*Ww, 2
          relative_coords[:, :, 0] += (
              training_window_size[0] - 1
          )  # shift to start from 0
          relative_coords[:, :, 1] += training_window_size[1] - 1
          relative_coords[:, :, 0] *= 2 * training_window_size[1] - 1
          relative_position_index = torch.zeros(
              size=(
                  training_window_size[0] * training_window_size[1] + 1,
              )
              * 2,
              dtype=relative_coords.dtype,
          )
          relative_position_index[1:, 1:] = relative_coords.sum(
              -1
          )  # Wh*Ww, Wh*Ww
          relative_position_index[0, 0:] = new_num_relative_distance - 3
          relative_position_index[0:, 0] = new_num_relative_distance - 2
          relative_position_index[0, 0] = new_num_relative_distance - 1
  
          relative_position_bias = new_relative_position_bias_table[
              relative_position_index.view(-1)
          ].view(
              training_window_size[0] * training_window_size[1] + 1,
              training_window_size[0] * training_window_size[1] + 1,
              -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
  
imports: import warnings
  import math
  import torch
  from functools import partial
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
label_desc: Use image node_id 2 for calling the RelativePositionBias obj's underlying member method code with example usage: obj.forward(training_window_size) and returns: relative_position_bias