code: def __init__(self, window_size, num_heads):
      super().__init__()
      self.window_size = window_size
      self.num_relative_distance = (2 * window_size[0] - 1) * (
          2 * window_size[1] - 1
      ) + 3
      self.relative_position_bias_table = nn.Parameter(
          torch.zeros(self.num_relative_distance, num_heads)
      )
  
      # get pair-wise relative position index for each token inside the window
      coords_h = torch.arange(window_size[0])
      coords_w = torch.arange(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] += (
          window_size[0] - 1
      )  # shift to start from 0
      relative_coords[:, :, 1] += window_size[1] - 1
      relative_coords[:, :, 0] *= 2 * window_size[1] - 1
      relative_position_index = torch.zeros(
          size=(window_size[0] * 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:] = self.num_relative_distance - 3
      relative_position_index[0:, 0] = self.num_relative_distance - 2
      relative_position_index[0, 0] = self.num_relative_distance - 1
  
      self.register_buffer(
          "relative_position_index", relative_position_index
      )
  
imports: import logging
  import math
  import numpy
  import random
  from dataclasses import dataclass, field
  from typing import Optional
  from omegaconf import II
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
label_desc: Use image node_id 1 to create a new RelativePositionBias object from inherited base classes: nn with example: obj = RelativePositionBias(window_size, num_heads)