code: def forward(self):
      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
      return relative_position_bias.permute(2, 0, 1).contiguous()
  
imports: import logging
  from dataclasses import dataclass
  from functools import partial
  from timm.models.vision_transformer import PatchEmbed, Block
  import torch
  import torch.nn
  import numpy
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.wav2vec.wav2vec2 import TransformerSentenceEncoderLayer
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the RelativePositionBias obj's underlying member method code with example usage: obj.forward() and returns: relative_position_bias