class_docstr: Relative Self-Attention similar to Swin V1. Implementation inspired by Timms Swin V1 implementation.
  Args:
      in_channels (`int`):
          Number of input channels.
      num_heads (`int`):
          Number of attention heads. Default 32
      grid_window_size (`Tuple[int, int]`):
          Grid/Window size to be utilized. Default (7, 7)
      attn_drop (`float`):
          Dropout ratio of attention weight. Default: 0.0
      drop (`float`):
          Dropout ratio of output. Default: 0.0
docstr: Returns the relative positional bias.
  Returns:
      relative_position_bias (torch.Tensor): Relative positional bias.
code: def _get_relative_positional_bias(self) -> torch.Tensor:
      """Returns the relative positional bias.
      Returns:
          relative_position_bias (torch.Tensor): Relative positional bias.
      """
      relative_position_bias = self.relative_position_bias_table[
          self.relative_position_index.view(-1)
      ].view(self.attn_area, self.attn_area, -1)
      relative_position_bias = relative_position_bias.permute(
          2, 0, 1
      ).contiguous()
      return relative_position_bias.unsqueeze(0)
  
file_path: towhee/towhee/models/layers/relative_self_attention.py
imports: from typing import Tuple
  from towhee.models.utils.weight_init import trunc_normal_
  from towhee.models.utils.get_relative_position_index import get_relative_position_index
  from torch import nn
  import torch
label_desc: Use image node_id 2 for calling the RelativeSelfAttention obj's underlying member method code with example usage: obj._get_relative_positional_bias() and returns: relative_position_bias