code: def _get_relative_position_bias(
      relative_position_bias_table: torch.Tensor,
      relative_position_index: torch.Tensor,
      window_size: List[int],
  ) -> torch.Tensor:
      N = window_size[0] * window_size[1]
      relative_position_bias = relative_position_bias_table[relative_position_index]  # type: ignore[index]
      relative_position_bias = relative_position_bias.view(N, N, -1)
      relative_position_bias = (
          relative_position_bias.permute(2, 0, 1)
          .contiguous()
          .unsqueeze(0)
      )
      return relative_position_bias
  
imports: import math
  from functools import partial
  from typing import Any, Callable, List, Optional
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..ops.misc import MLP, Permute
  from ..ops.stochastic_depth import StochasticDepth
  from ..transforms._presets import ImageClassification, InterpolationMode
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 2 for calling a global function with example usage: _get_relative_position_bias(relative_position_bias_table, relative_position_index, window_size) and returns: relative_position_bias