code: def relative_position_bucket(
      relative_position,
      bidirectional=True,
      num_buckets=32,
      max_distance=128,
  ):
      """
      Adapted from Mesh Tensorflow:
      https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
      Translate relative position to a bucket number for relative attention. The relative position is defined as
      memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
      position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
      small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
      positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
      This should allow for more graceful generalization to longer sequences than the model has been trained on
      Args:
          relative_position: an int32 Tensor
          bidirectional: a boolean - whether the attention is bidirectional
          num_buckets: an integer
          max_distance: an integer
      Returns:
          a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
      """
      relative_buckets = 0
      if bidirectional:
          num_buckets //= 2
          relative_buckets += (relative_position > 0).to(
              torch.long
          ) * num_buckets
          relative_position = torch.abs(relative_position)
      else:
          # we want the input relative_position to be negative in this case
          relative_position = -torch.min(
              relative_position, torch.zeros_like(relative_position)
          )
      # now relative_position is in the range [0, inf)
  
      # half of the buckets are for exact increments in positions
      max_exact = num_buckets // 2
      is_small = relative_position < max_exact
  
      # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
      relative_position_if_large = max_exact + (
          torch.log(relative_position.float() / max_exact)
          / math.log(max_distance / max_exact)
          * (num_buckets - max_exact)
      ).to(torch.long)
      relative_position_if_large = torch.min(
          relative_position_if_large,
          torch.full_like(relative_position_if_large, num_buckets - 1),
      )
  
      relative_buckets += torch.where(
          is_small, relative_position, relative_position_if_large
      )
      return relative_buckets
  
imports: import sys
  import os
  from os import makedirs
  from os.path import join
  import numpy
  import pyhocon
  import math
  import logging
  import random
  from typing import Optional, Tuple, Any, Dict, Iterable, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from transformers.activations import ACT2FN
label_desc: Use image node_id 5 for calling a global function with example usage: relative_position_bucket(relative_position, bidirectional, num_buckets, max_distance) and returns: relative_buckets