code: def relative_position_bucket(
      relative_position,
      bidirectional: bool = True,
      num_buckets: int = 32,
      max_distance: int = 128,
  ):
      """Map the relative position to buckets.
  
      The major difference between our implementation and
      that in [mesh_tensorflow](https://github.com/tensorflow/mesh/blob/c59988047e49b4d2af05603e3170724cdbadc467/mesh_tensorflow/transformer/transformer_layers.py#L595-L637)
      is that we use 'query_i - mem_j' as the (i, j)-th location in relative_position.
      Thus, a positive value means that the query slot is in a later timestamp than the memory slot.
      However, in mesh transformer, it is treated as `mem_i - query_j` (reversed).
      The implementation uses the first half of the bucket (num_buckets // 2) to store the
      exact increments in positions and the second half of the bucket
      (num_buckets - num_buckets // 2) to store the bucketing values in the logarithm order.
  
      Parameters
      ----------
      relative_position
          Shape (...,)
      bidirectional
          Whether we are dealing with bidirectional attention.
          If it's bidirectional, we will use the first half to map the positions of the
          positive shifts and the second half to map the positions of the negative shifts.
      num_buckets
          The number of buckets.
      max_distance
          Maximum distance. Positions that fall outside of 'max_distance' will be trimmed.
  
      Returns
      -------
      buckets
          Shape (...,).
          It has the same shape as the `relative_position`. It will have int32 type.
      """
      ret = 0
  
      if bidirectional:
          assert num_buckets % 2 == 0, (
              "When bidirectional is True, the number of buckets must be "
              "divisible by 2."
          )
          num_buckets //= 2
          ret = (
              ret
              + (relative_position < 0).astype(th.int32) * num_buckets
          )
          relative_position = th.abs(relative_position)
      else:
          # Clip all the negative values to 0
          relative_position = th.clip(
              relative_position, min=0, max=None
          )
      # Now, the relative_position is in the range [0, inf)
  
      # Half of the buckets deal with the exact increments,
      # i.e., 0, 1, 2, ..., max_exact - 1, where max_exact = num_buckets // 2
      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
      val_if_large = max_exact + (
          th.log(relative_position.type(th.float32) / max_exact)
          / math.log(max_distance / max_exact)
          * (num_buckets - max_exact)
      ).astype(th.int32)
      val_if_large = th.minimum(
          val_if_large, th.tensor(num_buckets - 1)
      )
      ret = ret + th.where(is_small, relative_position, val_if_large)
      return ret
  
imports: import math
  from collections import OrderedDict
  import torch
  import numpy
  from torch import nn
  from typing import Union
  from .utils import to_torch_dtype
label_desc: Use image node_id 2 for calling a global function with example usage: relative_position_bucket(relative_position, bidirectional, num_buckets, max_distance) and returns: ret