code: def _get_rel_pos_bias(
      seq_len,
      max_len=128,
      num_buckets=32,
      bidirectional=True,
      reset=True,
  ):
      # max_len = 520
      pos = np.array(range(seq_len))
      rel_pos = pos[:, None] - pos[None, :]
      ret = 0
      n = -rel_pos
      if bidirectional:
          num_buckets //= 2
          ret += (n < 0).astype(
              "int32"
          ) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
          n = np.abs(n)
      else:
          n = np.max(n, np.zeros_like(n))
      # now n is in the range [0, inf)
  
      # half of the buckets are for exact increments in positions
      max_exact = num_buckets // 2
      is_small = n < max_exact
      # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
      val_if_large = max_exact + (
          np.log(n.astype("float32") / max_exact)
          / math.log(max_len / max_exact)
          * (num_buckets - max_exact)
      ).astype("int32")
      tmp = np.full_like(val_if_large, num_buckets - 1)
      val_if_large = np.where(val_if_large < tmp, val_if_large, tmp)
  
      ret += np.where(is_small, n, val_if_large)
      if reset:
          num_buckets *= 2
          ret[:, 0] = num_buckets
          ret[0, :] = num_buckets // 2
  
      return np.array(ret).reshape([seq_len, seq_len]).astype("int64")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import json
  import logging
  import math
  import six
  import numpy
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from disco_diffusion_ernievil_base.vit_b_16x.ernievil2.transformers.file_utils import _fetch_from_remote, add_docstring
label_desc: Use image node_id 1 for calling a global function with example usage: _get_rel_pos_bias(seq_len, max_len, num_buckets, bidirectional, reset) and returns: np