code: def gen_rel_position(
      data, past_data=None, dtype=np.int32, layout="NT"
  ):
      """Create a matrix of relative position for RelAttentionScoreCell.
  
      The relative position is defined as the index difference: `mem_i` - `query_j`.
      Note, though, that the implementation here makes sense in self-attention's setting,
      but not in cross-attention's. Hence, both `mem_i` and `query_j` are time indices from
      `data` (or, in incremental decoding's case, the concatenated sequence from the current
      stepwise `data` and the previous steps `past_data`).
  
      Parameters
      ----------
      data
          The data. Under incremental decoding, seq_length = 1.
  
          - layout = 'NT'
              Shape (batch_size, seq_length, C)
          - layout = 'TN'
              Shape (seq_length, batch_size, C)
      past_data
          This is only used under incremental decoding. Stacked data from previous steps.
      dtype
          Data type of the mask
      layout
          Layout of the data + past_data
  
      Returns
      -------
      relative_position :
          Shape (query_length, mem_length) where query_length = mem_length = seq_length
      """
      time_axis = 1 if layout == "NT" else 0
      if past_data is None:
          position = npx.arange_like(data, axis=time_axis)
      else:
          # for incremental decoding only, where past data is of the shape:
          # NT(NTK): (B, L_seq, num_heads, n_kv) -> (B, L_seq, inner_dim)
          # TN(TNK): (L_seq, B, num_heads, n_kv) -> (L_seq, B, inner_dim)
          past_data = npx.reshape(past_data, (-2, -2, -5))
          position = npx.arange_like(
              np.concatenate([past_data, data], axis=time_axis),
              axis=time_axis,
          )
      query_position = np.expand_dims(position, axis=-1)
      mem_position = np.expand_dims(position, axis=0)
      relative_position = mem_position - query_position
      return relative_position.astype(np.int32)
  
imports: import math
  import mxnet
  from mxnet import np, npx
  import numpy
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from .op import l2_normalize
  from .layers import SinusoidalPositionalEmbedding, BucketPositionalEmbedding, LearnedPositionalEmbedding
  from typing import Optional
label_desc: Use image node_id 6 for calling a global function with example usage: gen_rel_position(data, past_data, dtype, layout) and returns: relative_position