code: def RelativeAttentionLayer(
      d_feature,
      context_bias_layer,
      location_bias_layer,
      total_kv_pooling,
      separate_cls,
      n_heads=1,
      dropout=0.0,
      mode="train",
  ):
      """Returns a layer that maps (q, k, v, masks) to (activations, masks).
  
      When number of keys is smaller than number of queries layer works in O(q^2*d).
      Otherwise it is O(q*k*d). That is because we need to shift relative distances
      by current_pooling. When we upsample this is current pooling is a fraction < 1
      Visual explanation:
      [01][23][45][67] -> [0][1][2][3][4][5][6][7]
      For token [0] we calculate relative distances as follows:
      * 0 2 4 6
      However for token [1] we need relative distances changed by 1, specifically:
      * -1 1 3 5
      So we not only need to calculate the distances that corresponds to spacing
      between the keys but also for the ones in between because there are more than
      one query tokens (on different positions which means different relative
      distances) for single key token.
  
      Args:
        d_feature: Depth/dimensionality of feature embedding.
        context_bias_layer: Global context bias from Transformer XL's attention.
          There should be one such layer shared for all relative attention layers
        location_bias_layer: Global location bias from Transformer XL's attention.
          There should be one such layer shared for all relative attention layers.
        total_kv_pooling: Accumulated pool size of keys/values used at this layer
        separate_cls: True/False if we separate_cls in calculations.
  
        n_heads: Number of attention heads.
        dropout: Probabilistic rate for internal dropout applied to attention
            activations (based on query-key pairs) before dotting them with values.
        mode: One of `'train'`, `'eval'`, or `'predict'`.
      """
  
      return cb.Serial(
          cb.Branch(
              PositionalEmbeddings(
                  d_feature, separate_cls, total_kv_pooling
              ),
              cb.Select([0]),
              cb.Select([1]),
          ),
          cb.Parallel(
              core.Dense(d_feature),
              core.Dense(d_feature),
              core.Dense(d_feature),
              core.Dense(d_feature),
          ),
          context_bias_layer,
          location_bias_layer,
          RelativeAttention(  # pylint: disable=no-value-for-parameter
              separate_cls=separate_cls,
              n_heads=n_heads,
              dropout=dropout,
              mode=mode,
          ),
          core.Dense(d_feature),
      )
  
imports: from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.attention import MergeHeads
  from trax.layers.attention import SplitIntoHeads
label_desc: Use image node_id 3 for calling a global function with example usage: RelativeAttentionLayer(d_feature, context_bias_layer, location_bias_layer, total_kv_pooling, separate_cls, n_heads, dropout, mode) and returns: cb