code: def Attention(d_feature, n_heads=1, dropout=0.0, mode="train"):
      """Returns a layer that maps `(vectors, mask)` to `(new_vectors, mask)`.
  
      This layer type represents one pass of multi-head self-attention, from vector
      set to vector set, using masks to represent out-of-bound (e.g., padding)
      positions. It:
  
        - makes three copies of incoming activations and maps these to multi-head
          query (Q) vectors, key (K) vectors, and value (V) vectors, respectively;
        - for each head, computes the scaled dot product of each Q-K pair;
        - applies mask to screen out positions that come from padding tokens
          (indicated by 0 value);
        - [in ``'train'`` mode] applies dropout to Q-K dot products;
        - for each head, computes Q-K attention strengths using a per-query softmax
          of the Q-K dot products;
        - for each head, for each query position, combines V vectors according
          to the Q-K attention strengths; and
        - concatenates and fuses resulting per-head vectors into outgoing
          activations matching original input activation shapes.
  
      Args:
        d_feature: Last/innermost dimension of activations in the input to and
            output from this layer.
        n_heads: Number of attention heads. Attention heads effectively split
            activation vectors into ``n_heads`` subvectors, of size
            ``d_feature / n_heads``.
        dropout: Probababilistic rate for attention dropout, which overrides
            (sets to zero) some attention strengths derived from query-key
            matching. As a result, on a given forward pass, some value vectors
            don't contribute to the output, analogous to how regular dropout can
            cause some node activations to be ignored. Applies only if layer is
            created in ``'train'`` mode.
        mode: One of ``'train'``, ``'eval'``, or ``'predict'``.
      """
      return cb.Serial(
          cb.Select([0, 0, 0]),
          AttentionQKV(
              d_feature, n_heads=n_heads, dropout=dropout, mode=mode
          ),
      )
  
imports: import numpy
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import combinators
  from trax.layers import convolution
  from trax.layers import core
  from trax.layers import initializers
  from trax.layers.assert_shape import assert_shape
  from trax.layers.base import Fn
  from trax.layers.research import sparsity
label_desc: Use image node_id 1 for calling a global function with example usage: Attention(d_feature, n_heads, dropout, mode) and returns: cb