code: def AttentionQKV(
      d_feature,
      n_heads=1,
      dropout=0.0,
      mode="train",
      cache_KV_in_predict=False,
      q_sparsity=None,
      result_sparsity=None,
  ):
      """Returns a layer that maps `(AQ, AK, AV, mask)` to `(new-A, mask)`.
  
      Unlike :py:class:`Attention` above, :py:class:`AttentionQKV` allows the
      incoming activations (`AQ`, `AK`, and `AV`) to come from different sources.
      This is used, for instance, in encoder-decoder attention (Q-related
      activations `AQ` from the decoder, K- and V-related activations -- `AK` and
      `AV` -- from the encoder). Otherwise, see the :py:class:`Attention`
      description for further context/details.
  
      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'``.
        cache_KV_in_predict: Whether to cache K/V arrays in ``'predict'`` mode.
        q_sparsity: Sparsity with which to process queries. If ``None``,
            :py:class:`Dense` is used; if ``'noop'``, no processing is used.
        result_sparsity: Sparsity with which to process result of the attention.
            If ``None``, :py:class:`Dense` is used; if ``'noop'``, no processing is
            used.
      """
  
      def _SparsifiableDense(layer_sparsity):
          if layer_sparsity is None:
              return core.Dense(d_feature)
          elif layer_sparsity == "noop":
              return cb.Serial()  # No-op layer.
          else:
              d_module = d_feature // layer_sparsity
              return cb.Serial(
                  sparsity.FactoredDense(
                      layer_sparsity, d_feature, d_feature
                  ),
                  sparsity.LocallyConvDense(
                      layer_sparsity,
                      d_module,
                      mode=mode,
                      kernel_size=3,
                      length_kernel_size=3,
                  ),
              )
  
      def _CacheableDense():
          if cache_KV_in_predict and mode == "predict":
              return cb.Cache(core.Dense(d_feature))
          else:
              return core.Dense(d_feature)
  
      def _PureAttention():
          return PureAttention(
              n_heads=n_heads, dropout=dropout, mode=mode
          )
  
      return cb.Serial(
          cb.Parallel(
              _SparsifiableDense(q_sparsity),
              _CacheableDense(),
              _CacheableDense(),
          ),
          _PureAttention(),
          _SparsifiableDense(result_sparsity),
      )
  
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 2 for calling a global function with example usage: AttentionQKV(d_feature, n_heads, dropout, mode, cache_KV_in_predict, q_sparsity, result_sparsity) and returns: cb, PureAttention, core, cb, core, cb, cb