I’ll see if I can find someone.

Aside: For embedding-bag the docs describe this a merging “embedding lookup” and “reduce”. But for the sum and mean combiners, isn’t it sufficient to implement this as a sparse tensor (the ids and weights) times a dense matrix (the embedding vectors)? Doesn’t that cover the cases except combiner=max? I think it would be possible to implement an efficient combiner=max if the sparse_segment_* series was complete and included a sparse_segment_max

  
      

      TensorFlow
  

  
    

Migrating feature_columns to TF2's Keras Preprocessing Layers