The library cannot implement all small code snippets that individual users want, especially not if those are easy to do yourself.

I’m not asking about some random code snippet. I’m asking about functionality to construct a sentence embedding form the token embeddings, which is core requirement for NLU applications. I’m further not asking anyone to implement anything new. I’m asking why would they remove the SequenceSummary class, which already constructs multiple types of sentence embeddings.

a sequence summary is more subjective: it can be seen as a final classification module on top of a model, but then the decision needs to be made how to implement it. Dropout? Activation? Mlp? How many layers? Additional skip connections? Perhaps even just ensemble first? Etc.

I’m not asking about the classification. I’m asking about how to construct the sentence embedding from the token embeddings, which is universal for all these neural language models because their core competency is to produce token embeddings.
The reason I’m asking about all this is that I don’t want to reinvent the wheel. There seems to already exist code to get sentence embeddings from BERT, XLNet, and others, but the code paths are not consistent.