jrmccluskey commented on issue #22572:
URL: https://github.com/apache/beam/issues/22572#issuecomment-1239527137

   Looking into this a little bit, it's doable for each handler type but the 
end result is somewhat restrictive for the user. The provided function is going 
to have to take the same arguments in the same position as the current 
inference methods. For the given examples discussed this isn't a huge issue 
(unless HuggingFace users really want to use the 30+ optional `generate()` 
parameters) and will likely cover a large number of use cases, but we'll still 
have some advanced users who will want more tuning and will likely turn to 
bespoke options.
   
   It also looks like providing the alternate inference function will need to 
be done at run_inference call-time, not handler init-time, since the 
scikit-learn and PyTorch approaches are using functions from specific instances 
of their respective models. Can't specify the function until you have the 
model, unless I'm missing something. 


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