Hi @lgusm ,
Maybe my understanding is wrong but here is what I observe about the situation.

TensorFlow website states that TF2 SavedModelFormat is the preferred way to publish models on TFHub.  Now the problem is that one can load a TF2 SavedModel and modify its layers. But on TFHub it is not possible because it appears that the whole model is further wrapped around a hub.KerasLayer class.  This seems to be a consistency issue if I am not wrong.
The current scenario prevents researchers like me ( I know so many others with similar concerns ) from adopting pre-trained TFHub models and using them in novel settings for experimentation.
Due to points 1) and 2) on many occassions researchers are unfortunately compelled to rewrite their prototype codes in other frameworks where the model sharing ecosystem is more consistent and pre-trained models are easily available.

Especially because of point 1), I think that revamping the hub.KerasLayer class could be a good idea as it will allow people not only to use pretrained models in simplistic settings of standard finetuning and feature extraction, but would also allow people like me to use them in more research settings.