# How to train the embedding of classical data as well as the classifier in TFQ?

I've got some classical training data, and, for the moment, each of these will have its own embedding parameters defining how it is inputted into a certain ansatz. What I'd like is a circuit in two parts:

encoding circuit --> classifier (--> readout)

where the classifier is trained, but is the same for every input where the encoding circuit uses the parameters for each input -- i.e. I want to train the encoding as well as classification.

I can't work out the logistics of how to do this in tfq. I understand the MNIST examples, where each input has a predefined mapping to a quantum state, and then a PQC is trained. There's also the reinforcement learning example where you have a different set of parameters reuploaded between alternating PQC layers, but again this isn't quite the same.

tfq.resolve_parameters looks like it might help me but I'm not really sure how to integrate it into the model code

Any insight appreciated :)