Key here is, a binary model is just a bag-of-floats with primitively typed inputs and outputs.

It's ~impossible to write up more than what's here because either:

A) you understand reverse engineering and model basics, and thus the current content is clear you'd use Frida to figure out how the arguments are passed to TensorFlow

or

B) you don't understand this is a binary reverse engineering problem, even when shown Frida. If more content was provided, you'd see it as specific to a particular problem. Which it has to be. You'd also need a walkthrough by hand about batching, tokenization, so on and so forth, too much for a write up, and it'd be too confusing to follow for another model.

TL;Dr a request for more content is asking for a reverse engineering article to give you a full education on modal inference