How to make combined mlmodel based on seperate mlmodels? adding if-else control flow

## ❓Question

I want to use coremltools to help accomplish combining some separate coreml models to form a bigger one.
For example, **I have models: main.mlmodel, if_branch.mlmodel, else_branch.mlmodel**, each have so many layers.
I want to make another mlmodel based on those three. And the flow should be like this:
Inputs include input to "main" and a condition input "cond".
Input feed to "main", then output of it will go to either "if_branch" or "else_branch" based on the other input "cond".

I tried using "NeuralNetworkBuilder" to construct the bigger model. But when constructing the if layer, what kind of method(api) should I use to move all layers in "if_branch.mlmodel" to the "ifbranch" of the if layer instead of rewriting each layer into "ifbranch"?

Start by reviewing the NeuralNetworkBuilder usage and the if-layer construction described in the issue, then inspect how the three separate .mlmodel files are represented. Determine whether existing model-composition support can place complete branch models into the conditional flow without rewriting their layers; done means documenting a supported approach or limitation.

Written by the indexing model from the issue text.