"just a bunch of weights and layers and parameters" -- I think you and the GP are agreeing. That's the definition of standard: If the model can be expressed using the currently-blessed set of layer definitions in CoreML, then yes. But if you're doing nonstandard stuff with weird control flow behavior, or RNNs that don't map into some of the common flavors, then all bets are off.

An example: Some of my colleagues put a QP solver in tandem with a DNN, so that the neural network could 'shell out' to the solver as part of its learning, and learned to solve small sudoku problems from examples alone: https://arxiv.org/abs/1703.00443 The pytorch code for it is one of the examples I like to use as a stress-test for doing funky things in the machine learning context.

TensorFlow is a very generic dataflow library at its heart - which happens to have a lot of DNN-specific functionality as ops. It's possible to express arbitrary computations in it, whereas CoreML and and similar frameworks make more assumptions that the computation will fit a particular mould, and optimize it thereby.