[lkois]

So porting between ML frameworks was my job for a while, and the short answer is Yes, common layers can be quite simple to describe and reproduce in different frameworks. eg "Conv2D(2,3)" is enough info, in code or text, to describe a 2d convolution layer with 2 outputs and a shape of 3x3.

The longer answer is that the rest of the Conv2D configuration can then be easily overlooked, unless changed from the defaults. And those can be different across frameworks and potentially break things, even they even exist in your preferred framework. You can always create custom layers though, if needed.

But many papers also seem to do a bad job describing the actual structure of their own ML network. They can be vague, confusing, or simply inaccurate. And that can be because they are a general concept with flexible details, or because they struggle to put their model into clear words and diagrams. Or simply because they know the code is going to do the lifting.