No. The training output is essentially a set of huge matrices, and using the model involves taking your input and those matrices and chaining a lot of matrix multiplications (how many and how big they are depends on the complexity of the model) to get your result. It is just simply not possible to understand that because none of the data has any sort of fixed responsibility or correspondence with specific features.

This is probably not exactly how it works, I'm not a ML guy, just someone who watched some of those "training a model to play a computer game" videos years ago, but it should at the very least be a close enough analogy.