@ptrblck nails it with his comment.
We also do have a short discussion in the Deep Learning with PyTorch book 62 in chapter 8, similar to the brief mention in slide 54 for a course I taught earlier this month 35 - copied here for your convenience:
Some thoughts on functional vs. Module

If you write for re-use, the functional / Module split of PyTorch has turned out
to be a good idea.
Use functional for stuff without state (unless you have a quick and dirty Sequential).
Never re-use modules (define one torch.nn.ReLU and use it 5 times). It’s a trap!
When doing analysis or quantization (when ReLU becomes stateful due to quantization params), this will break.

The latter two seem to cover your case here, but 2. is more a matter of personal preference, while 3. is really about writing better (as in less risky, more clear) code.
Item 1. ist more is more for when you write a library or something you expect to be re-used a lot.
Best regards
Thomas