conv1_v2 won’t show up in the parameters, as it’s basically just pointing to conv1.
The underlying layer will be trained, since conv1 is in the parameters.
If you compare the gradients of conv1 and conv1_v2, you’ll see that they are identical.
Would that work for your use case?
If not, could you explain your use case a bit?