So this question came from a stack overflow post 4 of mine; The ultimate goal is to train the parameters of the distribution, without having to worry about
a) the type of distribution used, or
b) how the parameters of the distribution are generated (in this case, the std of the normal distribution is defined in terms of its log to ensure non-negative results)
In short, here this means I do not want to have to do std = e^(log_std) at any point other than the first time I define the distribution.
This is something that will work in Tensorflow, as the graphs are static there. It would seem the easiest way would be to retain some small parts of the graph in PyTorch, but this seems to not be possible. The above was a way of trying to achieve that. @tom If you have any suggestions on this I would greatly appreciate it!