I’m confused about overwriting values in pytorch and when you should or should not (link to docs 14)
I would like some general suggestions if anyone has any, but here are a few examples that I’m confused about:
loss += criterion(output, target) #is overwriting loss acceptable?

or
for _ in range(5):
   output = model(input)  #Am I overwriting the output?
   loss += criterion(output, target)
loss.backward()

In the example above, I overwrite the Variable output multiple times in the for loop.

Am I “overwriting values required to compute gradients” with this in-place operation?
Does code like this make the autograd computational graph slower in some way?
What if I was using a Variable with thousands of elements instead of a scalar?

thank you