## Possible Issues

* If you have code that accumulates losses across iterations (e.g. to compute an
average at the end of an epoch), such as `total_loss += loss` where `loss` is
your per-iteration loss, you may find increased memory usage in your program.
This is because `loss` probably used to be a Python float (such as when it is
the result of `.sum()`), while it is now a zero-dim Tensor. `total_loss` is
thus accumulating `Tensor`s and their gradient history, which may keep around
large autograd graphs for much longer than necessary. To fix this, be sure to
convert the per-iteration loss to a Python number as soon as possible, for
example with `total_loss += float(loss)`.