No particular reason â€“ your T is a 3d tensor so the two versions are
equivalent. I suppose that using â€śnegativeâ€ť dimensions emphasizes
that weâ€™re extracting the diagonals from the 2d matrices made up by
the last two dimensions of T (so that this version would generalize to a
hypothetical use case where T had multiple leading â€śbatchâ€ť dimensions
such as T of shape [batch_size, channel_size, size_n, size_n]).

Itâ€™s really just stylistic â€“ and not necessarily a better style.