Intended Behaviour in Adjoint Cumsum?

[MothNik]

I'm currently having a hard time understanding the adjoint of the cumsum operator and while trying to understand it, I've encountered something. In line 281 - 283 in tvregdiff.py, the adjoint of the cumsum operator is defined as

def AT(w): return (sum(w) * np.ones(len(w)) -
    np.transpose(np.concatenate(([0.0], np.cumsum(w[:-1])))))

I'm wondering if the effect of

np.transpose(...)

is intended in this context? Since

np.concatenate(([0.0], np.cumsum(w[:-1])))

returns a 1D-vector and NumPy's transpose(...) has no effect on 1D-vectors, nothing will happen here (see also the docs). As example one can call

a = np.arange(0, 10)
np.allclose(a, np.transpose(a))    # evaluates to True because a is 1D
a = a.reshape((-1, 1))
np.allclose(a, np.transpose(a))    # evaluates to False because a is a 2D-column-vector, same applies to row vectors

As I said, I don't quite understand the adjoint of the cumsum, so I would like to ask if this behaviour (nothing happening) is intended or not?

Btw, instead of

sum(w) * np.ones(len(w))

one can use

np.full((len(w), ), fill_value = np.sum(w))

but this is just a very small detail. Thanks and best regards!