GeezerVison

from_networkx builds its coo arrays directly, but for weights a matrix
cannot hold (strings, objects, ints beyond 64 bits, uniform-length
sequences) it still called nx.to_scipy_sparse_array, only so that scipy
would raise. That made the error depend on scipy: ValueError since 1.15,
a TypeError or a NetworkXError blaming the sparse format before (which
abf6f62 restated as ValueError), and, with scipy not installed, a
ModuleNotFoundError from inside networkx. The fallback never returned a
matrix, so from_networkx otherwise had no use for scipy at all.

Raise the ValueError directly, naming the attribute and what it gave:

    edge weights must be bool, int (at most 64 bits), float or complex
    scalars; attribute 'weight' gives dtype <U1

and drop _from_networkx_via_scipy. The docstring gains a Raises section
for this and for the NetworkXError cases it shares with
nx.to_scipy_sparse_array. Only the message changes for callers with
scipy; callers without it get the ValueError instead of an ImportError.

The rejection tests are folded into one that covers each kind of weight
on all four graph classes, with scipy importable and with it blocked
through sys.modules, so they no longer skip without scipy. The
multigraph scipy-free test blocks scipy the same way instead of
monkeypatching the removed fallback.