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Runs the ABCDnet NN scaling/inference on whatever backend events are already on (torch device follows ak.backend(events)) instead of always using cuda, so scores come back via ak.from_dlpack with no round trip. Coerces PackedSelection.all() masks (always plain CPU numpy) back onto the events backend before indexing GPU-resident arrays with them. Backend for the initial event materialization is now read from the EWKCOFFEA_BACKEND env var (defaults to cuda) instead of a hardcoded string, so CPU and GPU jobs can run in parallel off the same file. Also adds CHUNK_TIMING instrumentation (per-phase wall time prints) used throughout the CPU vs GPU scaling studies.
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Running VVH analysis on GPU