Add multi-op batch screening layer; fix cudagraph fallback regressions
run_batch.py drives run_pytest.sh per operator across GPUs (stable-hash sharding, per-op subprocess isolation, process-group timeouts, retry with deterministic-failure cutoff, two-level dtype fallback, .complete resume, per-op REPLAY_FROM). batch_summary.py aggregates run.log tables into summary.csv. ops/ holds the curated assets: dual-repo inventories rebuilt via AST scan + pytest collect verification, shape sets migrated from the old regression harness and merged with upstream core_shapes class-name keys (upstream's set_shapes falls back op_name -> MRO class name -> 1-D DEFAULT_SHAPES, so replacing the shape file without class keys crashes the BLAS family), and a dismiss list where all 76 entries carry verified reasons. Validated end to end: 1036-op full screen with zero failures. Also fix two cudagraph plugin regressions: newer torch appends "enable device-side assertions" to every CUDA error, so the loose fatal-error marker disabled the documented do_bench fallback entirely; and an aborted graph capture can leave the default CUDA RNG generator stuck in capturing state, poisoning every later torch.randn - captures now run under a throwaway RNG state. run_pytest.sh gains an optional DTYPES passthrough.
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