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Preserve dtypes and propagate CUDA devices; 10k-sample comparison - #14
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Answers: float32/float64 now preserved end to end (ints promote to float32, labels untouched), CUDA inputs stay on CUDA through fit/predict/transform (was: device-mismatch RuntimeError in nearly every estimator). check_array gains dtype=None preservation. Benchmark scaled to 10k/task with honest numbers incl. the knn-predict gap (brute force ~5s vs ball tree ~0.3s). New tests/common/test_dtype_device.py (15 tests, CUDA cases gated). Local: 335 passed + 1 skipped, pre-commit clean, sphinx 0 warnings. Note: no GPU in CI, so CUDA paths are guarded-not-executed there; MPS called out as CPU-only in docs.