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Automatic differentiation package - tensorplay.autograd Automatic Mixed Precision package - tensorplay.amp Distributed communication package - tensorplay.distributed Index Multiprocessing package - tensorplay.multiprocessing Python Module Index Quantization Search tensorplay tensorplay._stax tensorplay.audio tensorplay.cuda tensorplay.fft tensorplay.futures tensorplay.hub tensorplay.library tensorplay.linalg tensorplay.nn tensorplay.nn.functional tensorplay.nn.init tensorplay.optim tensorplay.random tensorplay.sparse tensorplay.special tensorplay.stax tensorplay.utils.checkpoint tensorplay.utils.data tensorplay.vision

Library tensorplay.library.custom_op tensorplay.library.define tensorplay.library.get_kernel tensorplay.library.impl tensorplay.library.impl_abstract tensorplay.library.infer_schema tensorplay.library.opcheck tensorplay.library.register_autocast tensorplay.library.register_autograd tensorplay.library.register_fake tensorplay.library.register_kernel tensorplay.library.register_vmap tensorplay.library.triton_op tensorplay.library.wrap_triton

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latest (dev)2026-09-08 v1.0.02026-09-06

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tensorplay.library.register_fake

tensorplay.library.register_fake(op: str | CustomOpDef, func: Callable[[...], Any] | None = None, /, *, lib: Any = None, allow_override: bool = True) → Callable[[Callable[[...], Any]], Callable[[...], Any]][source]

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tensorplay.library.register_autograd

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tensorplay.library.register_kernel

Accepts a CustomOpDef or a qualified operator name. device_types=None or an empty iterable means the device-agnostic slot; composite spellings ( Composite… ) map there too. Usable directly or as a decorator.

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