Result for 0097505D137BCFEE4CD8B6442936EA84447D22B8

Query result

Key Value
FileName./usr/lib64/python3.8/site-packages/numba/tests/__pycache__/inlining_usecases.cpython-38.pyc
FileSize1635
MD51987C8CD11F3885D23DD341349FF4DCE
SHA-10097505D137BCFEE4CD8B6442936EA84447D22B8
SHA-2566F01E91730BE2CEB65D5A026A6B40D8A79DC6EEB502CEFFB2499C84CE5FB6336
SSDEEP48:PyIBV/I8v373LlV6t/fb9Mt02hhtNzg6oK:qIB5vLhVgfba02hTBg6oK
TLSHT176319BE2118619EEFF34F3B401D87A317A71926E5F5AA1474B1884AB2C997C82EF511C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5B764D08CFAEE3AF4221DF7215859120B
PackageArchx86_64
PackageDescriptionNumba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageNamepython3-numba
PackageRelease3.3
PackageVersion0.48.0
SHA-19EE3AC165B646510E966FBAF13E4F1739D692098
SHA-256D1B9933DC5C1E5226EECEEAE67919520C41E0CB57825907771AF1433D9450C71