Result for 01351514E15B890C1AD393AFA3173221C1A929E7

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/numba/targets/__pycache__/arrayobj.cpython-38.pyc
FileSize130713
MD51617FC363CBF388504269A30CDC35AEC
SHA-101351514E15B890C1AD393AFA3173221C1A929E7
SHA-256948DF2A3267024841A845185C2A3775A72F8D931B1309A7B9DDC86A2D703D9A8
SSDEEP3072:bfvEjSCayQIHHo6QQ931Q0UKM3P4OvCj++KXQMDDbCHSphv2EYMsC6DU:7vEjSCayQIo6QQtnUKM3P4OvCj++E9DV
TLSHT1DDD3F7D574B14EAFFC20F2F644EA0292D91BB26723468703741791BA3F50ED83E295AD
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
MD5EA13BEB974A81C11071BEA94F10826E2
PackageArchi586
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-165F50BE666F241E42BC26F21CA19FDA3478A9465
SHA-2563F26FD34FD71B7894A69C0455E85F6DC5DCD14C7787577277A24D0F58EA2172C