openKylin python-wrapt package

python-wrapt 2.1.1-ok1 source package in openKylin

python-wrapt (2.1.1-ok1) huanghe; urgency=medium

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Series	Pocket	Published	Component	Section
Huanghe V3.0	proposed	on 2026-06-02	main	python

File	Size	SHA-256 Checksum
python-wrapt_2.1.1.orig.tar.xz	141.2 KiB	76d3dac3e05b59c3837dfa0486d077e3fd5b5641e289db0de57236b0b7c8c244
python-wrapt_2.1.1-ok1.debian.tar.xz	4.6 KiB	30db2bc858f6d4c04f8a0dff6f5079a8f53561c5041d165bb5e06b139cdbb41c
python-wrapt_2.1.1-ok1.dsc	2.0 KiB	968eccfaafae37778b6e0064e7873cdea8c81439f764bcee6420329218da17d8

python-wrapt-doc: decorators, wrappers and monkey patching - doc: The aim of the wrapt module is to provide a transparent object proxy for
Python, which can be used as the basis for the construction of function
wrappers and decorator functions.
.
The wrapt module focuses very much on correctness. It therefore goes way
beyond existing mechanisms such as functools.wraps() to ensure that decorators
preserve introspectability, signatures, type checking abilities etc. The
decorators that can be constructed using this module will work in far more
scenarios than typical decorators and provide more predictable and consistent
behaviour.
.
To ensure that the overhead is as minimal as possible, a C extension module is
used for performance critical components. An automatic fallback to a pure
Python implementation is also provided where a target system does not have a
compiler to allow the C extension to be compiled.
.
This package contains the documentation.

python3-wrapt: decorators, wrappers and monkey patching: The aim of the wrapt module is to provide a transparent object proxy for
Python, which can be used as the basis for the construction of function
wrappers and decorator functions.
.
The wrapt module focuses very much on correctness. It therefore goes way
beyond existing mechanisms such as functools.wraps() to ensure that decorators
preserve introspectability, signatures, type checking abilities etc. The
decorators that can be constructed using this module will work in far more
scenarios than typical decorators and provide more predictable and consistent
behaviour.
.
To ensure that the overhead is as minimal as possible, a C extension module is
used for performance critical components. An automatic fallback to a pure
Python implementation is also provided where a target system does not have a
compiler to allow the C extension to be compiled.