New submission from Germán Méndez Bravo :
As the size of a Python project increases, the number of modules and the
complexity of its dependencies increases too, producing two problems in large
codebases: increased risk of import cycles and slow start times due to the
number of modules that
Germán Méndez Bravo added the comment:
I added a pull request with my fix here:
https://github.com/python/cpython/pull/27017
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Python tracker
<https://bugs.python.org/issue41
Change by Germán Méndez Bravo :
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keywords: +patch
pull_requests: +25576
stage: -> patch review
pull_request: https://github.com/python/cpython/pull/27017
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Python tracker
<https://bugs.python.org/issu
Germán Méndez Bravo added the comment:
Nils, unfortunately, fixing the MRO here won’t fix the issue because
`TypedDict.__annotations__` in the class copies the annotations from the parent
classes, and when the type evaluation is made, it’s made using the copied
annotation found in the
Germán Méndez Bravo added the comment:
The way I fixed this is I added `__forward_module__` to `typing.ForwardRef`, so
that it can resolve the forward reference with the same globals as the ones
specified by the module in `__forward_module__`. `TypedDict`'s metaclass should
then pas
Change by Germán Méndez Bravo :
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keywords: +patch
pull_requests: +23506
stage: -> patch review
pull_request: https://github.com/python/cpython/pull/24735
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Python tracker
<https://bugs.python.org/issu
New submission from Germán Méndez Bravo :
A call to `importlib.__import__()` normally locks the import for the module
being worked on; this, however has a performance impact for modules that are
already imported and fully initialized. An example of this are inline
`__import__()` calls in a
