TYP: Avoid shadowed dtype annotations - #31955
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NumPy numpygh-30754 fixed annotations shadowed by class members under Python 3.14 annotation-scope rules. Later ndarray and MaskedArray signatures reintroduced bare dtype references, which ty resolves to the dtype property and consequently infers Unknown for operations such as argmax(keepdims=True). Use _dtype or np.dtype for the 53 affected annotations and bounds, and move the _dtype alias below the dtype definition. This restores precise ty inference without changing runtime behavior. A follow-up could add ty alongside Pyrefly in the type-checking workflow and check the 166 production .pyi files. With the small existing baseline of final-class, override, parameter-default, and type-argument diagnostics suppressed, invalid-type-form can remain enabled and would catch regressions like this. Follow-up to numpygh-30754.
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Backport of #31951.
PR summary
gh-30754 fixed annotations shadowed by class members under
Python 3.14 annotation-scope rules. Later ndarray and MaskedArray
signatures reintroduced bare dtype references, which the ty type checker resolves to
the dtype property. As a result, it infers Unknown for operations
such as argmax(keepdims=True).
Use _dtype or np.dtype for the 53 affected annotations and bounds, and
move the _dtype alias below the dtype definition. This restores precise
ty inference without changing runtime behavior.
A follow-up could add ty alongside Pyrefly in the type-checking
workflow and check the 166 production .pyi files. With the small
existing baseline of final-class, override, parameter-default, and
type-argument diagnostics suppressed, invalid-type-form can remain
enabled and would catch regressions like this. If a maintainer agrees
that running ty in CI is valuable, I can work on this as a followup.
Follow-up to gh-30754.
First time committer introduction
This is @JelleZijlstra's work account. I have made many contributions to the Python ecosystem though as far as I recall I have not contributed to numpy before. See below for how I came to make this contribution.
AI Disclosure
I was having Codex look for cases where ty inferred less precise types than other type checkers. When it found some issues related to numpy, I had it prepare a fix inside numpy. I personally reviewed all code changes and wrote the final version of the PR body.