🎨 simplified TypeVar use in ndarray.__add__

Author: jorenham

src/numpy-stubs/__init__.pyi

@@ -646,7 +646,7 @@ _ShapeT_1nd = TypeVar("_ShapeT_1nd", bound=tuple[int, Unpack[tuple[int, ...]]])
 _1NShapeT = TypeVar("_1NShapeT", bound=tuple[L[1], Unpack[tuple[L[1], ...]]])  # (1,) | (1, 1) | (1, 1, 1) | ...
 
 _ScalarT = TypeVar("_ScalarT", bound=generic)
-_ScalarOutT = TypeVar("_ScalarOutT", bound=generic)
+_SelfScalarT = TypeVar("_SelfScalarT", bound=generic)
 _ScalarT_co = TypeVar("_ScalarT_co", bound=generic, default=Any, covariant=True)
 _IntScalarT = TypeVar("_IntScalarT", bound=bool_ | integer | object_)
 _RealNumberT = TypeVar("_RealNumberT", bound=integer | floating)
@@ -2139,17 +2139,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeT_co, _DTypeT_co]):
     @overload
     def __add__(self: NDArray[_ScalarT], x: _nt.Casts[_ScalarT], /) -> NDArray[_ScalarT]: ...  # type: ignore[overload-overlap]
     @overload
-    def __add__(self: NDArray[_ScalarT], x: _nt.CastsWith[_ScalarT, _ScalarOutT], /) -> NDArray[_ScalarOutT]: ...  # type: ignore[overload-overlap]
+    def __add__(self: NDArray[_SelfScalarT], x: _nt.CastsWith[_SelfScalarT, _ScalarT], /) -> NDArray[_ScalarT]: ...  # type: ignore[overload-overlap]
     @overload
-    def __add__(self: _nt.CastsWithBuiltin[_T, _ScalarOutT], x: _nt.SequenceND[_T], /) -> NDArray[_ScalarOutT]: ...
+    def __add__(self: _nt.CastsWithBuiltin[_T, _ScalarT], x: _nt.SequenceND[_T], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __add__(self: _nt.CastsWithInt[_ScalarOutT], x: _nt.SequenceND[_nt.JustInt], /) -> NDArray[_ScalarOutT]: ...
+    def __add__(self: _nt.CastsWithInt[_ScalarT], x: _nt.SequenceND[_nt.JustInt], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __add__(self: _nt.CastsWithFloat[_ScalarOutT], x: _nt.SequenceND[_nt.JustFloat], /) -> NDArray[_ScalarOutT]: ...
+    def __add__(self: _nt.CastsWithFloat[_ScalarT], x: _nt.SequenceND[_nt.JustFloat], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __add__(
-        self: _nt.CastsWithComplex[_ScalarOutT], x: _nt.SequenceND[_nt.JustComplex], /
-    ) -> NDArray[_ScalarOutT]: ...
+    def __add__(self: _nt.CastsWithComplex[_ScalarT], x: _nt.SequenceND[_nt.JustComplex], /) -> NDArray[_ScalarT]: ...
     @overload
     def __add__(self: NDArray[datetime64], x: _nt.CoTimeDelta_nd, /) -> NDArray[datetime64]: ...
     @overload
@@ -2165,19 +2163,15 @@ class ndarray(_ArrayOrScalarCommon, Generic[_ShapeT_co, _DTypeT_co]):
     @overload
     def __radd__(self: NDArray[_ScalarT], x: _nt.Casts[_ScalarT], /) -> NDArray[_ScalarT]: ...  # type: ignore[overload-overlap]
     @overload
-    def __radd__(self: NDArray[_ScalarT], x: _nt.CastsWith[_ScalarT, _ScalarOutT], /) -> NDArray[_ScalarOutT]: ...  # type: ignore[overload-overlap]
+    def __radd__(self: NDArray[_SelfScalarT], x: _nt.CastsWith[_SelfScalarT, _ScalarT], /) -> NDArray[_ScalarT]: ...  # type: ignore[overload-overlap]
     @overload
-    def __radd__(self: _nt.CastsWithBuiltin[_T, _ScalarOutT], x: _nt.SequenceND[_T], /) -> NDArray[_ScalarOutT]: ...
+    def __radd__(self: _nt.CastsWithBuiltin[_T, _ScalarT], x: _nt.SequenceND[_T], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __radd__(self: _nt.CastsWithInt[_ScalarOutT], x: _nt.SequenceND[_nt.JustInt], /) -> NDArray[_ScalarOutT]: ...
+    def __radd__(self: _nt.CastsWithInt[_ScalarT], x: _nt.SequenceND[_nt.JustInt], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __radd__(
-        self: _nt.CastsWithFloat[_ScalarOutT], x: _nt.SequenceND[_nt.JustFloat], /
-    ) -> NDArray[_ScalarOutT]: ...
+    def __radd__(self: _nt.CastsWithFloat[_ScalarT], x: _nt.SequenceND[_nt.JustFloat], /) -> NDArray[_ScalarT]: ...
     @overload
-    def __radd__(
-        self: _nt.CastsWithComplex[_ScalarOutT], x: _nt.SequenceND[_nt.JustComplex], /
-    ) -> NDArray[_ScalarOutT]: ...
+    def __radd__(self: _nt.CastsWithComplex[_ScalarT], x: _nt.SequenceND[_nt.JustComplex], /) -> NDArray[_ScalarT]: ...
     @overload
     def __radd__(self: NDArray[datetime64], x: _nt.CoTimeDelta_nd, /) -> NDArray[datetime64]: ...
     @overload