TYP: Use _Array1D alias in numpy.ma.core.pyi (numpy#28847)

Author: MarcoGorelli

numpy/ma/core.pyi

@@ -226,9 +226,10 @@ _DTypeT = TypeVar("_DTypeT", bound=dtype)
 _DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, covariant=True)
 _ArrayT = TypeVar("_ArrayT", bound=ndarray[Any, Any])
 _ScalarT = TypeVar("_ScalarT", bound=generic)
-_ScalarT_co = TypeVar("_ScalarT_co", bound=generic)
+_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, covariant=True)
 # A subset of `MaskedArray` that can be parametrized w.r.t. `np.generic`
 _MaskedArray: TypeAlias = MaskedArray[_Shape, dtype[_ScalarT]]
+_Array1D: TypeAlias = np.ndarray[tuple[int], np.dtype[_ScalarT]]
 
 MaskType = bool_
 nomask: bool_[Literal[False]]
@@ -1166,9 +1167,9 @@ def sort(
     stable: Literal[False] | None = False,
 ) -> NDArray[Any]: ...
 @overload
-def compressed(x: _ArrayLike[_ScalarT_co]) -> ndarray[tuple[int], dtype[_ScalarT_co]]: ...
+def compressed(x: _ArrayLike[_ScalarT_co]) -> _Array1D[_ScalarT_co]: ...
 @overload
-def compressed(x: ArrayLike) -> ndarray[tuple[int], dtype]: ...
+def compressed(x: ArrayLike) -> _Array1D[Any]: ...
 def concatenate(arrays, axis=...): ...
 def diag(v, k=...): ...
 def left_shift(a, n): ...