Merge pull request #18915 from BvB93/unsafe

MAINT: Remove unsafe unions and ABCs from return-annotations

11 files changed, 533 insertions, 615 deletions

diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi
index 9435c27f3..5146a11d5 100644
--- a/numpy/__init__.pyi
+++ b/numpy/__init__.pyi
@@ -1048,14 +1048,21 @@ class _ArrayOrScalarCommon:
     def __array_wrap__(array, context=...): ...
     def __setstate__(self, __state): ...
     # a `bool_` is returned when `keepdims=True` and `self` is a 0d array
+
     @overload
     def all(
-        self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...
+        self,
+        axis: None = ...,
+        out: None = ...,
+        keepdims: Literal[False] = ...,
     ) -> bool_: ...
     @overload
     def all(
-        self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...
-    ) -> Union[bool_, ndarray]: ...
+        self,
+        axis: Optional[_ShapeLike] = ...,
+        out: None = ...,
+        keepdims: bool = ...,
+    ) -> Any: ...
     @overload
     def all(
         self,
@@ -1063,14 +1070,21 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         keepdims: bool = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
     def any(
-        self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...
+        self,
+        axis: None = ...,
+        out: None = ...,
+        keepdims: Literal[False] = ...,
     ) -> bool_: ...
     @overload
     def any(
-        self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...
-    ) -> Union[bool_, ndarray]: ...
+        self,
+        axis: Optional[_ShapeLike] = ...,
+        out: None = ...,
+        keepdims: bool = ...,
+    ) -> Any: ...
     @overload
     def any(
         self,
@@ -1078,40 +1092,67 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         keepdims: bool = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
-    def argmax(self, axis: None = ..., out: None = ...) -> signedinteger: ...
+    def argmax(
+        self,
+        axis: None = ...,
+        out: None = ...,
+    ) -> signedinteger[Any]: ...
     @overload
     def argmax(
-        self, axis: _ShapeLike = ..., out: None = ...
-    ) -> Union[signedinteger, ndarray]: ...
+        self,
+        axis: _ShapeLike = ...,
+        out: None = ...,
+    ) -> Any: ...
     @overload
     def argmax(
-        self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ...
+        self,
+        axis: Optional[_ShapeLike] = ...,
+        out: _NdArraySubClass = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
-    def argmin(self, axis: None = ..., out: None = ...) -> signedinteger: ...
+    def argmin(
+        self,
+        axis: None = ...,
+        out: None = ...,
+    ) -> signedinteger[Any]: ...
     @overload
     def argmin(
-        self, axis: _ShapeLike = ..., out: None = ...
-    ) -> Union[signedinteger, ndarray]: ...
+        self,
+        axis: _ShapeLike = ...,
+         out: None = ...,
+    ) -> Any: ...
     @overload
     def argmin(
-        self, axis: Optional[_ShapeLike] = ..., out: _NdArraySubClass = ...
+        self,
+        axis: Optional[_ShapeLike] = ...,
+        out: _NdArraySubClass = ...,
     ) -> _NdArraySubClass: ...
+
     def argsort(
         self,
         axis: Optional[SupportsIndex] = ...,
         kind: Optional[_SortKind] = ...,
         order: Union[None, str, Sequence[str]] = ...,
     ) -> ndarray: ...
+
     @overload
     def choose(
-        self, choices: ArrayLike, out: None = ..., mode: _ModeKind = ...,
+        self,
+        choices: ArrayLike,
+        out: None = ...,
+        mode: _ModeKind = ...,
     ) -> ndarray: ...
     @overload
     def choose(
-        self, choices: ArrayLike, out: _NdArraySubClass = ..., mode: _ModeKind = ...,
+        self,
+        choices: ArrayLike,
+        out: _NdArraySubClass = ...,
+        mode: _ModeKind = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
     def clip(
         self,
@@ -1119,7 +1160,7 @@ class _ArrayOrScalarCommon:
         max: Optional[ArrayLike] = ...,
         out: None = ...,
         **kwargs: Any,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def clip(
         self,
@@ -1127,7 +1168,7 @@ class _ArrayOrScalarCommon:
         max: ArrayLike = ...,
         out: None = ...,
         **kwargs: Any,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def clip(
         self,
@@ -1144,6 +1185,7 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         **kwargs: Any,
     ) -> _NdArraySubClass: ...
+
     @overload
     def compress(
         self,
@@ -1158,8 +1200,11 @@ class _ArrayOrScalarCommon:
         axis: Optional[SupportsIndex] = ...,
         out: _NdArraySubClass = ...,
     ) -> _NdArraySubClass: ...
+
     def conj(self: _ArraySelf) -> _ArraySelf: ...
+
     def conjugate(self: _ArraySelf) -> _ArraySelf: ...
+
     @overload
     def cumprod(
         self,
@@ -1174,6 +1219,7 @@ class _ArrayOrScalarCommon:
         dtype: DTypeLike = ...,
         out: _NdArraySubClass = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
     def cumsum(
         self,
@@ -1188,15 +1234,7 @@ class _ArrayOrScalarCommon:
         dtype: DTypeLike = ...,
         out: _NdArraySubClass = ...,
     ) -> _NdArraySubClass: ...
-    @overload
-    def max(
-        self,
-        axis: None = ...,
-        out: None = ...,
-        keepdims: Literal[False] = ...,
-        initial: _NumberLike = ...,
-        where: _ArrayLikeBool = ...,
-    ) -> number: ...
+
     @overload
     def max(
         self,
@@ -1205,7 +1243,7 @@ class _ArrayOrScalarCommon:
         keepdims: bool = ...,
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def max(
         self,
@@ -1215,14 +1253,7 @@ class _ArrayOrScalarCommon:
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
     ) -> _NdArraySubClass: ...
-    @overload
-    def mean(
-        self,
-        axis: None = ...,
-        dtype: DTypeLike = ...,
-        out: None = ...,
-        keepdims: Literal[False] = ...,
-    ) -> number: ...
+
     @overload
     def mean(
         self,
@@ -1230,7 +1261,7 @@ class _ArrayOrScalarCommon:
         dtype: DTypeLike = ...,
         out: None = ...,
         keepdims: bool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def mean(
         self,
@@ -1239,15 +1270,7 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         keepdims: bool = ...,
     ) -> _NdArraySubClass: ...
-    @overload
-    def min(
-        self,
-        axis: None = ...,
-        out: None = ...,
-        keepdims: Literal[False] = ...,
-        initial: _NumberLike = ...,
-        where: _ArrayLikeBool = ...,
-    ) -> number: ...
+
     @overload
     def min(
         self,
@@ -1256,7 +1279,7 @@ class _ArrayOrScalarCommon:
         keepdims: bool = ...,
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def min(
         self,
@@ -1266,17 +1289,12 @@ class _ArrayOrScalarCommon:
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
     ) -> _NdArraySubClass: ...
-    def newbyteorder(self: _ArraySelf, __new_order: _ByteOrder = ...) -> _ArraySelf: ...
-    @overload
-    def prod(
-        self,
-        axis: None = ...,
-        dtype: DTypeLike = ...,
-        out: None = ...,
-        keepdims: Literal[False] = ...,
-        initial: _NumberLike = ...,
-        where: _ArrayLikeBool = ...,
-    ) -> number: ...
+
+    def newbyteorder(
+        self: _ArraySelf,
+        __new_order: _ByteOrder = ...,
+    ) -> _ArraySelf: ...
+
     @overload
     def prod(
         self,
@@ -1286,7 +1304,7 @@ class _ArrayOrScalarCommon:
         keepdims: bool = ...,
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def prod(
         self,
@@ -1297,14 +1315,14 @@ class _ArrayOrScalarCommon:
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
     def ptp(
-        self, axis: None = ..., out: None = ..., keepdims: Literal[False] = ...,
-    ) -> number: ...
-    @overload
-    def ptp(
-        self, axis: Optional[_ShapeLike] = ..., out: None = ..., keepdims: bool = ...,
-    ) -> Union[number, ndarray]: ...
+        self,
+        axis: Optional[_ShapeLike] = ...,
+        out: None = ...,
+        keepdims: bool = ...,
+    ) -> Any: ...
     @overload
     def ptp(
         self,
@@ -1312,26 +1330,26 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         keepdims: bool = ...,
     ) -> _NdArraySubClass: ...
+
     def repeat(
-        self, repeats: _ArrayLikeIntOrBool, axis: Optional[SupportsIndex] = ...
+        self,
+        repeats: _ArrayLikeIntOrBool,
+        axis: Optional[SupportsIndex] = ...,
     ) -> ndarray: ...
+
     @overload
     def round(
-        self: _ArraySelf, decimals: SupportsIndex = ..., out: None = ...
+        self: _ArraySelf,
+        decimals: SupportsIndex = ...,
+        out: None = ...,
     ) -> _ArraySelf: ...
     @overload
     def round(
-        self, decimals: SupportsIndex = ..., out: _NdArraySubClass = ...
-    ) -> _NdArraySubClass: ...
-    @overload
-    def std(
         self,
-        axis: None = ...,
-        dtype: DTypeLike = ...,
-        out: None = ...,
-        ddof: int = ...,
-        keepdims: Literal[False] = ...,
-    ) -> number: ...
+        decimals: SupportsIndex = ...,
+        out: _NdArraySubClass = ...,
+    ) -> _NdArraySubClass: ...
+
     @overload
     def std(
         self,
@@ -1340,7 +1358,7 @@ class _ArrayOrScalarCommon:
         out: None = ...,
         ddof: int = ...,
         keepdims: bool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def std(
         self,
@@ -1350,16 +1368,7 @@ class _ArrayOrScalarCommon:
         ddof: int = ...,
         keepdims: bool = ...,
     ) -> _NdArraySubClass: ...
-    @overload
-    def sum(
-        self,
-        axis: None = ...,
-        dtype: DTypeLike = ...,
-        out: None = ...,
-        keepdims: Literal[False] = ...,
-        initial: _NumberLike = ...,
-        where: _ArrayLikeBool = ...,
-    ) -> number: ...
+
     @overload
     def sum(
         self,
@@ -1369,7 +1378,7 @@ class _ArrayOrScalarCommon:
         keepdims: bool = ...,
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def sum(
         self,
@@ -1380,6 +1389,7 @@ class _ArrayOrScalarCommon:
         initial: _NumberLike = ...,
         where: _ArrayLikeBool = ...,
     ) -> _NdArraySubClass: ...
+
     @overload
     def take(
         self,
@@ -1387,7 +1397,7 @@ class _ArrayOrScalarCommon:
         axis: Optional[SupportsIndex] = ...,
         out: None = ...,
         mode: _ModeKind = ...,
-    ) -> generic: ...
+    ) -> Any: ...
     @overload
     def take(
         self,
@@ -1404,15 +1414,7 @@ class _ArrayOrScalarCommon:
         out: _NdArraySubClass = ...,
         mode: _ModeKind = ...,
     ) -> _NdArraySubClass: ...
-    @overload
-    def var(
-        self,
-        axis: None = ...,
-        dtype: DTypeLike = ...,
-        out: None = ...,
-        ddof: int = ...,
-        keepdims: Literal[False] = ...,
-    ) -> number: ...
+
     @overload
     def var(
         self,
@@ -1421,7 +1423,7 @@ class _ArrayOrScalarCommon:
         out: None = ...,
         ddof: int = ...,
         keepdims: bool = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def var(
         self,
@@ -1512,7 +1514,7 @@ class ndarray(_ArrayOrScalarCommon, Iterable, Sized, Container):
         axis2: SupportsIndex = ...,
     ) -> _ArraySelf: ...
     @overload
-    def dot(self, b: ArrayLike, out: None = ...) -> Union[number, ndarray]: ...
+    def dot(self, b: ArrayLike, out: None = ...) -> Any: ...
     @overload
     def dot(self, b: ArrayLike, out: _NdArraySubClass = ...) -> _NdArraySubClass: ...
     # `nonzero()` is deprecated for 0d arrays/generics
@@ -1552,7 +1554,7 @@ class ndarray(_ArrayOrScalarCommon, Iterable, Sized, Container):
         axis2: SupportsIndex = ...,
         dtype: DTypeLike = ...,
         out: None = ...,
-    ) -> Union[number, ndarray]: ...
+    ) -> Any: ...
     @overload
     def trace(
         self,  # >= 2D array
diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi
index 66eb3bfb8..f61d9b7ae 100644
--- a/numpy/core/fromnumeric.pyi
+++ b/numpy/core/fromnumeric.pyi
@@ -4,8 +4,7 @@ from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar
 
 from numpy import (
     ndarray,
-    number,
-    integer,
+    signedinteger,
     bool_,
     generic,
     _OrderKACF,
@@ -22,8 +21,6 @@ from numpy.typing import (
     ArrayLike,
     _ShapeLike,
     _Shape,
-    _IntLike,
-    _BoolLike,
     _NumberLike,
 )
 
@@ -42,80 +39,58 @@ _ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, comp
 _Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
 
 # Integers and booleans can generally be used interchangeably
-_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
 _ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
-_ScalarGenericDT = TypeVar(
-    "_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
-)
-
-_Number = TypeVar("_Number", bound=number)
 
 # The signature of take() follows a common theme with its overloads:
 # 1. A generic comes in; the same generic comes out
 # 2. A scalar comes in; a generic comes out
 # 3. An array-like object comes in; some keyword ensures that a generic comes out
 # 4. An array-like object comes in; an ndarray or generic comes out
-@overload
-def take(
-    a: _ScalarGenericDT,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _ModeKind = ...,
-) -> _ScalarGenericDT: ...
-@overload
-def take(
-    a: _Scalar,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _ModeKind = ...,
-) -> _ScalarNumpy: ...
-@overload
-def take(
-    a: ArrayLike,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _ModeKind = ...,
-) -> _ScalarNumpy: ...
-@overload
 def take(
     a: ArrayLike,
     indices: _ArrayLikeIntOrBool,
     axis: Optional[int] = ...,
     out: Optional[ndarray] = ...,
     mode: _ModeKind = ...,
-) -> Union[_ScalarNumpy, ndarray]: ...
-def reshape(a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ...) -> ndarray: ...
-@overload
-def choose(
-    a: _ScalarIntOrBool,
-    choices: ArrayLike,
-    out: Optional[ndarray] = ...,
-    mode: _ModeKind = ...,
-) -> _ScalarIntOrBool: ...
-@overload
-def choose(
-    a: Union[_IntLike, _BoolLike], choices: ArrayLike, out: Optional[ndarray] = ..., mode: _ModeKind = ...
-) -> Union[integer, bool_]: ...
-@overload
+) -> Any: ...
+
+def reshape(
+    a: ArrayLike,
+    newshape: _ShapeLike,
+    order: _OrderACF = ...,
+) -> ndarray: ...
+
 def choose(
     a: _ArrayLikeIntOrBool,
     choices: ArrayLike,
     out: Optional[ndarray] = ...,
     mode: _ModeKind = ...,
-) -> ndarray: ...
+) -> Any: ...
+
 def repeat(
-    a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
+    a: ArrayLike,
+    repeats: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
 ) -> ndarray: ...
+
 def put(
-    a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _ModeKind = ...
+    a: ndarray,
+    ind: _ArrayLikeIntOrBool,
+    v: ArrayLike,
+    mode: _ModeKind = ...,
 ) -> None: ...
-def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> ndarray: ...
+
+def swapaxes(
+    a: ArrayLike,
+    axis1: int,
+    axis2: int,
+) -> ndarray: ...
+
 def transpose(
-    a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ...
+    a: ArrayLike,
+    axes: Union[None, Sequence[int], ndarray] = ...
 ) -> ndarray: ...
+
 def partition(
     a: ArrayLike,
     kth: _ArrayLikeIntOrBool,
@@ -123,61 +98,62 @@ def partition(
     kind: _PartitionKind = ...,
     order: Union[None, str, Sequence[str]] = ...,
 ) -> ndarray: ...
-@overload
-def argpartition(
-    a: generic,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> integer: ...
-@overload
-def argpartition(
-    a: _ScalarBuiltin,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-@overload
+
 def argpartition(
     a: ArrayLike,
     kth: _ArrayLikeIntOrBool,
     axis: Optional[int] = ...,
     kind: _PartitionKind = ...,
     order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
+) -> Any: ...
+
 def sort(
     a: ArrayLike,
     axis: Optional[int] = ...,
     kind: Optional[_SortKind] = ...,
     order: Union[None, str, Sequence[str]] = ...,
 ) -> ndarray: ...
+
 def argsort(
     a: ArrayLike,
     axis: Optional[int] = ...,
     kind: Optional[_SortKind] = ...,
     order: Union[None, str, Sequence[str]] = ...,
 ) -> ndarray: ...
+
 @overload
-def argmax(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+def argmax(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> signedinteger[Any]: ...
 @overload
 def argmax(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
 @overload
-def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+def argmin(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> signedinteger[Any]: ...
 @overload
 def argmin(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
 @overload
 def searchsorted(
     a: ArrayLike,
     v: _Scalar,
     side: _SortSide = ...,
     sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
-) -> integer: ...
+) -> signedinteger[Any]: ...
 @overload
 def searchsorted(
     a: ArrayLike,
@@ -185,14 +161,30 @@ def searchsorted(
     side: _SortSide = ...,
     sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
 ) -> ndarray: ...
-def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ...
+
+def resize(
+    a: ArrayLike,
+    new_shape: _ShapeLike,
+) -> ndarray: ...
+
 @overload
-def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ...
+def squeeze(
+    a: _ScalarGeneric,
+    axis: Optional[_ShapeLike] = ...,
+) -> _ScalarGeneric: ...
 @overload
-def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ...
+def squeeze(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+
 def diagonal(
-    a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ...  # >= 2D array
+    a: ArrayLike,
+    offset: int = ...,
+    axis1: int = ...,
+    axis2: int = ...,  # >= 2D array
 ) -> ndarray: ...
+
 def trace(
     a: ArrayLike,  # >= 2D array
     offset: int = ...,
@@ -200,32 +192,21 @@ def trace(
     axis2: int = ...,
     dtype: DTypeLike = ...,
     out: Optional[ndarray] = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
 def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
+
 def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
+
 def shape(a: ArrayLike) -> _Shape: ...
+
 def compress(
     condition: ArrayLike,  # 1D bool array
     a: ArrayLike,
     axis: Optional[int] = ...,
     out: Optional[ndarray] = ...,
 ) -> ndarray: ...
-@overload
-def clip(
-    a: _Number,
-    a_min: ArrayLike,
-    a_max: Optional[ArrayLike],
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> _Number: ...
-@overload
-def clip(
-    a: _Number,
-    a_min: None,
-    a_max: ArrayLike,
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> _Number: ...
+
 @overload
 def clip(
     a: ArrayLike,
@@ -233,7 +214,7 @@ def clip(
     a_max: Optional[ArrayLike],
     out: Optional[ndarray] = ...,
     **kwargs: Any,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
 @overload
 def clip(
     a: ArrayLike,
@@ -241,18 +222,8 @@ def clip(
     a_max: ArrayLike,
     out: Optional[ndarray] = ...,
     **kwargs: Any,
-) -> Union[number, ndarray]: ...
-@overload
-def sum(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DTypeLike = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
+) -> Any: ...
+
 def sum(
     a: ArrayLike,
     axis: _ShapeLike = ...,
@@ -261,12 +232,13 @@ def sum(
     keepdims: bool = ...,
     initial: _NumberLike = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
 @overload
 def all(
     a: ArrayLike,
     axis: None = ...,
-    out: Optional[ndarray] = ...,
+    out: None = ...,
     keepdims: Literal[False] = ...,
 ) -> bool_: ...
 @overload
@@ -275,12 +247,13 @@ def all(
     axis: Optional[_ShapeLike] = ...,
     out: Optional[ndarray] = ...,
     keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
 @overload
 def any(
     a: ArrayLike,
     axis: None = ...,
-    out: Optional[ndarray] = ...,
+    out: None = ...,
     keepdims: Literal[False] = ...,
 ) -> bool_: ...
 @overload
@@ -289,53 +262,22 @@ def any(
     axis: Optional[_ShapeLike] = ...,
     out: Optional[ndarray] = ...,
     keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
 def cumsum(
     a: ArrayLike,
     axis: Optional[int] = ...,
     dtype: DTypeLike = ...,
     out: Optional[ndarray] = ...,
 ) -> ndarray: ...
-@overload
-def ptp(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> _Number: ...
-@overload
-def ptp(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+
 def ptp(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
     out: Optional[ndarray] = ...,
     keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amax(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amax(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def amax(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -343,26 +285,8 @@ def amax(
     keepdims: bool = ...,
     initial: _NumberLike = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amin(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amin(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def amin(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -370,7 +294,7 @@ def amin(
     keepdims: bool = ...,
     initial: _NumberLike = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
 
 # TODO: `np.prod()``: For object arrays `initial` does not necessarily
 # have to be a numerical scalar.
@@ -379,27 +303,6 @@ def amin(
 
 # Note that the same situation holds for all wrappers around
 # `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
-@overload
-def prod(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def prod(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
 def prod(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -408,53 +311,33 @@ def prod(
     keepdims: bool = ...,
     initial: _NumberLike = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
+
 def cumprod(
     a: ArrayLike,
     axis: Optional[int] = ...,
     dtype: DTypeLike = ...,
     out: Optional[ndarray] = ...,
 ) -> ndarray: ...
+
 def ndim(a: ArrayLike) -> int: ...
+
 def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
-@overload
-def around(
-    a: _Number, decimals: int = ..., out: Optional[ndarray] = ...
-) -> _Number: ...
-@overload
-def around(
-    a: _NumberLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> number: ...
-@overload
+
 def around(
-    a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> ndarray: ...
-@overload
-def mean(
     a: ArrayLike,
-    axis: None = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+    decimals: int = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
 def mean(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
     dtype: DTypeLike = ...,
     out: Optional[ndarray] = ...,
     keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def std(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    ddof: int = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def std(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -462,17 +345,8 @@ def std(
     out: Optional[ndarray] = ...,
     ddof: int = ...,
     keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def var(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    ddof: int = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def var(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -480,4 +354,4 @@ def var(
     out: Optional[ndarray] = ...,
     ddof: int = ...,
     keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
diff --git a/numpy/core/function_base.pyi b/numpy/core/function_base.pyi
index 1490bed4a..573dc2193 100644
--- a/numpy/core/function_base.pyi
+++ b/numpy/core/function_base.pyi
@@ -1,7 +1,7 @@
 import sys
 from typing import overload, Tuple, Union, Sequence, Any
 
-from numpy import ndarray, inexact
+from numpy import ndarray
 from numpy.typing import ArrayLike, DTypeLike, _SupportsArray, _NumberLike
 
 if sys.version_info >= (3, 8):
@@ -36,7 +36,8 @@ def linspace(
     retstep: Literal[True] = ...,
     dtype: DTypeLike = ...,
     axis: SupportsIndex = ...,
-) -> Tuple[ndarray, inexact]: ...
+) -> Tuple[ndarray, Any]: ...
+
 def logspace(
     start: _ArrayLikeNumber,
     stop: _ArrayLikeNumber,
@@ -46,6 +47,7 @@ def logspace(
     dtype: DTypeLike = ...,
     axis: SupportsIndex = ...,
 ) -> ndarray: ...
+
 def geomspace(
     start: _ArrayLikeNumber,
     stop: _ArrayLikeNumber,
diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi
index d91cb31c2..6b9ef4b20 100644
--- a/numpy/core/numeric.pyi
+++ b/numpy/core/numeric.pyi
@@ -41,6 +41,7 @@ def zeros_like(
     subok: bool = ...,
     shape: Optional[_ShapeLike] = ...,
 ) -> ndarray: ...
+
 def ones(
     shape: _ShapeLike,
     dtype: DTypeLike = ...,
@@ -48,6 +49,7 @@ def ones(
     *,
     like: ArrayLike = ...,
 ) -> ndarray: ...
+
 @overload
 def ones_like(
     a: _ArrayType,
@@ -64,6 +66,7 @@ def ones_like(
     subok: bool = ...,
     shape: Optional[_ShapeLike] = ...,
 ) -> ndarray: ...
+
 @overload
 def empty_like(
     a: _ArrayType,
@@ -80,6 +83,7 @@ def empty_like(
     subok: bool = ...,
     shape: Optional[_ShapeLike] = ...,
 ) -> ndarray: ...
+
 def full(
     shape: _ShapeLike,
     fill_value: Any,
@@ -88,6 +92,7 @@ def full(
     *,
     like: ArrayLike = ...,
 ) -> ndarray: ...
+
 @overload
 def full_like(
     a: _ArrayType,
@@ -106,39 +111,73 @@ def full_like(
     subok: bool = ...,
     shape: Optional[_ShapeLike] = ...,
 ) -> ndarray: ...
+
 @overload
 def count_nonzero(
-    a: ArrayLike, axis: None = ..., *, keepdims: Literal[False] = ...
+    a: ArrayLike,
+    axis: None = ...,
+    *,
+    keepdims: Literal[False] = ...,
 ) -> int: ...
 @overload
 def count_nonzero(
-    a: ArrayLike, axis: _ShapeLike = ..., *, keepdims: bool = ...
-) -> Union[signedinteger[Any], ndarray]: ...  # TODO: np.intp
+    a: ArrayLike,
+    axis: _ShapeLike = ...,
+    *,
+    keepdims: bool = ...,
+) -> Any: ...  # TODO: np.intp or ndarray[np.intp]
+
 def isfortran(a: Union[ndarray, generic]) -> bool: ...
+
 def argwhere(a: ArrayLike) -> ndarray: ...
+
 def flatnonzero(a: ArrayLike) -> ndarray: ...
-def correlate(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
-def convolve(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
+
+def correlate(
+    a: ArrayLike,
+    v: ArrayLike,
+    mode: _CorrelateMode = ...,
+) -> ndarray: ...
+
+def convolve(
+    a: ArrayLike,
+    v: ArrayLike,
+    mode: _CorrelateMode = ...,
+) -> ndarray: ...
+
 @overload
-def outer(a: ArrayLike, b: ArrayLike, out: None = ...) -> ndarray: ...
+def outer(
+    a: ArrayLike,
+    b: ArrayLike,
+    out: None = ...,
+) -> ndarray: ...
 @overload
-def outer(a: ArrayLike, b: ArrayLike, out: _ArrayType = ...) -> _ArrayType: ...
+def outer(
+    a: ArrayLike,
+    b: ArrayLike,
+    out: _ArrayType = ...,
+) -> _ArrayType: ...
+
 def tensordot(
     a: ArrayLike,
     b: ArrayLike,
     axes: Union[int, Tuple[_ShapeLike, _ShapeLike]] = ...,
 ) -> ndarray: ...
+
 def roll(
     a: ArrayLike,
     shift: _ShapeLike,
     axis: Optional[_ShapeLike] = ...,
 ) -> ndarray: ...
+
 def rollaxis(a: ndarray, axis: int, start: int = ...) -> ndarray: ...
+
 def moveaxis(
     a: ndarray,
     source: _ShapeLike,
     destination: _ShapeLike,
 ) -> ndarray: ...
+
 def cross(
     a: ArrayLike,
     b: ArrayLike,
@@ -147,6 +186,7 @@ def cross(
     axisc: int = ...,
     axis: Optional[int] = ...,
 ) -> ndarray: ...
+
 @overload
 def indices(
     dimensions: Sequence[int],
@@ -159,6 +199,7 @@ def indices(
     dtype: DTypeLike = ...,
     sparse: Literal[True] = ...,
 ) -> Tuple[ndarray, ...]: ...
+
 def fromfunction(
     function: Callable[..., _T],
     shape: Sequence[int],
@@ -167,10 +208,20 @@ def fromfunction(
     like: ArrayLike = ...,
     **kwargs: Any,
 ) -> _T: ...
+
 def isscalar(element: Any) -> bool: ...
+
 def binary_repr(num: int, width: Optional[int] = ...) -> str: ...
+
 def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ...
-def identity(n: int, dtype: DTypeLike = ..., *, like: ArrayLike = ...) -> ndarray: ...
+
+def identity(
+    n: int,
+    dtype: DTypeLike = ...,
+    *,
+    like: ArrayLike = ...,
+) -> ndarray: ...
+
 def allclose(
     a: ArrayLike,
     b: ArrayLike,
@@ -178,12 +229,15 @@ def allclose(
     atol: float = ...,
     equal_nan: bool = ...,
 ) -> bool: ...
+
 def isclose(
     a: ArrayLike,
     b: ArrayLike,
     rtol: float = ...,
     atol: float = ...,
     equal_nan: bool = ...,
-) -> Union[bool_, ndarray]: ...
+) -> Any: ...
+
 def array_equal(a1: ArrayLike, a2: ArrayLike) -> bool: ...
+
 def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...
diff --git a/numpy/typing/_callable.py b/numpy/typing/_callable.py
index 91b7a4ec2..ad38028e1 100644
--- a/numpy/typing/_callable.py
+++ b/numpy/typing/_callable.py
@@ -156,7 +156,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL:
         @overload
         def __call__(
             self, __other: Union[int, signedinteger[Any]]
-        ) -> Union[signedinteger[Any], float64]: ...
+        ) -> Any: ...
         @overload
         def __call__(self, __other: float) -> float64: ...
         @overload
@@ -184,7 +184,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL:
         @overload
         def __call__(
             self, __other: Union[int, signedinteger[Any]]
-        ) -> Union[signedinteger[Any], float64]: ...
+        ) -> Any: ...
         @overload
         def __call__(self, __other: float) -> float64: ...
         @overload
@@ -198,7 +198,7 @@ if TYPE_CHECKING or HAVE_PROTOCOL:
         @overload
         def __call__(
             self, __other: Union[int, signedinteger[Any]]
-        ) -> Union[_2Tuple[signedinteger[Any]], _2Tuple[float64]]: ...
+        ) -> _2Tuple[Any]: ...
         @overload
         def __call__(self, __other: float) -> _2Tuple[float64]: ...
         @overload
diff --git a/numpy/typing/tests/data/fail/fromnumeric.py b/numpy/typing/tests/data/fail/fromnumeric.py
index c9156895d..d8f7a5d69 100644
--- a/numpy/typing/tests/data/fail/fromnumeric.py
+++ b/numpy/typing/tests/data/fail/fromnumeric.py
@@ -7,17 +7,17 @@ A.setflags(write=False)
 
 a = np.bool_(True)
 
-np.take(a, None)  # E: No overload variant of "take" matches argument type
-np.take(a, axis=1.0)  # E: No overload variant of "take" matches argument type
-np.take(A, out=1)  # E: No overload variant of "take" matches argument type
-np.take(A, mode="bob")  # E: No overload variant of "take" matches argument type
+np.take(a, None)  # E: incompatible type
+np.take(a, axis=1.0)  # E: incompatible type
+np.take(A, out=1)  # E: incompatible type
+np.take(A, mode="bob")  # E: incompatible type
 
 np.reshape(a, None)  # E: Argument 2 to "reshape" has incompatible type
 np.reshape(A, 1, order="bob")  # E: Argument "order" to "reshape" has incompatible type
 
-np.choose(a, None)  # E: No overload variant of "choose" matches argument type
-np.choose(a, out=1.0)  # E: No overload variant of "choose" matches argument type
-np.choose(A, mode="bob")  # E: No overload variant of "choose" matches argument type
+np.choose(a, None)  # E: incompatible type
+np.choose(a, out=1.0)  # E: incompatible type
+np.choose(A, mode="bob")  # E: incompatible type
 
 np.repeat(a, None)  # E: Argument 2 to "repeat" has incompatible type
 np.repeat(A, 1, axis=1.0)  # E: Argument "axis" to "repeat" has incompatible type
@@ -38,14 +38,14 @@ np.partition(
     A, 0, order=range(5)  # E: Argument "order" to "partition" has incompatible type
 )
 
-np.argpartition(  # E: No overload variant of "argpartition" matches argument type
-    a, None
+np.argpartition(
+    a, None  # E: incompatible type
 )
-np.argpartition(  # E: No overload variant of "argpartition" matches argument type
-    a, 0, axis="bob"
+np.argpartition(
+    a, 0, axis="bob"  # E: incompatible type
 )
-np.argpartition(  # E: No overload variant of "argpartition" matches argument type
-    A, 0, kind="bob"
+np.argpartition(
+    A, 0, kind="bob"  # E: incompatible type
 )
 np.argpartition(
     A, 0, order=range(5)  # E: Argument "order" to "argpartition" has incompatible type
@@ -93,62 +93,62 @@ np.compress(
 np.clip(a, 1, 2, out=1)  # E: No overload variant of "clip" matches argument type
 np.clip(1, None, None)  # E: No overload variant of "clip" matches argument type
 
-np.sum(a, axis=1.0)  # E: No overload variant of "sum" matches argument type
-np.sum(a, keepdims=1.0)  # E: No overload variant of "sum" matches argument type
-np.sum(a, initial=[1])  # E: No overload variant of "sum" matches argument type
+np.sum(a, axis=1.0)  # E: incompatible type
+np.sum(a, keepdims=1.0)  # E: incompatible type
+np.sum(a, initial=[1])  # E: incompatible type
 
-np.all(a, axis=1.0)  # E: No overload variant of "all" matches argument type
-np.all(a, keepdims=1.0)  # E: No overload variant of "all" matches argument type
-np.all(a, out=1.0)  # E: No overload variant of "all" matches argument type
+np.all(a, axis=1.0)  # E: No overload variant
+np.all(a, keepdims=1.0)  # E: No overload variant
+np.all(a, out=1.0)  # E: No overload variant
 
-np.any(a, axis=1.0)  # E: No overload variant of "any" matches argument type
-np.any(a, keepdims=1.0)  # E: No overload variant of "any" matches argument type
-np.any(a, out=1.0)  # E: No overload variant of "any" matches argument type
+np.any(a, axis=1.0)  # E: No overload variant
+np.any(a, keepdims=1.0)  # E: No overload variant
+np.any(a, out=1.0)  # E: No overload variant
 
-np.cumsum(a, axis=1.0)  # E: Argument "axis" to "cumsum" has incompatible type
-np.cumsum(a, dtype=1.0)  # E: Argument "dtype" to "cumsum" has incompatible type
-np.cumsum(a, out=1.0)  # E: Argument "out" to "cumsum" has incompatible type
+np.cumsum(a, axis=1.0)  # E: incompatible type
+np.cumsum(a, dtype=1.0)  # E: incompatible type
+np.cumsum(a, out=1.0)  # E: incompatible type
 
-np.ptp(a, axis=1.0)  # E: No overload variant of "ptp" matches argument type
-np.ptp(a, keepdims=1.0)  # E: No overload variant of "ptp" matches argument type
-np.ptp(a, out=1.0)  # E: No overload variant of "ptp" matches argument type
+np.ptp(a, axis=1.0)  # E: incompatible type
+np.ptp(a, keepdims=1.0)  # E: incompatible type
+np.ptp(a, out=1.0)  # E: incompatible type
 
-np.amax(a, axis=1.0)  # E: No overload variant of "amax" matches argument type
-np.amax(a, keepdims=1.0)  # E: No overload variant of "amax" matches argument type
-np.amax(a, out=1.0)  # E: No overload variant of "amax" matches argument type
-np.amax(a, initial=[1.0])  # E: No overload variant of "amax" matches argument type
+np.amax(a, axis=1.0)  # E: incompatible type
+np.amax(a, keepdims=1.0)  # E: incompatible type
+np.amax(a, out=1.0)  # E: incompatible type
+np.amax(a, initial=[1.0])  # E: incompatible type
 np.amax(a, where=[1.0])  # E: List item 0 has incompatible type
 
-np.amin(a, axis=1.0)  # E: No overload variant of "amin" matches argument type
-np.amin(a, keepdims=1.0)  # E: No overload variant of "amin" matches argument type
-np.amin(a, out=1.0)  # E: No overload variant of "amin" matches argument type
-np.amin(a, initial=[1.0])  # E: No overload variant of "amin" matches argument type
+np.amin(a, axis=1.0)  # E: incompatible type
+np.amin(a, keepdims=1.0)  # E: incompatible type
+np.amin(a, out=1.0)  # E: incompatible type
+np.amin(a, initial=[1.0])  # E: incompatible type
 np.amin(a, where=[1.0])  # E: List item 0 has incompatible type
 
-np.prod(a, axis=1.0)  # E: No overload variant of "prod" matches argument type
-np.prod(a, out=False)  # E: No overload variant of "prod" matches argument type
-np.prod(a, keepdims=1.0)  # E: No overload variant of "prod" matches argument type
-np.prod(a, initial=int)  # E: No overload variant of "prod" matches argument type
-np.prod(a, where=1.0)  # E: No overload variant of "prod" matches argument type
+np.prod(a, axis=1.0)  # E: incompatible type
+np.prod(a, out=False)  # E: incompatible type
+np.prod(a, keepdims=1.0)  # E: incompatible type
+np.prod(a, initial=int)  # E: incompatible type
+np.prod(a, where=1.0)  # E: incompatible type
 
 np.cumprod(a, axis=1.0)  # E: Argument "axis" to "cumprod" has incompatible type
 np.cumprod(a, out=False)  # E: Argument "out" to "cumprod" has incompatible type
 
 np.size(a, axis=1.0)  # E: Argument "axis" to "size" has incompatible type
 
-np.around(a, decimals=1.0)  # E: No overload variant of "around" matches argument type
-np.around(a, out=type)  # E: No overload variant of "around" matches argument type
+np.around(a, decimals=1.0)  # E: incompatible type
+np.around(a, out=type)  # E: incompatible type
 
-np.mean(a, axis=1.0)  # E: No overload variant of "mean" matches argument type
-np.mean(a, out=False)  # E: No overload variant of "mean" matches argument type
-np.mean(a, keepdims=1.0)  # E: No overload variant of "mean" matches argument type
+np.mean(a, axis=1.0)  # E: incompatible type
+np.mean(a, out=False)  # E: incompatible type
+np.mean(a, keepdims=1.0)  # E: incompatible type
 
-np.std(a, axis=1.0)  # E: No overload variant of "std" matches argument type
-np.std(a, out=False)  # E: No overload variant of "std" matches argument type
-np.std(a, ddof='test')  # E: No overload variant of "std" matches argument type
-np.std(a, keepdims=1.0)  # E: No overload variant of "std" matches argument type
+np.std(a, axis=1.0)  # E: incompatible type
+np.std(a, out=False)  # E: incompatible type
+np.std(a, ddof='test')  # E: incompatible type
+np.std(a, keepdims=1.0)  # E: incompatible type
 
-np.var(a, axis=1.0)  # E: No overload variant of "var" matches argument type
-np.var(a, out=False)  # E: No overload variant of "var" matches argument type
-np.var(a, ddof='test')  # E: No overload variant of "var" matches argument type
-np.var(a, keepdims=1.0)  # E: No overload variant of "var" matches argument type
+np.var(a, axis=1.0)  # E: incompatible type
+np.var(a, out=False)  # E: incompatible type
+np.var(a, ddof='test')  # E: incompatible type
+np.var(a, keepdims=1.0)  # E: incompatible type
diff --git a/numpy/typing/tests/data/reveal/arithmetic.py b/numpy/typing/tests/data/reveal/arithmetic.py
index 20310e691..7d93893d4 100644
--- a/numpy/typing/tests/data/reveal/arithmetic.py
+++ b/numpy/typing/tests/data/reveal/arithmetic.py
@@ -217,9 +217,9 @@ reveal_type(AR + f4)  # E: Any
 # Int
 
 reveal_type(i8 + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(i8 + u8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u8)  # E: Any
 reveal_type(i8 + i4)  # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(i8 + u4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u4)  # E: Any
 reveal_type(i8 + b_)  # E: numpy.signedinteger[numpy.typing._64Bit]
 reveal_type(i8 + b)  # E: numpy.signedinteger[numpy.typing._64Bit]
 reveal_type(i8 + c)  # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
@@ -228,19 +228,19 @@ reveal_type(i8 + i)  # E: numpy.signedinteger[Any]
 reveal_type(i8 + AR)  # E: Any
 
 reveal_type(u8 + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
-reveal_type(u8 + i4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i4)  # E: Any
 reveal_type(u8 + u4)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(u8 + b_)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(u8 + b)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(u8 + c)  # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
 reveal_type(u8 + f)  # E: numpy.floating[numpy.typing._64Bit]
-reveal_type(u8 + i)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i)  # E: Any
 reveal_type(u8 + AR)  # E: Any
 
 reveal_type(i8 + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(u8 + i8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u8 + i8)  # E: Any
 reveal_type(i4 + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
-reveal_type(u4 + i8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i8)  # E: Any
 reveal_type(b_ + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
 reveal_type(b + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
 reveal_type(c + i8)  # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
@@ -249,13 +249,13 @@ reveal_type(i + i8)  # E: numpy.signedinteger[Any]
 reveal_type(AR + i8)  # E: Any
 
 reveal_type(u8 + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
-reveal_type(i4 + u8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i4 + u8)  # E: Any
 reveal_type(u4 + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(b_ + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(b + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(c + u8)  # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
 reveal_type(f + u8)  # E: numpy.floating[numpy.typing._64Bit]
-reveal_type(i + u8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i + u8)  # E: Any
 reveal_type(AR + u8)  # E: Any
 
 reveal_type(i4 + i8)  # E: numpy.signedinteger[numpy.typing._64Bit]
@@ -265,11 +265,11 @@ reveal_type(i4 + b_)  # E: numpy.signedinteger[numpy.typing._32Bit]
 reveal_type(i4 + b)  # E: numpy.signedinteger[numpy.typing._32Bit]
 reveal_type(i4 + AR)  # E: Any
 
-reveal_type(u4 + i8)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
-reveal_type(u4 + i4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i8)  # E: Any
+reveal_type(u4 + i4)  # E: Any
 reveal_type(u4 + u8)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(u4 + u4)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
-reveal_type(u4 + i)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(u4 + i)  # E: Any
 reveal_type(u4 + b_)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
 reveal_type(u4 + b)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
 reveal_type(u4 + AR)  # E: Any
@@ -281,11 +281,11 @@ reveal_type(b_ + i4)  # E: numpy.signedinteger[numpy.typing._32Bit]
 reveal_type(b + i4)  # E: numpy.signedinteger[numpy.typing._32Bit]
 reveal_type(AR + i4)  # E: Any
 
-reveal_type(i8 + u4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
-reveal_type(i4 + u4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i8 + u4)  # E: Any
+reveal_type(i4 + u4)  # E: Any
 reveal_type(u8 + u4)  # E: numpy.unsignedinteger[numpy.typing._64Bit]
 reveal_type(u4 + u4)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
 reveal_type(b_ + u4)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
 reveal_type(b + u4)  # E: numpy.unsignedinteger[numpy.typing._32Bit]
-reveal_type(i + u4)  # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
+reveal_type(i + u4)  # E: Any
 reveal_type(AR + u4)  # E: Any
diff --git a/numpy/typing/tests/data/reveal/array_constructors.py b/numpy/typing/tests/data/reveal/array_constructors.py
index 106174736..c4c162bb2 100644
--- a/numpy/typing/tests/data/reveal/array_constructors.py
+++ b/numpy/typing/tests/data/reveal/array_constructors.py
@@ -41,7 +41,7 @@ reveal_type(np.require(B, requirements="A"))  # E: SubClass
 reveal_type(np.require(C))  # E: ndarray
 
 reveal_type(np.linspace(0, 10))  # E: numpy.ndarray
-reveal_type(np.linspace(0, 10, retstep=True))  # E: Tuple[numpy.ndarray, numpy.inexact[Any]]
+reveal_type(np.linspace(0, 10, retstep=True))  # E: Tuple[numpy.ndarray, Any]
 reveal_type(np.logspace(0, 10))  # E: numpy.ndarray
 reveal_type(np.geomspace(1, 10))  # E: numpy.ndarray
 
diff --git a/numpy/typing/tests/data/reveal/fromnumeric.py b/numpy/typing/tests/data/reveal/fromnumeric.py
index 75865c285..5d67ab97f 100644
--- a/numpy/typing/tests/data/reveal/fromnumeric.py
+++ b/numpy/typing/tests/data/reveal/fromnumeric.py
@@ -12,27 +12,13 @@ b = np.float32(1.0)
 c = 1.0
 d = np.array(1.0, dtype=np.float32)  # writeable
 
-reveal_type(np.take(a, 0))  # E: numpy.bool_
-reveal_type(np.take(b, 0))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(
-    np.take(c, 0)  # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
-    np.take(A, 0)  # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
-    np.take(B, 0)  # E: Union[numpy.generic, datetime.datetime, datetime.timedelta]
-)
-reveal_type(
-    np.take(  # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray]
-        A, [0]
-    )
-)
-reveal_type(
-    np.take(  # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray]
-        B, [0]
-    )
-)
+reveal_type(np.take(a, 0))  # E: Any
+reveal_type(np.take(b, 0))  # E: Any
+reveal_type(np.take(c, 0))  # E: Any
+reveal_type(np.take(A, 0))  # E: Any
+reveal_type(np.take(B, 0))  # E: Any
+reveal_type(np.take(A, [0]))  # E: Any
+reveal_type(np.take(B, [0]))  # E: Any
 
 reveal_type(np.reshape(a, 1))  # E: numpy.ndarray
 reveal_type(np.reshape(b, 1))  # E: numpy.ndarray
@@ -40,8 +26,8 @@ reveal_type(np.reshape(c, 1))  # E: numpy.ndarray
 reveal_type(np.reshape(A, 1))  # E: numpy.ndarray
 reveal_type(np.reshape(B, 1))  # E: numpy.ndarray
 
-reveal_type(np.choose(a, [True, True]))  # E: numpy.bool_
-reveal_type(np.choose(A, [True, True]))  # E: numpy.ndarray
+reveal_type(np.choose(a, [True, True]))  # E: Any
+reveal_type(np.choose(A, [True, True]))  # E: Any
 
 reveal_type(np.repeat(a, 1))  # E: numpy.ndarray
 reveal_type(np.repeat(b, 1))  # E: numpy.ndarray
@@ -66,11 +52,11 @@ reveal_type(np.partition(c, 0, axis=None))  # E: numpy.ndarray
 reveal_type(np.partition(A, 0))  # E: numpy.ndarray
 reveal_type(np.partition(B, 0))  # E: numpy.ndarray
 
-reveal_type(np.argpartition(a, 0))  # E: numpy.integer[Any]
-reveal_type(np.argpartition(b, 0))  # E: numpy.integer[Any]
-reveal_type(np.argpartition(c, 0))  # E: numpy.ndarray
-reveal_type(np.argpartition(A, 0))  # E: numpy.ndarray
-reveal_type(np.argpartition(B, 0))  # E: numpy.ndarray
+reveal_type(np.argpartition(a, 0))  # E: Any
+reveal_type(np.argpartition(b, 0))  # E: Any
+reveal_type(np.argpartition(c, 0))  # E: Any
+reveal_type(np.argpartition(A, 0))  # E: Any
+reveal_type(np.argpartition(B, 0))  # E: Any
 
 reveal_type(np.sort(A, 0))  # E: numpy.ndarray
 reveal_type(np.sort(B, 0))  # E: numpy.ndarray
@@ -78,18 +64,18 @@ reveal_type(np.sort(B, 0))  # E: numpy.ndarray
 reveal_type(np.argsort(A, 0))  # E: numpy.ndarray
 reveal_type(np.argsort(B, 0))  # E: numpy.ndarray
 
-reveal_type(np.argmax(A))  # E: numpy.integer[Any]
-reveal_type(np.argmax(B))  # E: numpy.integer[Any]
-reveal_type(np.argmax(A, axis=0))  # E: Union[numpy.integer[Any], numpy.ndarray]
-reveal_type(np.argmax(B, axis=0))  # E: Union[numpy.integer[Any], numpy.ndarray]
+reveal_type(np.argmax(A))  # E: numpy.signedinteger[Any]
+reveal_type(np.argmax(B))  # E: numpy.signedinteger[Any]
+reveal_type(np.argmax(A, axis=0))  # E: Any
+reveal_type(np.argmax(B, axis=0))  # E: Any
 
-reveal_type(np.argmin(A))  # E: numpy.integer[Any]
-reveal_type(np.argmin(B))  # E: numpy.integer[Any]
-reveal_type(np.argmin(A, axis=0))  # E: Union[numpy.integer[Any], numpy.ndarray]
-reveal_type(np.argmin(B, axis=0))  # E: Union[numpy.integer[Any], numpy.ndarray]
+reveal_type(np.argmin(A))  # E: numpy.signedinteger[Any]
+reveal_type(np.argmin(B))  # E: numpy.signedinteger[Any]
+reveal_type(np.argmin(A, axis=0))  # E: Any
+reveal_type(np.argmin(B, axis=0))  # E: Any
 
-reveal_type(np.searchsorted(A[0], 0))  # E: numpy.integer[Any]
-reveal_type(np.searchsorted(B[0], 0))  # E: numpy.integer[Any]
+reveal_type(np.searchsorted(A[0], 0))  # E: numpy.signedinteger[Any]
+reveal_type(np.searchsorted(B[0], 0))  # E: numpy.signedinteger[Any]
 reveal_type(np.searchsorted(A[0], [0]))  # E: numpy.ndarray
 reveal_type(np.searchsorted(B[0], [0]))  # E: numpy.ndarray
 
@@ -108,8 +94,8 @@ reveal_type(np.squeeze(B))  # E: numpy.ndarray
 reveal_type(np.diagonal(A))  # E: numpy.ndarray
 reveal_type(np.diagonal(B))  # E: numpy.ndarray
 
-reveal_type(np.trace(A))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.trace(B))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.trace(A))  # E: Any
+reveal_type(np.trace(B))  # E: Any
 
 reveal_type(np.ravel(a))  # E: numpy.ndarray
 reveal_type(np.ravel(b))  # E: numpy.ndarray
@@ -135,39 +121,39 @@ reveal_type(np.compress([True], c))  # E: numpy.ndarray
 reveal_type(np.compress([True], A))  # E: numpy.ndarray
 reveal_type(np.compress([True], B))  # E: numpy.ndarray
 
-reveal_type(np.clip(a, 0, 1.0))  # E: numpy.number[Any]
-reveal_type(np.clip(b, -1, 1))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.clip(c, 0, 1))  # E: numpy.number[Any]
-reveal_type(np.clip(A, 0, 1))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.clip(B, 0, 1))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.clip(a, 0, 1.0))  # E: Any
+reveal_type(np.clip(b, -1, 1))  # E: Any
+reveal_type(np.clip(c, 0, 1))  # E: Any
+reveal_type(np.clip(A, 0, 1))  # E: Any
+reveal_type(np.clip(B, 0, 1))  # E: Any
 
-reveal_type(np.sum(a))  # E: numpy.number[Any]
-reveal_type(np.sum(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.sum(c))  # E: numpy.number[Any]
-reveal_type(np.sum(A))  # E: numpy.number[Any]
-reveal_type(np.sum(B))  # E: numpy.number[Any]
-reveal_type(np.sum(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.sum(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(np.sum(a))  # E: Any
+reveal_type(np.sum(b))  # E: Any
+reveal_type(np.sum(c))  # E: Any
+reveal_type(np.sum(A))  # E: Any
+reveal_type(np.sum(B))  # E: Any
+reveal_type(np.sum(A, axis=0))  # E: Any
+reveal_type(np.sum(B, axis=0))  # E: Any
 
 reveal_type(np.all(a))  # E: numpy.bool_
 reveal_type(np.all(b))  # E: numpy.bool_
 reveal_type(np.all(c))  # E: numpy.bool_
 reveal_type(np.all(A))  # E: numpy.bool_
 reveal_type(np.all(B))  # E: numpy.bool_
-reveal_type(np.all(A, axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(B, axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(A, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.all(B, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.all(A, axis=0))  # E: Any
+reveal_type(np.all(B, axis=0))  # E: Any
+reveal_type(np.all(A, keepdims=True))  # E: Any
+reveal_type(np.all(B, keepdims=True))  # E: Any
 
 reveal_type(np.any(a))  # E: numpy.bool_
 reveal_type(np.any(b))  # E: numpy.bool_
 reveal_type(np.any(c))  # E: numpy.bool_
 reveal_type(np.any(A))  # E: numpy.bool_
 reveal_type(np.any(B))  # E: numpy.bool_
-reveal_type(np.any(A, axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(B, axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(A, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.any(B, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.any(A, axis=0))  # E: Any
+reveal_type(np.any(B, axis=0))  # E: Any
+reveal_type(np.any(A, keepdims=True))  # E: Any
+reveal_type(np.any(B, keepdims=True))  # E: Any
 
 reveal_type(np.cumsum(a))  # E: numpy.ndarray
 reveal_type(np.cumsum(b))  # E: numpy.ndarray
@@ -175,47 +161,47 @@ reveal_type(np.cumsum(c))  # E: numpy.ndarray
 reveal_type(np.cumsum(A))  # E: numpy.ndarray
 reveal_type(np.cumsum(B))  # E: numpy.ndarray
 
-reveal_type(np.ptp(a))  # E: numpy.number[Any]
-reveal_type(np.ptp(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.ptp(c))  # E: numpy.number[Any]
-reveal_type(np.ptp(A))  # E: numpy.number[Any]
-reveal_type(np.ptp(B))  # E: numpy.number[Any]
-reveal_type(np.ptp(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.ptp(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.amax(a))  # E: numpy.number[Any]
-reveal_type(np.amax(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.amax(c))  # E: numpy.number[Any]
-reveal_type(np.amax(A))  # E: numpy.number[Any]
-reveal_type(np.amax(B))  # E: numpy.number[Any]
-reveal_type(np.amax(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amax(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.amin(a))  # E: numpy.number[Any]
-reveal_type(np.amin(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.amin(c))  # E: numpy.number[Any]
-reveal_type(np.amin(A))  # E: numpy.number[Any]
-reveal_type(np.amin(B))  # E: numpy.number[Any]
-reveal_type(np.amin(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.amin(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-
-reveal_type(np.prod(a))  # E: numpy.number[Any]
-reveal_type(np.prod(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.prod(c))  # E: numpy.number[Any]
-reveal_type(np.prod(A))  # E: numpy.number[Any]
-reveal_type(np.prod(B))  # E: numpy.number[Any]
-reveal_type(np.prod(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.prod(b, out=d))  # E: numpy.ndarray
-reveal_type(np.prod(B, out=d))  # E: numpy.ndarray
+reveal_type(np.ptp(a))  # E: Any
+reveal_type(np.ptp(b))  # E: Any
+reveal_type(np.ptp(c))  # E: Any
+reveal_type(np.ptp(A))  # E: Any
+reveal_type(np.ptp(B))  # E: Any
+reveal_type(np.ptp(A, axis=0))  # E: Any
+reveal_type(np.ptp(B, axis=0))  # E: Any
+reveal_type(np.ptp(A, keepdims=True))  # E: Any
+reveal_type(np.ptp(B, keepdims=True))  # E: Any
+
+reveal_type(np.amax(a))  # E: Any
+reveal_type(np.amax(b))  # E: Any
+reveal_type(np.amax(c))  # E: Any
+reveal_type(np.amax(A))  # E: Any
+reveal_type(np.amax(B))  # E: Any
+reveal_type(np.amax(A, axis=0))  # E: Any
+reveal_type(np.amax(B, axis=0))  # E: Any
+reveal_type(np.amax(A, keepdims=True))  # E: Any
+reveal_type(np.amax(B, keepdims=True))  # E: Any
+
+reveal_type(np.amin(a))  # E: Any
+reveal_type(np.amin(b))  # E: Any
+reveal_type(np.amin(c))  # E: Any
+reveal_type(np.amin(A))  # E: Any
+reveal_type(np.amin(B))  # E: Any
+reveal_type(np.amin(A, axis=0))  # E: Any
+reveal_type(np.amin(B, axis=0))  # E: Any
+reveal_type(np.amin(A, keepdims=True))  # E: Any
+reveal_type(np.amin(B, keepdims=True))  # E: Any
+
+reveal_type(np.prod(a))  # E: Any
+reveal_type(np.prod(b))  # E: Any
+reveal_type(np.prod(c))  # E: Any
+reveal_type(np.prod(A))  # E: Any
+reveal_type(np.prod(B))  # E: Any
+reveal_type(np.prod(A, axis=0))  # E: Any
+reveal_type(np.prod(B, axis=0))  # E: Any
+reveal_type(np.prod(A, keepdims=True))  # E: Any
+reveal_type(np.prod(B, keepdims=True))  # E: Any
+reveal_type(np.prod(b, out=d))  # E: Any
+reveal_type(np.prod(B, out=d))  # E: Any
 
 reveal_type(np.cumprod(a))  # E: numpy.ndarray
 reveal_type(np.cumprod(b))  # E: numpy.ndarray
@@ -235,44 +221,44 @@ reveal_type(np.size(c))  # E: int
 reveal_type(np.size(A))  # E: int
 reveal_type(np.size(B))  # E: int
 
-reveal_type(np.around(a))  # E: numpy.number[Any]
-reveal_type(np.around(b))  # E: numpy.floating[numpy.typing._32Bit]
-reveal_type(np.around(c))  # E: numpy.number[Any]
-reveal_type(np.around(A))  # E: numpy.ndarray
-reveal_type(np.around(B))  # E: numpy.ndarray
-
-reveal_type(np.mean(a))  # E: numpy.number[Any]
-reveal_type(np.mean(b))  # E: numpy.number[Any]
-reveal_type(np.mean(c))  # E: numpy.number[Any]
-reveal_type(np.mean(A))  # E: numpy.number[Any]
-reveal_type(np.mean(B))  # E: numpy.number[Any]
-reveal_type(np.mean(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.mean(b, out=d))  # E: numpy.ndarray
-reveal_type(np.mean(B, out=d))  # E: numpy.ndarray
-
-reveal_type(np.std(a))  # E: numpy.number[Any]
-reveal_type(np.std(b))  # E: numpy.number[Any]
-reveal_type(np.std(c))  # E: numpy.number[Any]
-reveal_type(np.std(A))  # E: numpy.number[Any]
-reveal_type(np.std(B))  # E: numpy.number[Any]
-reveal_type(np.std(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.std(b, out=d))  # E: numpy.ndarray
-reveal_type(np.std(B, out=d))  # E: numpy.ndarray
-
-reveal_type(np.var(a))  # E: numpy.number[Any]
-reveal_type(np.var(b))  # E: numpy.number[Any]
-reveal_type(np.var(c))  # E: numpy.number[Any]
-reveal_type(np.var(A))  # E: numpy.number[Any]
-reveal_type(np.var(B))  # E: numpy.number[Any]
-reveal_type(np.var(A, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(np.var(b, out=d))  # E: numpy.ndarray
-reveal_type(np.var(B, out=d))  # E: numpy.ndarray
+reveal_type(np.around(a))  # E: Any
+reveal_type(np.around(b))  # E: Any
+reveal_type(np.around(c))  # E: Any
+reveal_type(np.around(A))  # E: Any
+reveal_type(np.around(B))  # E: Any
+
+reveal_type(np.mean(a))  # E: Any
+reveal_type(np.mean(b))  # E: Any
+reveal_type(np.mean(c))  # E: Any
+reveal_type(np.mean(A))  # E: Any
+reveal_type(np.mean(B))  # E: Any
+reveal_type(np.mean(A, axis=0))  # E: Any
+reveal_type(np.mean(B, axis=0))  # E: Any
+reveal_type(np.mean(A, keepdims=True))  # E: Any
+reveal_type(np.mean(B, keepdims=True))  # E: Any
+reveal_type(np.mean(b, out=d))  # E: Any
+reveal_type(np.mean(B, out=d))  # E: Any
+
+reveal_type(np.std(a))  # E: Any
+reveal_type(np.std(b))  # E: Any
+reveal_type(np.std(c))  # E: Any
+reveal_type(np.std(A))  # E: Any
+reveal_type(np.std(B))  # E: Any
+reveal_type(np.std(A, axis=0))  # E: Any
+reveal_type(np.std(B, axis=0))  # E: Any
+reveal_type(np.std(A, keepdims=True))  # E: Any
+reveal_type(np.std(B, keepdims=True))  # E: Any
+reveal_type(np.std(b, out=d))  # E: Any
+reveal_type(np.std(B, out=d))  # E: Any
+
+reveal_type(np.var(a))  # E: Any
+reveal_type(np.var(b))  # E: Any
+reveal_type(np.var(c))  # E: Any
+reveal_type(np.var(A))  # E: Any
+reveal_type(np.var(B))  # E: Any
+reveal_type(np.var(A, axis=0))  # E: Any
+reveal_type(np.var(B, axis=0))  # E: Any
+reveal_type(np.var(A, keepdims=True))  # E: Any
+reveal_type(np.var(B, keepdims=True))  # E: Any
+reveal_type(np.var(b, out=d))  # E: Any
+reveal_type(np.var(B, out=d))  # E: Any
diff --git a/numpy/typing/tests/data/reveal/ndarray_misc.py b/numpy/typing/tests/data/reveal/ndarray_misc.py
index 826c8aaa6..80e1c484e 100644
--- a/numpy/typing/tests/data/reveal/ndarray_misc.py
+++ b/numpy/typing/tests/data/reveal/ndarray_misc.py
@@ -16,24 +16,24 @@ B: SubClass
 
 reveal_type(f8.all())  # E: numpy.bool_
 reveal_type(A.all())  # E: numpy.bool_
-reveal_type(A.all(axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(A.all(keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(A.all(axis=0))  # E: Any
+reveal_type(A.all(keepdims=True))  # E: Any
 reveal_type(A.all(out=B))  # E: SubClass
 
 reveal_type(f8.any())  # E: numpy.bool_
 reveal_type(A.any())  # E: numpy.bool_
-reveal_type(A.any(axis=0))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(A.any(keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(A.any(axis=0))  # E: Any
+reveal_type(A.any(keepdims=True))  # E: Any
 reveal_type(A.any(out=B))  # E: SubClass
 
 reveal_type(f8.argmax())  # E: numpy.signedinteger[Any]
 reveal_type(A.argmax())  # E: numpy.signedinteger[Any]
-reveal_type(A.argmax(axis=0))  # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(A.argmax(axis=0))  # E: Any
 reveal_type(A.argmax(out=B))  # E: SubClass
 
 reveal_type(f8.argmin())  # E: numpy.signedinteger[Any]
 reveal_type(A.argmin())  # E: numpy.signedinteger[Any]
-reveal_type(A.argmin(axis=0))  # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(A.argmin(axis=0))  # E: Any
 reveal_type(A.argmin(out=B))  # E: SubClass
 
 reveal_type(f8.argsort())  # E: numpy.ndarray
@@ -43,9 +43,9 @@ reveal_type(f8.astype(np.int64).choose([()]))  # E: numpy.ndarray
 reveal_type(A.choose([0]))  # E: numpy.ndarray
 reveal_type(A.choose([0], out=B))  # E: SubClass
 
-reveal_type(f8.clip(1))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.clip(1))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.clip(None, 1))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.clip(1))  # E: Any
+reveal_type(A.clip(1))  # E: Any
+reveal_type(A.clip(None, 1))  # E: Any
 reveal_type(A.clip(1, out=B))  # E: SubClass
 reveal_type(A.clip(None, 1, out=B))  # E: SubClass
 
@@ -69,38 +69,38 @@ reveal_type(f8.cumsum())  # E: numpy.ndarray
 reveal_type(A.cumsum())  # E: numpy.ndarray
 reveal_type(A.cumsum(out=B))  # E: SubClass
 
-reveal_type(f8.max())  # E: numpy.number[Any]
-reveal_type(A.max())  # E: numpy.number[Any]
-reveal_type(A.max(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.max(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.max())  # E: Any
+reveal_type(A.max())  # E: Any
+reveal_type(A.max(axis=0))  # E: Any
+reveal_type(A.max(keepdims=True))  # E: Any
 reveal_type(A.max(out=B))  # E: SubClass
 
-reveal_type(f8.mean())  # E: numpy.number[Any]
-reveal_type(A.mean())  # E: numpy.number[Any]
-reveal_type(A.mean(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.mean(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.mean())  # E: Any
+reveal_type(A.mean())  # E: Any
+reveal_type(A.mean(axis=0))  # E: Any
+reveal_type(A.mean(keepdims=True))  # E: Any
 reveal_type(A.mean(out=B))  # E: SubClass
 
-reveal_type(f8.min())  # E: numpy.number[Any]
-reveal_type(A.min())  # E: numpy.number[Any]
-reveal_type(A.min(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.min(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.min())  # E: Any
+reveal_type(A.min())  # E: Any
+reveal_type(A.min(axis=0))  # E: Any
+reveal_type(A.min(keepdims=True))  # E: Any
 reveal_type(A.min(out=B))  # E: SubClass
 
 reveal_type(f8.newbyteorder())  # E: numpy.floating[numpy.typing._64Bit]
 reveal_type(A.newbyteorder())  # E: numpy.ndarray
 reveal_type(B.newbyteorder('|'))  # E: SubClass
 
-reveal_type(f8.prod())  # E: numpy.number[Any]
-reveal_type(A.prod())  # E: numpy.number[Any]
-reveal_type(A.prod(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.prod(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.prod())  # E: Any
+reveal_type(A.prod())  # E: Any
+reveal_type(A.prod(axis=0))  # E: Any
+reveal_type(A.prod(keepdims=True))  # E: Any
 reveal_type(A.prod(out=B))  # E: SubClass
 
-reveal_type(f8.ptp())  # E: numpy.number[Any]
-reveal_type(A.ptp())  # E: numpy.number[Any]
-reveal_type(A.ptp(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.ptp(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.ptp())  # E: Any
+reveal_type(A.ptp())  # E: Any
+reveal_type(A.ptp(axis=0))  # E: Any
+reveal_type(A.ptp(keepdims=True))  # E: Any
 reveal_type(A.ptp(out=B))  # E: SubClass
 
 reveal_type(f8.round())  # E: numpy.floating[numpy.typing._64Bit]
@@ -111,40 +111,40 @@ reveal_type(f8.repeat(1))  # E: numpy.ndarray
 reveal_type(A.repeat(1))  # E: numpy.ndarray
 reveal_type(B.repeat(1))  # E: numpy.ndarray
 
-reveal_type(f8.std())  # E: numpy.number[Any]
-reveal_type(A.std())  # E: numpy.number[Any]
-reveal_type(A.std(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.std(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.std())  # E: Any
+reveal_type(A.std())  # E: Any
+reveal_type(A.std(axis=0))  # E: Any
+reveal_type(A.std(keepdims=True))  # E: Any
 reveal_type(A.std(out=B))  # E: SubClass
 
-reveal_type(f8.sum())  # E: numpy.number[Any]
-reveal_type(A.sum())  # E: numpy.number[Any]
-reveal_type(A.sum(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.sum(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.sum())  # E: Any
+reveal_type(A.sum())  # E: Any
+reveal_type(A.sum(axis=0))  # E: Any
+reveal_type(A.sum(keepdims=True))  # E: Any
 reveal_type(A.sum(out=B))  # E: SubClass
 
-reveal_type(f8.take(0))  # E: numpy.generic
-reveal_type(A.take(0))  # E: numpy.generic
+reveal_type(f8.take(0))  # E: Any
+reveal_type(A.take(0))  # E: Any
 reveal_type(A.take([0]))  # E: numpy.ndarray
 reveal_type(A.take(0, out=B))  # E: SubClass
 reveal_type(A.take([0], out=B))  # E: SubClass
 
-reveal_type(f8.var())  # E: numpy.number[Any]
-reveal_type(A.var())  # E: numpy.number[Any]
-reveal_type(A.var(axis=0))  # E: Union[numpy.number[Any], numpy.ndarray]
-reveal_type(A.var(keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(f8.var())  # E: Any
+reveal_type(A.var())  # E: Any
+reveal_type(A.var(axis=0))  # E: Any
+reveal_type(A.var(keepdims=True))  # E: Any
 reveal_type(A.var(out=B))  # E: SubClass
 
 reveal_type(A.argpartition([0]))  # E: numpy.ndarray
 
 reveal_type(A.diagonal())  # E: numpy.ndarray
 
-reveal_type(A.dot(1))  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(A.dot(1))  # E: Any
 reveal_type(A.dot(1, out=B))  # E: SubClass
 
 reveal_type(A.nonzero())  # E: tuple[numpy.ndarray]
 
 reveal_type(A.searchsorted([1]))  # E: numpy.ndarray
 
-reveal_type(A.trace())  # E: Union[numpy.number[Any], numpy.ndarray]
+reveal_type(A.trace())  # E: Any
 reveal_type(A.trace(out=B))  # E: SubClass
diff --git a/numpy/typing/tests/data/reveal/numeric.py b/numpy/typing/tests/data/reveal/numeric.py
index 5cbfa4ac7..a3671cef9 100644
--- a/numpy/typing/tests/data/reveal/numeric.py
+++ b/numpy/typing/tests/data/reveal/numeric.py
@@ -20,8 +20,8 @@ C: SubClass
 reveal_type(np.count_nonzero(i8))  # E: int
 reveal_type(np.count_nonzero(A))  # E: int
 reveal_type(np.count_nonzero(B))  # E: int
-reveal_type(np.count_nonzero(A, keepdims=True))  # E: Union[numpy.signedinteger[Any], numpy.ndarray]
-reveal_type(np.count_nonzero(A, axis=0))  # E: Union[numpy.signedinteger[Any], numpy.ndarray]
+reveal_type(np.count_nonzero(A, keepdims=True))  # E: Any
+reveal_type(np.count_nonzero(A, axis=0))  # E: Any
 
 reveal_type(np.isfortran(i8))  # E: bool
 reveal_type(np.isfortran(A))  # E: bool
@@ -76,9 +76,9 @@ reveal_type(np.allclose(i8, A))  # E: bool
 reveal_type(np.allclose(B, A))  # E: bool
 reveal_type(np.allclose(A, A))  # E: bool
 
-reveal_type(np.isclose(i8, A))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.isclose(B, A))  # E: Union[numpy.bool_, numpy.ndarray]
-reveal_type(np.isclose(A, A))  # E: Union[numpy.bool_, numpy.ndarray]
+reveal_type(np.isclose(i8, A))  # E: Any
+reveal_type(np.isclose(B, A))  # E: Any
+reveal_type(np.isclose(A, A))  # E: Any
 
 reveal_type(np.array_equal(i8, A))  # E: bool
 reveal_type(np.array_equal(B, A))  # E: bool