MAINT: Remove unsafe unions from `np.core.fromnumeric`

3 files changed, 286 insertions, 422 deletions

diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi
index fc7f28a59..26a43c1a0 100644
--- a/numpy/core/fromnumeric.pyi
+++ b/numpy/core/fromnumeric.pyi
@@ -23,7 +23,6 @@ from numpy.typing import (
     ArrayLike,
     _ShapeLike,
     _Shape,
-    _IntLike_co,
     _NumberLike_co,
 )
 
@@ -42,11 +41,7 @@ _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)
 
@@ -55,67 +50,51 @@ _Number = TypeVar("_Number", bound=number)
 # 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: _IntLike_co, 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,
@@ -123,54 +102,55 @@ 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]] = ...,
-) -> intp: ...
-@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] = ...) -> intp: ...
+def argmax(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> intp: ...
 @overload
 def argmax(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[ndarray, intp]: ...
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
 @overload
-def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> intp: ...
+def argmin(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+) -> intp: ...
 @overload
 def argmin(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[ndarray, intp]: ...
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> Any: ...
+
 @overload
 def searchsorted(
     a: ArrayLike,
@@ -185,14 +165,30 @@ 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 +196,21 @@ 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 +218,7 @@ def clip(
     a_max: Optional[ArrayLike],
     out: Optional[ndarray] = ...,
     **kwargs: Any,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
 @overload
 def clip(
     a: ArrayLike,
@@ -241,18 +226,8 @@ 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_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
+) -> Any: ...
+
 def sum(
     a: ArrayLike,
     axis: _ShapeLike = ...,
@@ -261,12 +236,13 @@ def sum(
     keepdims: bool = ...,
     initial: _NumberLike_co = ...,
     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 +251,13 @@ 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 +266,22 @@ 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_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amax(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def amax(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -343,26 +289,8 @@ def amax(
     keepdims: bool = ...,
     initial: _NumberLike_co = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amin(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amin(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
+) -> Any: ...
+
 def amin(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -370,7 +298,7 @@ def amin(
     keepdims: bool = ...,
     initial: _NumberLike_co = ...,
     where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
 
 # TODO: `np.prod()``: For object arrays `initial` does not necessarily
 # have to be a numerical scalar.
@@ -379,27 +307,6 @@ 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_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def prod(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DTypeLike = ...,
-    out: None = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike_co = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
 def prod(
     a: ArrayLike,
     axis: Optional[_ShapeLike] = ...,
@@ -408,53 +315,33 @@ def prod(
     keepdims: bool = ...,
     initial: _NumberLike_co = ...,
     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_co, 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 +349,8 @@ 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 +358,4 @@ def var(
     out: Optional[ndarray] = ...,
     ddof: int = ...,
     keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
+) -> Any: ...
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/fromnumeric.py b/numpy/typing/tests/data/reveal/fromnumeric.py
index 2b58f019f..bbcfbb85a 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: {float32}
-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[Any, Any]]
-        A, [0]
-    )
-)
-reveal_type(
-    np.take(  # E: Union[Union[numpy.generic, datetime.datetime, datetime.timedelta], numpy.ndarray[Any, Any]]
-        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[Any, Any]
 reveal_type(np.reshape(b, 1))  # E: numpy.ndarray[Any, Any]
@@ -40,8 +26,8 @@ reveal_type(np.reshape(c, 1))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.reshape(A, 1))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.reshape(B, 1))  # E: numpy.ndarray[Any, Any]
 
-reveal_type(np.choose(a, [True, True]))  # E: numpy.bool_
-reveal_type(np.choose(A, [True, True]))  # E: numpy.ndarray[Any, Any]
+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[Any, Any]
 reveal_type(np.repeat(b, 1))  # E: numpy.ndarray[Any, Any]
@@ -66,11 +52,11 @@ reveal_type(np.partition(c, 0, axis=None))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.partition(A, 0))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.partition(B, 0))  # E: numpy.ndarray[Any, Any]
 
-reveal_type(np.argpartition(a, 0))  # E: {intp}
-reveal_type(np.argpartition(b, 0))  # E: {intp}
-reveal_type(np.argpartition(c, 0))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.argpartition(A, 0))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.argpartition(B, 0))  # E: numpy.ndarray[Any, Any]
+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[Any, Any]
 reveal_type(np.sort(B, 0))  # E: numpy.ndarray[Any, Any]
@@ -80,13 +66,13 @@ reveal_type(np.argsort(B, 0))  # E: numpy.ndarray[Any, Any]
 
 reveal_type(np.argmax(A))  # E: {intp}
 reveal_type(np.argmax(B))  # E: {intp}
-reveal_type(np.argmax(A, axis=0))  # E: Union[numpy.ndarray[Any, Any], {intp}]
-reveal_type(np.argmax(B, axis=0))  # E: Union[numpy.ndarray[Any, Any], {intp}]
+reveal_type(np.argmax(A, axis=0))  # E: Any
+reveal_type(np.argmax(B, axis=0))  # E: Any
 
 reveal_type(np.argmin(A))  # E: {intp}
 reveal_type(np.argmin(B))  # E: {intp}
-reveal_type(np.argmin(A, axis=0))  # E: Union[numpy.ndarray[Any, Any], {intp}]
-reveal_type(np.argmin(B, axis=0))  # E: Union[numpy.ndarray[Any, Any], {intp}]
+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: {intp}
 reveal_type(np.searchsorted(B[0], 0))  # E: {intp}
@@ -108,8 +94,8 @@ reveal_type(np.squeeze(B))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.diagonal(A))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.diagonal(B))  # E: numpy.ndarray[Any, Any]
 
-reveal_type(np.trace(A))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.trace(B))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
+reveal_type(np.trace(A))  # E: Any
+reveal_type(np.trace(B))  # E: Any
 
 reveal_type(np.ravel(a))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.ravel(b))  # E: numpy.ndarray[Any, Any]
@@ -135,39 +121,39 @@ reveal_type(np.compress([True], c))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.compress([True], A))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.compress([True], B))  # E: numpy.ndarray[Any, Any]
 
-reveal_type(np.clip(a, 0, 1.0))  # E: numpy.number[Any]
-reveal_type(np.clip(b, -1, 1))  # E: {float32}
-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[Any, Any]]
-reveal_type(np.clip(B, 0, 1))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
+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: {float32}
-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[Any, Any]]
-reveal_type(np.sum(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
+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[Any, Any]]
-reveal_type(np.all(B, axis=0))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
-reveal_type(np.all(A, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
-reveal_type(np.all(B, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
+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[Any, Any]]
-reveal_type(np.any(B, axis=0))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
-reveal_type(np.any(A, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
-reveal_type(np.any(B, keepdims=True))  # E: Union[numpy.bool_, numpy.ndarray[Any, Any]]
+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[Any, Any]
 reveal_type(np.cumsum(b))  # E: numpy.ndarray[Any, Any]
@@ -175,47 +161,47 @@ reveal_type(np.cumsum(c))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.cumsum(A))  # E: numpy.ndarray[Any, Any]
 reveal_type(np.cumsum(B))  # E: numpy.ndarray[Any, Any]
 
-reveal_type(np.ptp(a))  # E: numpy.number[Any]
-reveal_type(np.ptp(b))  # E: {float32}
-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[Any, Any]]
-reveal_type(np.ptp(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.ptp(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.ptp(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-
-reveal_type(np.amax(a))  # E: numpy.number[Any]
-reveal_type(np.amax(b))  # E: {float32}
-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[Any, Any]]
-reveal_type(np.amax(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.amax(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.amax(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-
-reveal_type(np.amin(a))  # E: numpy.number[Any]
-reveal_type(np.amin(b))  # E: {float32}
-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[Any, Any]]
-reveal_type(np.amin(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.amin(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.amin(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-
-reveal_type(np.prod(a))  # E: numpy.number[Any]
-reveal_type(np.prod(b))  # E: {float32}
-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[Any, Any]]
-reveal_type(np.prod(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.prod(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.prod(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.prod(b, out=d))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.prod(B, out=d))  # E: numpy.ndarray[Any, Any]
+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[Any, Any]
 reveal_type(np.cumprod(b))  # E: numpy.ndarray[Any, Any]
@@ -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: {float32}
-reveal_type(np.around(c))  # E: numpy.number[Any]
-reveal_type(np.around(A))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.around(B))  # E: numpy.ndarray[Any, Any]
-
-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[Any, Any]]
-reveal_type(np.mean(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.mean(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.mean(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.mean(b, out=d))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.mean(B, out=d))  # E: numpy.ndarray[Any, Any]
-
-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[Any, Any]]
-reveal_type(np.std(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.std(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.std(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.std(b, out=d))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.std(B, out=d))  # E: numpy.ndarray[Any, Any]
-
-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[Any, Any]]
-reveal_type(np.var(B, axis=0))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.var(A, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.var(B, keepdims=True))  # E: Union[numpy.number[Any], numpy.ndarray[Any, Any]]
-reveal_type(np.var(b, out=d))  # E: numpy.ndarray[Any, Any]
-reveal_type(np.var(B, out=d))  # E: numpy.ndarray[Any, Any]
+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