from collections.abc import Iterable
from typing import Any, TypeVar, overload, SupportsIndex
from numpy import generic
from numpy._typing import (
NDArray,
ArrayLike,
_ShapeLike,
_Shape,
_ArrayLike
)
_SCT = TypeVar("_SCT", bound=generic)
__all__: list[str]
class DummyArray:
__array_interface__: dict[str, Any]
base: None | NDArray[Any]
def __init__(
self,
interface: dict[str, Any],
base: None | NDArray[Any] = ...,
) -> None: ...
@overload
def as_strided(
x: _ArrayLike[_SCT],
shape: None | Iterable[int] = ...,
strides: None | Iterable[int] = ...,
subok: bool = ...,
writeable: bool = ...,
) -> NDArray[_SCT]: ...
@overload
def as_strided(
x: ArrayLike,
shape: None | Iterable[int] = ...,
strides: None | Iterable[int] = ...,
subok: bool = ...,
writeable: bool = ...,
) -> NDArray[Any]: ...
@overload
def sliding_window_view(
x: _ArrayLike[_SCT],
window_shape: int | Iterable[int],
axis: None | SupportsIndex = ...,
*,
subok: bool = ...,
writeable: bool = ...,
) -> NDArray[_SCT]: ...
@overload
def sliding_window_view(
x: ArrayLike,
window_shape: int | Iterable[int],
axis: None | SupportsIndex = ...,
*,
subok: bool = ...,
writeable: bool = ...,
) -> NDArray[Any]: ...
@overload
def broadcast_to(
array: _ArrayLike[_SCT],
shape: int | Iterable[int],
subok: bool = ...,
) -> NDArray[_SCT]: ...
@overload
def broadcast_to(
array: ArrayLike,
shape: int | Iterable[int],
subok: bool = ...,
) -> NDArray[Any]: ...
def broadcast_shapes(*args: _ShapeLike) -> _Shape: ...
def broadcast_arrays(
*args: ArrayLike,
subok: bool = ...,
) -> tuple[NDArray[Any], ...]: ...