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 = ..., ) -> list[NDArray[Any]]: ...