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📄 __init__.py
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📄 __init__.pyi
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📁 __pycache__/
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📄 _datasource.py
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📄 _iotools.py
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📄 _version.py
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📄 _version.pyi
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📄 arraypad.py
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📄 arraypad.pyi
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📄 arraysetops.py
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📄 arraysetops.pyi
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📄 arrayterator.py
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📄 arrayterator.pyi
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📄 format.py
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📄 format.pyi
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📄 function_base.py
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📄 function_base.pyi
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📄 histograms.py
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📄 histograms.pyi
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📄 index_tricks.py
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📄 index_tricks.pyi
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📄 mixins.py
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📄 mixins.pyi
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📄 nanfunctions.py
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📄 nanfunctions.pyi
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📄 npyio.py
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📄 npyio.pyi
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📄 polynomial.py
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📄 polynomial.pyi
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📄 recfunctions.py
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📄 scimath.py
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📄 scimath.pyi
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📄 setup.py
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📄 shape_base.py
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📄 shape_base.pyi
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📄 stride_tricks.py
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📄 stride_tricks.pyi
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📁 tests/
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📄 twodim_base.py
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📄 twodim_base.pyi
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📄 type_check.py
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📄 type_check.pyi
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📄 ufunclike.py
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📄 ufunclike.pyi
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📄 user_array.py
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📄 utils.py
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📄 utils.pyi
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EDITING: stride_tricks.pyi
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]]: ...
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