438 lines
11 KiB
Python
438 lines
11 KiB
Python
from collections.abc import Callable, Sequence
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from typing import (
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Any,
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TypeAlias,
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TypeVar,
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overload,
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)
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from typing import (
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Literal as L,
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)
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import numpy as np
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from numpy import (
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_OrderCF,
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complex128,
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complexfloating,
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datetime64,
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float64,
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floating,
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generic,
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int_,
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intp,
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object_,
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signedinteger,
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timedelta64,
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)
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from numpy._typing import (
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ArrayLike,
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DTypeLike,
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NDArray,
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_ArrayLike,
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_ArrayLikeComplex_co,
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_ArrayLikeFloat_co,
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_ArrayLikeInt_co,
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_ArrayLikeObject_co,
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_DTypeLike,
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_SupportsArray,
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_SupportsArrayFunc,
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)
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__all__ = [
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"diag",
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"diagflat",
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"eye",
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"fliplr",
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"flipud",
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"tri",
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"triu",
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"tril",
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"vander",
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"histogram2d",
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"mask_indices",
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"tril_indices",
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"tril_indices_from",
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"triu_indices",
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"triu_indices_from",
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]
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###
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_T = TypeVar("_T")
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_ScalarT = TypeVar("_ScalarT", bound=generic)
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_ComplexFloatingT = TypeVar("_ComplexFloatingT", bound=np.complexfloating)
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_InexactT = TypeVar("_InexactT", bound=np.inexact)
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_NumberCoT = TypeVar("_NumberCoT", bound=_Number_co)
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# The returned arrays dtype must be compatible with `np.equal`
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_MaskFunc: TypeAlias = Callable[[NDArray[int_], _T], NDArray[_Number_co | timedelta64 | datetime64 | object_]]
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_Int_co: TypeAlias = np.integer | np.bool
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_Float_co: TypeAlias = np.floating | _Int_co
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_Number_co: TypeAlias = np.number | np.bool
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_ArrayLike1D: TypeAlias = _SupportsArray[np.dtype[_ScalarT]] | Sequence[_ScalarT]
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_ArrayLike1DInt_co: TypeAlias = _SupportsArray[np.dtype[_Int_co]] | Sequence[int | _Int_co]
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_ArrayLike1DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[float | _Float_co]
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_ArrayLike2DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[_ArrayLike1DFloat_co]
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_ArrayLike1DNumber_co: TypeAlias = _SupportsArray[np.dtype[_Number_co]] | Sequence[complex | _Number_co]
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###
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@overload
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def fliplr(m: _ArrayLike[_ScalarT]) -> NDArray[_ScalarT]: ...
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@overload
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def fliplr(m: ArrayLike) -> NDArray[Any]: ...
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@overload
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def flipud(m: _ArrayLike[_ScalarT]) -> NDArray[_ScalarT]: ...
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@overload
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def flipud(m: ArrayLike) -> NDArray[Any]: ...
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@overload
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def eye(
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N: int,
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M: int | None = ...,
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k: int = ...,
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dtype: None = ...,
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order: _OrderCF = ...,
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*,
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device: L["cpu"] | None = ...,
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like: _SupportsArrayFunc | None = ...,
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) -> NDArray[float64]: ...
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@overload
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def eye(
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N: int,
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M: int | None,
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k: int,
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dtype: _DTypeLike[_ScalarT],
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order: _OrderCF = ...,
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*,
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device: L["cpu"] | None = ...,
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like: _SupportsArrayFunc | None = ...,
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) -> NDArray[_ScalarT]: ...
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@overload
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def eye(
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N: int,
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M: int | None = ...,
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k: int = ...,
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*,
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dtype: _DTypeLike[_ScalarT],
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order: _OrderCF = ...,
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device: L["cpu"] | None = ...,
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like: _SupportsArrayFunc | None = ...,
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) -> NDArray[_ScalarT]: ...
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@overload
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def eye(
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N: int,
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M: int | None = ...,
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k: int = ...,
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dtype: DTypeLike = ...,
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order: _OrderCF = ...,
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*,
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device: L["cpu"] | None = ...,
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like: _SupportsArrayFunc | None = ...,
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) -> NDArray[Any]: ...
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@overload
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def diag(v: _ArrayLike[_ScalarT], k: int = ...) -> NDArray[_ScalarT]: ...
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@overload
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def diag(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
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@overload
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def diagflat(v: _ArrayLike[_ScalarT], k: int = ...) -> NDArray[_ScalarT]: ...
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@overload
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def diagflat(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
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@overload
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def tri(
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N: int,
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M: int | None = ...,
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k: int = ...,
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dtype: None = ...,
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*,
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like: _SupportsArrayFunc | None = ...
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) -> NDArray[float64]: ...
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@overload
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def tri(
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N: int,
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M: int | None,
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k: int,
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dtype: _DTypeLike[_ScalarT],
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*,
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like: _SupportsArrayFunc | None = ...
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) -> NDArray[_ScalarT]: ...
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@overload
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def tri(
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N: int,
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M: int | None = ...,
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k: int = ...,
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*,
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dtype: _DTypeLike[_ScalarT],
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like: _SupportsArrayFunc | None = ...
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) -> NDArray[_ScalarT]: ...
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@overload
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def tri(
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N: int,
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M: int | None = ...,
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k: int = ...,
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dtype: DTypeLike = ...,
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*,
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like: _SupportsArrayFunc | None = ...
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) -> NDArray[Any]: ...
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@overload
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def tril(m: _ArrayLike[_ScalarT], k: int = 0) -> NDArray[_ScalarT]: ...
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@overload
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def tril(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
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@overload
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def triu(m: _ArrayLike[_ScalarT], k: int = 0) -> NDArray[_ScalarT]: ...
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@overload
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def triu(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
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@overload
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def vander( # type: ignore[misc]
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x: _ArrayLikeInt_co,
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N: int | None = ...,
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increasing: bool = ...,
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) -> NDArray[signedinteger]: ...
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@overload
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def vander( # type: ignore[misc]
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x: _ArrayLikeFloat_co,
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N: int | None = ...,
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increasing: bool = ...,
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) -> NDArray[floating]: ...
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@overload
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def vander(
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x: _ArrayLikeComplex_co,
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N: int | None = ...,
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increasing: bool = ...,
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) -> NDArray[complexfloating]: ...
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@overload
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def vander(
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x: _ArrayLikeObject_co,
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N: int | None = ...,
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increasing: bool = ...,
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) -> NDArray[object_]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1D[_ComplexFloatingT],
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y: _ArrayLike1D[_ComplexFloatingT | _Float_co],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_ComplexFloatingT],
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NDArray[_ComplexFloatingT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1D[_ComplexFloatingT | _Float_co],
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y: _ArrayLike1D[_ComplexFloatingT],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_ComplexFloatingT],
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NDArray[_ComplexFloatingT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1D[_InexactT],
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y: _ArrayLike1D[_InexactT | _Int_co],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_InexactT],
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NDArray[_InexactT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1D[_InexactT | _Int_co],
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y: _ArrayLike1D[_InexactT],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_InexactT],
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NDArray[_InexactT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DInt_co | Sequence[float],
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y: _ArrayLike1DInt_co | Sequence[float],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[float64],
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NDArray[float64],
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]: ...
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@overload
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def histogram2d(
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x: Sequence[complex],
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y: Sequence[complex],
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bins: int | Sequence[int] = ...,
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[complex128 | float64],
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NDArray[complex128 | float64],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DNumber_co,
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y: _ArrayLike1DNumber_co,
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bins: _ArrayLike1D[_NumberCoT] | Sequence[_ArrayLike1D[_NumberCoT]],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_NumberCoT],
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NDArray[_NumberCoT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1D[_InexactT],
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y: _ArrayLike1D[_InexactT],
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bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_NumberCoT | _InexactT],
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NDArray[_NumberCoT | _InexactT],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DInt_co | Sequence[float],
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y: _ArrayLike1DInt_co | Sequence[float],
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bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_NumberCoT | float64],
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NDArray[_NumberCoT | float64],
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]: ...
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@overload
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def histogram2d(
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x: Sequence[complex],
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y: Sequence[complex],
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bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[_NumberCoT | complex128 | float64],
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NDArray[_NumberCoT | complex128 | float64],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DNumber_co,
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y: _ArrayLike1DNumber_co,
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bins: Sequence[Sequence[bool]],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[np.bool],
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NDArray[np.bool],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DNumber_co,
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y: _ArrayLike1DNumber_co,
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bins: Sequence[Sequence[int]],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[np.int_ | np.bool],
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NDArray[np.int_ | np.bool],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DNumber_co,
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y: _ArrayLike1DNumber_co,
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bins: Sequence[Sequence[float]],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[np.float64 | np.int_ | np.bool],
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NDArray[np.float64 | np.int_ | np.bool],
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]: ...
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@overload
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def histogram2d(
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x: _ArrayLike1DNumber_co,
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y: _ArrayLike1DNumber_co,
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bins: Sequence[Sequence[complex]],
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range: _ArrayLike2DFloat_co | None = ...,
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density: bool | None = ...,
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weights: _ArrayLike1DFloat_co | None = ...,
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) -> tuple[
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NDArray[float64],
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NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
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NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
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]: ...
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# NOTE: we're assuming/demanding here the `mask_func` returns
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# an ndarray of shape `(n, n)`; otherwise there is the possibility
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# of the output tuple having more or less than 2 elements
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@overload
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def mask_indices(
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n: int,
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mask_func: _MaskFunc[int],
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k: int = ...,
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) -> tuple[NDArray[intp], NDArray[intp]]: ...
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@overload
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def mask_indices(
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n: int,
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mask_func: _MaskFunc[_T],
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k: _T,
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) -> tuple[NDArray[intp], NDArray[intp]]: ...
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def tril_indices(
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n: int,
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k: int = ...,
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m: int | None = ...,
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) -> tuple[NDArray[int_], NDArray[int_]]: ...
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def tril_indices_from(
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arr: NDArray[Any],
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k: int = ...,
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) -> tuple[NDArray[int_], NDArray[int_]]: ...
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def triu_indices(
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n: int,
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k: int = ...,
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m: int | None = ...,
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) -> tuple[NDArray[int_], NDArray[int_]]: ...
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def triu_indices_from(
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arr: NDArray[Any],
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k: int = ...,
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) -> tuple[NDArray[int_], NDArray[int_]]: ...
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