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							7.3 KiB
						
					
					
				
			
		
		
	
	
							283 lines
						
					
					
						
							7.3 KiB
						
					
					
				from collections.abc import Iterable
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from typing import (
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    Literal as L,
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    overload,
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    TypeVar,
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    Any,
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    SupportsIndex,
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    SupportsInt,
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)
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from numpy import (
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    generic,
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    floating,
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    complexfloating,
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    int32,
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    float64,
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    complex128,
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)
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from numpy.linalg import LinAlgError as LinAlgError
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from numpy._typing import (
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    NDArray,
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    ArrayLike,
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    _ArrayLikeInt_co,
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    _ArrayLikeFloat_co,
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    _ArrayLikeComplex_co,
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    _ArrayLikeTD64_co,
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    _ArrayLikeObject_co,
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)
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_T = TypeVar("_T")
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_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])
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_2Tuple = tuple[_T, _T]
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_ModeKind = L["reduced", "complete", "r", "raw"]
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__all__: list[str]
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@overload
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def tensorsolve(
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    a: _ArrayLikeInt_co,
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    b: _ArrayLikeInt_co,
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    axes: None | Iterable[int] =...,
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) -> NDArray[float64]: ...
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@overload
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def tensorsolve(
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    a: _ArrayLikeFloat_co,
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    b: _ArrayLikeFloat_co,
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    axes: None | Iterable[int] =...,
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) -> NDArray[floating[Any]]: ...
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@overload
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def tensorsolve(
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    a: _ArrayLikeComplex_co,
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    b: _ArrayLikeComplex_co,
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    axes: None | Iterable[int] =...,
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) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def solve(
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    a: _ArrayLikeInt_co,
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    b: _ArrayLikeInt_co,
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) -> NDArray[float64]: ...
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@overload
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def solve(
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    a: _ArrayLikeFloat_co,
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    b: _ArrayLikeFloat_co,
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) -> NDArray[floating[Any]]: ...
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@overload
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def solve(
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    a: _ArrayLikeComplex_co,
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    b: _ArrayLikeComplex_co,
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) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def tensorinv(
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    a: _ArrayLikeInt_co,
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    ind: int = ...,
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) -> NDArray[float64]: ...
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@overload
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def tensorinv(
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    a: _ArrayLikeFloat_co,
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    ind: int = ...,
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) -> NDArray[floating[Any]]: ...
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@overload
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def tensorinv(
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    a: _ArrayLikeComplex_co,
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    ind: int = ...,
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) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def inv(a: _ArrayLikeInt_co) -> NDArray[float64]: ...
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@overload
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def inv(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
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@overload
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def inv(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
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# TODO: The supported input and output dtypes are dependent on the value of `n`.
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# For example: `n < 0` always casts integer types to float64
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def matrix_power(
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    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
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    n: SupportsIndex,
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) -> NDArray[Any]: ...
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@overload
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def cholesky(a: _ArrayLikeInt_co) -> NDArray[float64]: ...
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@overload
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def cholesky(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
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@overload
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def cholesky(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[float64]]: ...
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@overload
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def qr(a: _ArrayLikeFloat_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[floating[Any]]]: ...
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@overload
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def qr(a: _ArrayLikeComplex_co, mode: _ModeKind = ...) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
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@overload
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def eigvals(a: _ArrayLikeInt_co) -> NDArray[float64] | NDArray[complex128]: ...
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@overload
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def eigvals(a: _ArrayLikeFloat_co) -> NDArray[floating[Any]] | NDArray[complexfloating[Any, Any]]: ...
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@overload
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def eigvals(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def eigvalsh(a: _ArrayLikeInt_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[float64]: ...
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@overload
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def eigvalsh(a: _ArrayLikeComplex_co, UPLO: L["L", "U", "l", "u"] = ...) -> NDArray[floating[Any]]: ...
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@overload
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def eig(a: _ArrayLikeInt_co) -> _2Tuple[NDArray[float64]] | _2Tuple[NDArray[complex128]]: ...
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@overload
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def eig(a: _ArrayLikeFloat_co) -> _2Tuple[NDArray[floating[Any]]] | _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
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@overload
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def eig(a: _ArrayLikeComplex_co) -> _2Tuple[NDArray[complexfloating[Any, Any]]]: ...
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@overload
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def eigh(
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    a: _ArrayLikeInt_co,
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    UPLO: L["L", "U", "l", "u"] = ...,
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) -> tuple[NDArray[float64], NDArray[float64]]: ...
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@overload
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def eigh(
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    a: _ArrayLikeFloat_co,
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    UPLO: L["L", "U", "l", "u"] = ...,
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) -> tuple[NDArray[floating[Any]], NDArray[floating[Any]]]: ...
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@overload
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def eigh(
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    a: _ArrayLikeComplex_co,
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    UPLO: L["L", "U", "l", "u"] = ...,
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) -> tuple[NDArray[floating[Any]], NDArray[complexfloating[Any, Any]]]: ...
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@overload
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def svd(
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    a: _ArrayLikeInt_co,
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    full_matrices: bool = ...,
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    compute_uv: L[True] = ...,
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    hermitian: bool = ...,
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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 svd(
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    a: _ArrayLikeFloat_co,
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    full_matrices: bool = ...,
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    compute_uv: L[True] = ...,
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    hermitian: bool = ...,
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) -> tuple[
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    NDArray[floating[Any]],
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    NDArray[floating[Any]],
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    NDArray[floating[Any]],
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]: ...
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@overload
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def svd(
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    a: _ArrayLikeComplex_co,
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    full_matrices: bool = ...,
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    compute_uv: L[True] = ...,
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    hermitian: bool = ...,
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) -> tuple[
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    NDArray[complexfloating[Any, Any]],
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    NDArray[floating[Any]],
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    NDArray[complexfloating[Any, Any]],
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]: ...
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@overload
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def svd(
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    a: _ArrayLikeInt_co,
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    full_matrices: bool = ...,
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    compute_uv: L[False] = ...,
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    hermitian: bool = ...,
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) -> NDArray[float64]: ...
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@overload
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def svd(
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    a: _ArrayLikeComplex_co,
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    full_matrices: bool = ...,
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    compute_uv: L[False] = ...,
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    hermitian: bool = ...,
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) -> NDArray[floating[Any]]: ...
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# TODO: Returns a scalar for 2D arrays and
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# a `(x.ndim - 2)`` dimensionl array otherwise
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def cond(x: _ArrayLikeComplex_co, p: None | float | L["fro", "nuc"] = ...) -> Any: ...
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# TODO: Returns `int` for <2D arrays and `intp` otherwise
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def matrix_rank(
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    A: _ArrayLikeComplex_co,
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    tol: None | _ArrayLikeFloat_co = ...,
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    hermitian: bool = ...,
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) -> Any: ...
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@overload
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def pinv(
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    a: _ArrayLikeInt_co,
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    rcond: _ArrayLikeFloat_co = ...,
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    hermitian: bool = ...,
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) -> NDArray[float64]: ...
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@overload
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def pinv(
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    a: _ArrayLikeFloat_co,
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    rcond: _ArrayLikeFloat_co = ...,
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    hermitian: bool = ...,
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) -> NDArray[floating[Any]]: ...
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@overload
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def pinv(
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    a: _ArrayLikeComplex_co,
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    rcond: _ArrayLikeFloat_co = ...,
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    hermitian: bool = ...,
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) -> NDArray[complexfloating[Any, Any]]: ...
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# TODO: Returns a 2-tuple of scalars for 2D arrays and
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# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
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def slogdet(a: _ArrayLikeComplex_co) -> _2Tuple[Any]: ...
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# TODO: Returns a 2-tuple of scalars for 2D arrays and
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# a 2-tuple of `(a.ndim - 2)`` dimensionl arrays otherwise
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def det(a: _ArrayLikeComplex_co) -> Any: ...
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@overload
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def lstsq(a: _ArrayLikeInt_co, b: _ArrayLikeInt_co, rcond: None | float = ...) -> tuple[
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    NDArray[float64],
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    NDArray[float64],
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    int32,
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    NDArray[float64],
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]: ...
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@overload
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def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: None | float = ...) -> tuple[
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    NDArray[floating[Any]],
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    NDArray[floating[Any]],
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    int32,
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    NDArray[floating[Any]],
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]: ...
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@overload
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def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: None | float = ...) -> tuple[
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    NDArray[complexfloating[Any, Any]],
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    NDArray[floating[Any]],
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    int32,
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    NDArray[floating[Any]],
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]: ...
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@overload
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def norm(
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    x: ArrayLike,
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    ord: None | float | L["fro", "nuc"] = ...,
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    axis: None = ...,
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    keepdims: bool = ...,
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) -> floating[Any]: ...
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@overload
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def norm(
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    x: ArrayLike,
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    ord: None | float | L["fro", "nuc"] = ...,
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    axis: SupportsInt | SupportsIndex | tuple[int, ...] = ...,
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    keepdims: bool = ...,
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) -> Any: ...
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# TODO: Returns a scalar or array
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def multi_dot(
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    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
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    *,
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    out: None | NDArray[Any] = ...,
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) -> Any: ...
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