ronantakizawa/codereview-bench huggingface dataset:
Repository: numpy/numpy
File: numpy/_core/tests/test_numeric.py (Python)

Code before:
        f = np.False_
        t = np.True_
        assert_((t | t) is t)
        assert_((f | t) is t)
        assert_((t | f) is t)
        assert_((f | f) is f)

    def test_bitwise_and(self):
        f = np.False_
        t = np.True_
        assert_((t & t) is t)
        assert_((f & t) is f)
        assert_((t & f) is f)
        assert_((f & f) is f)

    def test_bitwise_xor(self):
        f = np.False_
        t = np.True_
        assert_((t ^ t) is f)
        assert_((f ^ t) is t)
        assert_((t ^ f) is t)
        assert_((f ^ f) is f)


class TestBoolArray:
    def _create_arrays(self):
        # offset for simd tests
        t = np.array([True] * 41, dtype=bool)[1::]
        f = np.array([False] * 41, dtype=bool)[1::]
        o = np.array([False] * 42, dtype=bool)[2::]
        nm = f.copy()
        im = t.copy()
        nm[3] = True
        nm[-2] = True
        im[3] = False
        im[-2] = False
        return t, f, o, nm, im

    def test_all_any(self):
        t, f, _, nm, im = self._create_arrays()
        assert_(t.all())
        assert_(t.any())
        assert_(not f.all())
        assert_(not f.any())
        assert_(nm.any())
        assert_(im.any())
        assert_(not nm.all())
        assert_(not im.all())
        # check bad element in all positions
        for i in range(256 - 7):
            d = np.array([False] * 256, dtype=bool)[7::]

Review comment (suggestion):
since we already use pytest wouldn't be better to turn it to a fixture?

also more descriptive names, like `bool_arrays` are usually preferred 

Code after:
        f = np.False_
        t = np.True_
        assert_((t | t) is t)
        assert_((f | t) is t)
        assert_((t | f) is t)
        assert_((f | f) is f)

    def test_bitwise_and(self):
        f = np.False_
        t = np.True_
        assert_((t & t) is t)
        assert_((f & t) is f)
        assert_((t & f) is f)
        assert_((f & f) is f)

    def test_bitwise_xor(self):
        f = np.False_
        t = np.True_
        assert_((t ^ t) is f)
        assert_((f ^ t) is t)
        assert_((t ^ f) is t)
        assert_((f ^ f) is f)


class TestBoolArray:
    def _create_bool_arrays(self):
        # offset for simd tests
        t = np.array([True] * 41, dtype=bool)[1::]
        f = np.array([False] * 41, dtype=bool)[1::]
        o = np.array([False] * 42, dtype=bool)[2::]
        nm = f.copy()
        im = t.copy()
        nm[3] = True
        nm[-2] = True
        im[3] = False
        im[-2] = False
        return t, f, o, nm, im

    def test_all_any(self):
        t, f, _, nm, im = self._create_bool_arrays()
        assert_(t.all())
        assert_(t.any())
        assert_(not f.all())
        assert_(not f.any())
        assert_(nm.any())
        assert_(im.any())
        assert_(not nm.all())
        assert_(not im.all())
        # check bad element in all positions
        for i in range(256 - 7):
            d = np.array([False] * 256, dtype=bool)[7::]
Repository: numpy/numpy
File: numpy/fft/_pocketfft.pyi (Python)

Code before:
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def rfftn(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def irfftn(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[float64] | None = None,
) -> NDArray[float64]: ...

def fft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def ifft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def rfft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def irfft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,
    norm: _NormKind = None,

Review comment (suggestion):
maybe we could comment the default value, like we've done earlier:

```suggestion
    axes: Sequence[int] | None = ...,  # = (-2, 1)
```

Code after:
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def rfftn(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def irfftn(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = None,
    norm: _NormKind = None,
    out: NDArray[float64] | None = None,
) -> NDArray[float64]: ...

def fft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,  # = (-2, -1)
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def ifft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,  # = (-2, -1)
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def rfft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,  # = (-2, -1)
    norm: _NormKind = None,
    out: NDArray[complex128] | None = None,
) -> NDArray[complex128]: ...

def irfft2(
    a: ArrayLike,
    s: Sequence[int] | None = None,
    axes: Sequence[int] | None = ...,  # = (-2, -1)
    norm: _NormKind = None,
Repository: numpy/numpy
File: numpy/linalg/_linalg.pyi (Python)

Code before:
    keepdims: bool = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
    keepdims: bool = False,
) -> floating: ...
@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] = None,
    ord: float | None = 2,
    keepdims: bool = False,
) -> Any: ...

# TODO: Returns a scalar or array
def multi_dot(
    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
    *,
    out: NDArray[Any] | None = None,
) -> Any: ...

def diagonal(
    x: ArrayLike,  # >= 2D array
    /,
    *,
    offset: SupportsIndex = 0,
) -> NDArray[Any]: ...

def trace(
    x: ArrayLike,  # >= 2D array
    /,
    *,
    offset: SupportsIndex = 0,
    dtype: DTypeLike | None = None,
) -> Any: ...

Review comment (suggestion):
```suggestion
    axis: SupportsInt | SupportsIndex | tuple[int, ...],
```

Code after:
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: L[False] = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
    keepdims: L[False] = False,
) -> floating: ...
@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...],
    ord: float | None = 2,
    keepdims: bool = False,
) -> Any: ...

# TODO: Returns a scalar or array
def multi_dot(
    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
    *,
    out: NDArray[Any] | None = None,
) -> Any: ...

def diagonal(
    x: ArrayLike,  # >= 2D array
    /,
    *,
    offset: SupportsIndex = 0,
) -> NDArray[Any]: ...
Repository: numpy/numpy
File: numpy/linalg/_linalg.pyi (Python)

Code before:

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
    keepdims: bool = False,
) -> floating: ...
@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] = None,
    ord: float | None = 2,
    keepdims: bool = False,
) -> Any: ...

# TODO: Returns a scalar or array
def multi_dot(
    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
    *,
    out: NDArray[Any] | None = None,
) -> Any: ...

def diagonal(
    x: ArrayLike,  # >= 2D array
    /,
    *,
    offset: SupportsIndex = 0,
) -> NDArray[Any]: ...


Review comment (suggestion):
might as well fix this while we're at it

```suggestion
    keepdims: L[False] = False,
```

Code after:
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] | None,
    keepdims: bool = False,
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: L[False] = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
    keepdims: L[False] = False,
) -> floating: ...
@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...],
    ord: float | None = 2,
    keepdims: bool = False,
) -> Any: ...

# TODO: Returns a scalar or array
def multi_dot(
    arrays: Iterable[_ArrayLikeComplex_co | _ArrayLikeObject_co | _ArrayLikeTD64_co],
    *,
    out: NDArray[Any] | None = None,
) -> Any: ...
Repository: numpy/numpy
File: numpy/linalg/_linalg.pyi (Python)

Code before:
    int32,
    NDArray[floating],
]: ...

@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: None = None,
    keepdims: bool = False,
) -> floating: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] = None,
    keepdims: bool = False,
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
    keepdims: bool = False,
) -> floating: ...
@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] = None,

Review comment (suggestion):
```suggestion
    keepdims: L[False] = False,
```

Code after:
    int32,
    NDArray[floating],
]: ...

@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: None = None,
    keepdims: L[False] = False,
) -> floating: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] | None,
    keepdims: bool = False,
) -> Any: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] | None,
    keepdims: bool = False,
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: L[False] = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

@overload
def vector_norm(
    x: ArrayLike,
    /,
    *,
    axis: None = None,
    ord: float | None = 2,
Repository: numpy/numpy
File: numpy/linalg/_linalg.pyi (Python)

Code before:
    NDArray[float64],
    NDArray[float64],
    int32,
    NDArray[float64],
]: ...
@overload
def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: float | None = None) -> tuple[
    NDArray[floating],
    NDArray[floating],
    int32,
    NDArray[floating],
]: ...
@overload
def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: float | None = None) -> tuple[
    NDArray[complexfloating],
    NDArray[floating],
    int32,
    NDArray[floating],
]: ...

@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: None = None,
    keepdims: bool = False,
) -> floating: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] = None,
    keepdims: bool = False,
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> floating: ...
@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: bool = False,
) -> Any: ...

Review comment (suggestion):
```suggestion
    keepdims: L[False] = False,
```

Code after:
    NDArray[float64],
    NDArray[float64],
    int32,
    NDArray[float64],
]: ...
@overload
def lstsq(a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, rcond: float | None = None) -> tuple[
    NDArray[floating],
    NDArray[floating],
    int32,
    NDArray[floating],
]: ...
@overload
def lstsq(a: _ArrayLikeComplex_co, b: _ArrayLikeComplex_co, rcond: float | None = None) -> tuple[
    NDArray[complexfloating],
    NDArray[floating],
    int32,
    NDArray[floating],
]: ...

@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    axis: None = None,
    keepdims: L[False] = False,
) -> floating: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] | None,
    keepdims: bool = False,
) -> Any: ...
@overload
def norm(
    x: ArrayLike,
    ord: float | L["fro", "nuc"] | None = None,
    *,
    axis: SupportsInt | SupportsIndex | tuple[int, ...] | None,
    keepdims: bool = False,
) -> Any: ...

@overload
def matrix_norm(
    x: ArrayLike,
    /,
    *,
    ord: float | L["fro", "nuc"] | None = "fro",
    keepdims: L[False] = False,
) -> floating: ...
Repository: numpy/numpy
File: numpy/polynomial/_polybase.pyi (Python)

Code before:
    ) -> _Other: ...
    @overload
    def convert(
        self,
        /,
        domain: _SeriesLikeCoef_co | None = None,
        *,
        kind: type[_Other],
        window: _SeriesLikeCoef_co | None = None,
    ) -> _Other: ...
    @overload
    def convert(
        self,
        /,
        domain: _SeriesLikeCoef_co | None = None,
        kind: None = None,
        window: _SeriesLikeCoef_co | None = None,
    ) -> Self: ...

    def mapparms(self, /) -> _Tuple2[Any]: ...

    def integ(
        self,
        /,
        m: SupportsIndex = 1,
        k: _CoefLike_co | _SeriesLikeCoef_co = ...,
        lbnd: _CoefLike_co | None = None,
    ) -> Self: ...

    def deriv(self, /, m: SupportsIndex = 1) -> Self: ...

    def roots(self, /) -> _CoefSeries: ...

    def linspace(
        self,
        /,
        n: SupportsIndex = 100,
        domain: _SeriesLikeCoef_co | None = None,
    ) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...

    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co = None,
        full: Literal[False] = False,
        w: _SeriesLikeCoef_co | None = None,

Review comment (suggestion):
```suggestion
        k: _CoefLike_co | _SeriesLikeCoef_co = ...,  # = []
```

Code after:
    ) -> _Other: ...
    @overload
    def convert(
        self,
        /,
        domain: _SeriesLikeCoef_co | None = None,
        *,
        kind: type[_Other],
        window: _SeriesLikeCoef_co | None = None,
    ) -> _Other: ...
    @overload
    def convert(
        self,
        /,
        domain: _SeriesLikeCoef_co | None = None,
        kind: None = None,
        window: _SeriesLikeCoef_co | None = None,
    ) -> Self: ...

    def mapparms(self, /) -> _Tuple2[Any]: ...

    def integ(
        self,
        /,
        m: SupportsIndex = 1,
        k: _CoefLike_co | _SeriesLikeCoef_co = ...,  # = []
        lbnd: _CoefLike_co | None = None,
    ) -> Self: ...

    def deriv(self, /, m: SupportsIndex = 1) -> Self: ...

    def roots(self, /) -> _CoefSeries: ...

    def linspace(
        self,
        /,
        n: SupportsIndex = 100,
        domain: _SeriesLikeCoef_co | None = None,
    ) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...

    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co | None = None,
        full: Literal[False] = False,
        w: _SeriesLikeCoef_co | None = None,
Repository: numpy/numpy
File: numpy/polynomial/_polybase.pyi (Python)

Code before:
        /,
        m: SupportsIndex = 1,
        k: _CoefLike_co | _SeriesLikeCoef_co = ...,
        lbnd: _CoefLike_co | None = None,
    ) -> Self: ...

    def deriv(self, /, m: SupportsIndex = 1) -> Self: ...

    def roots(self, /) -> _CoefSeries: ...

    def linspace(
        self,
        /,
        n: SupportsIndex = 100,
        domain: _SeriesLikeCoef_co | None = None,
    ) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...

    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co = None,
        full: Literal[False] = False,
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co = None,
        *,
        full: Literal[True],
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,

Review comment (suggestion):
```suggestion
        rcond: _FloatLike_co | None = None,
```

Code after:
        /,
        m: SupportsIndex = 1,
        k: _CoefLike_co | _SeriesLikeCoef_co = ...,  # = []
        lbnd: _CoefLike_co | None = None,
    ) -> Self: ...

    def deriv(self, /, m: SupportsIndex = 1) -> Self: ...

    def roots(self, /) -> _CoefSeries: ...

    def linspace(
        self,
        /,
        n: SupportsIndex = 100,
        domain: _SeriesLikeCoef_co | None = None,
    ) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...

    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co | None = None,
        full: Literal[False] = False,
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None = None,
        rcond: _FloatLike_co | None = None,
        *,
        full: Literal[True],
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
Repository: numpy/numpy
File: numpy/polynomial/_polybase.pyi (Python)

Code before:
        *,
        full: Literal[True],
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None,
        rcond: _FloatLike_co,
        full: Literal[True], /,
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...

    @classmethod
    def fromroots(
        cls,
        roots: _ArrayLikeCoef_co,
        domain: _SeriesLikeCoef_co | None = ...,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def identity(
        cls,
        domain: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def basis(
        cls,
        deg: _AnyInt,
        domain: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def cast(
        cls,
        series: ABCPolyBase,

Review comment (suggestion):
```suggestion
        domain: _SeriesLikeCoef_co | None = ...,  # = []
```

Code after:
        *,
        full: Literal[True],
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
    @overload
    @classmethod
    def fit(
        cls,
        x: _SeriesLikeCoef_co,
        y: _SeriesLikeCoef_co,
        deg: int | _SeriesLikeInt_co,
        domain: _SeriesLikeCoef_co | None,
        rcond: _FloatLike_co,
        full: Literal[True], /,
        w: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...

    @classmethod
    def fromroots(
        cls,
        roots: _ArrayLikeCoef_co,
        domain: _SeriesLikeCoef_co | None = ...,  # = []
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def identity(
        cls,
        domain: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def basis(
        cls,
        deg: _AnyInt,
        domain: _SeriesLikeCoef_co | None = None,
        window: _SeriesLikeCoef_co | None = None,
        symbol: str = "x",
    ) -> Self: ...

    @classmethod
    def cast(
        cls,
        series: ABCPolyBase,