Improve error messages for unexpected keyword arguments in overloaded functions

[KevinRK29]

This PR improves error messages when calling overloaded functions with unexpected keyword arguments, making it easier to identify and fix typos.

Changes

• Add suggestions for misspelled keyword argument names (using existing best_matches fuzzy matching)
• Include the line number where a type-compatible overload variant is defined
• Fall back to showing all possible overload variants when no type-matching suggestion can be made

[github-actions]

Diff from mypy_primer, showing the effect of this PR on open source code:

spark (https://github.com/apache/spark)
+ python/pyspark/pandas/namespace.py:1140: error: Unexpected keyword argument "date_parser" for overloaded function "read_excel" defined on line 51  [call-overload]
- python/pyspark/pandas/namespace.py:1140: error: No overload variant of "read_excel" matches argument types "BytesIO | Any", "str | int | list[str | int] | None", "int | list[int]", "list[Any] | None", "list[int] | None", "int | str | list[int | str] | Callable[[str], bool] | None", "dict[str, str | dtype[Any] | ExtensionDtype] | None", "str | None", "dict[Any, Any] | None", "Any | None", "Any | None", "int | list[int] | None", "int | None", "Any | None", "bool", "bool", "bool | list[Any] | dict[Any, Any]", "Callable[..., Any] | None", "str | None", "str | None", "int", "dict[str, Any]"  [call-overload]
- python/pyspark/pandas/namespace.py:1140: note: Possible overload variants:
- python/pyspark/pandas/namespace.py:1140: note:     def [IntStrT: (int, str)] read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[IntStrT], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[tuple[Any, ...], dtype[Any]] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[Any], Any]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ..., engine_kwargs: dict[str, Any] | None = ...) -> dict[IntStrT, DataFrame]
- python/pyspark/pandas/namespace.py:1140: note:     def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: None, *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[tuple[Any, ...], dtype[Any]] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[Any], Any]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ..., engine_kwargs: dict[str, Any] | None = ...) -> dict[str, DataFrame]
- python/pyspark/pandas/namespace.py:1140: note:     def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[int | str], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[tuple[Any, ...], dtype[Any]] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[Any], Any]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ..., engine_kwargs: dict[str, Any] | None = ...) -> dict[int | str, DataFrame]
- python/pyspark/pandas/namespace.py:1140: note:     def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: int | str = ..., *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[tuple[Any, ...], dtype[Any]] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[complex] | type[bool] | type[object] | type[str]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[Any], Any]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] | None = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ..., engine_kwargs: dict[str, Any] | None = ...) -> DataFrame

prefect (https://github.com/PrefectHQ/prefect)
+ src/prefect/futures.py:222: error: Unexpected keyword argument "_sync" for overloaded function "result" of "State" defined on line 293  [call-overload]
- src/prefect/futures.py:222: error: No overload variant of "result" of "State" matches argument types "bool", "bool"  [call-overload]
- src/prefect/futures.py:222: note: Possible overload variants:
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: Literal[True] = ..., retry_result_failure: bool = ...) -> Any
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: Literal[False] = ..., retry_result_failure: bool = ...) -> Any | Exception
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: bool = ..., retry_result_failure: bool = ...) -> Any | Exception
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: Literal[True] = ..., retry_result_failure: bool = ...) -> R
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: Literal[False] = ..., retry_result_failure: bool = ...) -> R | Exception
- src/prefect/futures.py:222: note:     def result(self, raise_on_failure: bool = ..., retry_result_failure: bool = ...) -> R | Exception
+ src/prefect/utilities/engine.py:764: error: Unexpected keyword argument "_sync" for overloaded function "result" of "State" defined on line 293  [call-overload]
- src/prefect/utilities/engine.py:764: error: No overload variant of "result" of "State" matches argument types "bool", "bool"  [call-overload]
- src/prefect/utilities/engine.py:764: note: Possible overload variants:
- src/prefect/utilities/engine.py:764: note:     def result(self, raise_on_failure: Literal[True] = ..., retry_result_failure: bool = ...) -> Any
- src/prefect/utilities/engine.py:764: note:     def result(self, raise_on_failure: Literal[False] = ..., retry_result_failure: bool = ...) -> Any | Exception
- src/prefect/utilities/engine.py:764: note:     def result(self, raise_on_failure: bool = ..., retry_result_failure: bool = ...) -> Any | Exception
- src/prefect/task_engine.py:529: error: No overload variant of "result" of "State" matches argument types "bool", "bool"  [call-overload]
+ src/prefect/task_engine.py:529: error: Unexpected keyword argument "_sync" for overloaded function "result" of "State" defined on line 293  [call-overload]
- src/prefect/task_engine.py:529: note: Possible overload variants:
- src/prefect/task_engine.py:529: note:     def result(self, raise_on_failure: Literal[True] = ..., retry_result_failure: bool = ...) -> R
- src/prefect/task_engine.py:529: note:     def result(self, raise_on_failure: Literal[False] = ..., retry_result_failure: bool = ...) -> R | Exception
- src/prefect/task_engine.py:529: note:     def result(self, raise_on_failure: bool = ..., retry_result_failure: bool = ...) -> R | Exception

scipy (https://github.com/scipy/scipy)
- scipy/sparse/linalg/tests/test_interface.py:306: error: No overload variant of "__call__" of "_GUFunc_Nin2_Nout1" matches argument types "Any", "Any", "int"  [call-overload]
+ scipy/sparse/linalg/tests/test_interface.py:306: error: Unexpected keyword argument "axis" for overloaded function "__call__" of "_GUFunc_Nin2_Nout1"  [call-overload]
- scipy/sparse/linalg/tests/test_interface.py:307: error: No overload variant of "__call__" of "_GUFunc_Nin2_Nout1" matches argument types "Any", "Any", "int"  [call-overload]
+ scipy/sparse/linalg/tests/test_interface.py:307: error: Unexpected keyword argument "axis" for overloaded function "__call__" of "_GUFunc_Nin2_Nout1"  [call-overload]

xarray (https://github.com/pydata/xarray)
+ xarray/tests/test_plot.py:1167: error: Unexpected keyword argument "start" for overloaded function "arange" defined on line 968  [call-overload]
+ xarray/tests/test_plot.py:1168: error: Unexpected keyword argument "start" for overloaded function "arange" defined on line 968  [call-overload]
- xarray/tests/test_plot.py:1167: error: No overload variant of "arange" matches argument types "int", "int", "int"  [call-overload]
- xarray/tests/test_plot.py:1167: note: Possible overload variants:
- xarray/tests/test_plot.py:1167: note:     def [_ArangeScalarT: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]] arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[_ArangeScalarT] | dtype[_ArangeScalarT] | _HasDType[dtype[_ArangeScalarT]] | _HasNumPyDType[dtype[_ArangeScalarT]], device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[_ArangeScalarT]]
- xarray/tests/test_plot.py:1167: note:     def arange(int | integer[Any] | numpy.bool[builtins.bool], /, stop: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[int] | type[signedinteger[_32Bit | _64Bit]] | dtype[signedinteger[_32Bit | _64Bit]] | _HasDType[dtype[signedinteger[_32Bit | _64Bit]]] | _HasNumPyDType[dtype[signedinteger[_32Bit | _64Bit]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]
- xarray/tests/test_plot.py:1167: note:     def arange(float | floating[Any], /, stop: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _HasDType[dtype[float64]] | _HasNumPyDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
- xarray/tests/test_plot.py:1167: note:     def arange(float | floating[Any] | integer[Any] | numpy.bool[builtins.bool], /, stop: float | floating[Any], step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _HasDType[dtype[float64]] | _HasNumPyDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
- xarray/tests/test_plot.py:1167: note:     def arange(timedelta64[timedelta | int | None], /, stop: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _HasDType[dtype[timedelta64[timedelta | int | None]]] | _HasNumPyDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
- xarray/tests/test_plot.py:1167: note:     def arange(int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool], /, stop: timedelta64[timedelta | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _HasDType[dtype[timedelta64[timedelta | int | None]]] | _HasNumPyDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
- xarray/tests/test_plot.py:1167: note:     def arange(datetime64[date | int | None], /, stop: datetime64[date | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[datetime64[date | int | None]] | dtype[datetime64[date | int | None]] | _HasDType[dtype[datetime64[date | int | None]]] | _HasNumPyDType[dtype[datetime64[date | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[datetime64[Any]]]
- xarray/tests/test_plot.py:1167: note:     def arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[Any] | dtype[Any] | _HasDType[dtype[Any]] | _HasNumPyDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict | str | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[Any]]
- xarray/tests/test_plot.py:1168: error: No overload variant of "arange" matches argument types "int", "int", "int"  [call-overload]
- xarray/tests/test_plot.py:1168: note: Possible overload variants:
- xarray/tests/test_plot.py:1168: note:     def [_ArangeScalarT: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]] arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[_ArangeScalarT] | dtype[_ArangeScalarT] | _HasDType[dtype[_ArangeScalarT]] | _HasNumPyDType[dtype[_ArangeScalarT]], device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[_ArangeScalarT]]
- xarray/tests/test_plot.py:1168: note:     def arange(int | integer[Any] | numpy.bool[builtins.bool], /, stop: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[int] | type[signedinteger[_32Bit | _64Bit]] | dtype[signedinteger[_32Bit | _64Bit]] | _HasDType[dtype[signedinteger[_32Bit | _64Bit]]] | _HasNumPyDType[dtype[signedinteger[_32Bit | _64Bit]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]
- xarray/tests/test_plot.py:1168: note:     def arange(float | floating[Any], /, stop: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _HasDType[dtype[float64]] | _HasNumPyDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
- xarray/tests/test_plot.py:1168: note:     def arange(float | floating[Any] | integer[Any] | numpy.bool[builtins.bool], /, stop: float | floating[Any], step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _HasDType[dtype[float64]] | _HasNumPyDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
- xarray/tests/test_plot.py:1168: note:     def arange(timedelta64[timedelta | int | None], /, stop: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _HasDType[dtype[timedelta64[timedelta | int | None]]] | _HasNumPyDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
- xarray/tests/test_plot.py:1168: note:     def arange(int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool], /, stop: timedelta64[timedelta | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _HasDType[dtype[timedelta64[timedelta | int | None]]] | _HasNumPyDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
- xarray/tests/test_plot.py:1168: note:     def arange(datetime64[date | int | None], /, stop: datetime64[date | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[datetime64[date | int | None]] | dtype[datetime64[date | int | None]] | _HasDType[dtype[datetime64[date | int | None]]] | _HasNumPyDType[dtype[datetime64[date | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[datetime64[Any]]]
- xarray/tests/test_plot.py:1168: note:     def arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[Any] | dtype[Any] | _HasDType[dtype[Any]] | _HasNumPyDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict | str | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[Any]]