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- from __future__ import annotations
- from typing import Any
- import numpy as np
- class Object:
- def __ceil__(self) -> Object:
- return self
- def __floor__(self) -> Object:
- return self
- def __ge__(self, value: object) -> bool:
- return True
- def __array__(self, dtype: np.typing.DTypeLike | None = None,
- copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
- ret = np.empty((), dtype=object)
- ret[()] = self
- return ret
- AR_LIKE_b = [True, True, False]
- AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
- AR_LIKE_i = [1, 2, 3]
- AR_LIKE_f = [1.0, 2.0, 3.0]
- AR_LIKE_O = [Object(), Object(), Object()]
- AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.zeros(3, dtype="U5")
- np.fix(AR_LIKE_b)
- np.fix(AR_LIKE_u)
- np.fix(AR_LIKE_i)
- np.fix(AR_LIKE_f)
- np.fix(AR_LIKE_O)
- np.fix(AR_LIKE_f, out=AR_U)
- np.isposinf(AR_LIKE_b)
- np.isposinf(AR_LIKE_u)
- np.isposinf(AR_LIKE_i)
- np.isposinf(AR_LIKE_f)
- np.isposinf(AR_LIKE_f, out=AR_U)
- np.isneginf(AR_LIKE_b)
- np.isneginf(AR_LIKE_u)
- np.isneginf(AR_LIKE_i)
- np.isneginf(AR_LIKE_f)
- np.isneginf(AR_LIKE_f, out=AR_U)
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