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from __future__ import annotations
from ._array_object import ndarray
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._types import Optional, Tuple, Union, array
import numpy as np
def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return ndarray._new(np.max(x._array, axis=axis, keepdims=keepdims))
def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return ndarray._new(np.asarray(np.mean(x._array, axis=axis, keepdims=keepdims)))
def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return ndarray._new(np.min(x._array, axis=axis, keepdims=keepdims))
def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return ndarray._new(np.asarray(np.prod(x._array, axis=axis, keepdims=keepdims)))
def std(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array:
# Note: the keyword argument correction is different here
return ndarray._new(np.asarray(np.std(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return ndarray._new(np.asarray(np.sum(x._array, axis=axis, keepdims=keepdims)))
def var(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False) -> array:
# Note: the keyword argument correction is different here
return ndarray._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
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