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from __future__ import annotations
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 np.max._implementation(x, axis=axis, keepdims=keepdims)
def mean(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return np.asarray(np.mean._implementation(x, axis=axis, keepdims=keepdims))
def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return np.min._implementation(x, axis=axis, keepdims=keepdims)
def prod(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return np.asarray(np.prod._implementation(x, 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 np.asarray(np.std._implementation(x, axis=axis, ddof=correction, keepdims=keepdims))
def sum(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array:
return np.asarray(np.sum._implementation(x, 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 np.asarray(np.var._implementation(x, axis=axis, ddof=correction, keepdims=keepdims))
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