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from __future__ import annotations
from ._array_object import Array
from typing import Optional, Tuple, Union
import numpy as np
def max(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
return Array._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 Array._new(np.mean(x._array, axis=axis, keepdims=keepdims))
def min(
x: Array,
/,
*,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
keepdims: bool = False,
) -> Array:
return Array._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 Array._new(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 Array._new(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 Array._new(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 Array._new(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims))
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