old_contents: 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.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 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.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 Array._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 Array._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 Array._new(np.asarray(np.var(x._array, axis=axis, ddof=correction, keepdims=keepdims)))
  
new_contents: 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))
  
subject: Remove asarray() calls from the array API statistical functions