text: from __future__ import annotations
  
  from ._array_object import Array
  from ._dtypes import _result_type
  
  from typing import Optional, Tuple
  
  import numpy as np
  
  
  def argmax(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
      """
      Array API compatible wrapper for :py:func:`np.argmax <numpy.argmax>`.
  
      See its docstring for more information.
      """
      return Array._new(np.asarray(np.argmax(x._array, axis=axis, keepdims=keepdims)))
  
  
  def argmin(x: Array, /, *, axis: Optional[int] = None, keepdims: bool = False) -> Array:
      """
      Array API compatible wrapper for :py:func:`np.argmin <numpy.argmin>`.
  
      See its docstring for more information.
      """
      return Array._new(np.asarray(np.argmin(x._array, axis=axis, keepdims=keepdims)))
  
  
  def nonzero(x: Array, /) -> Tuple[Array, ...]:
      """
      Array API compatible wrapper for :py:func:`np.nonzero <numpy.nonzero>`.
  
      See its docstring for more information.
      """
      return tuple(Array._new(i) for i in np.nonzero(x._array))
  
  
  def where(condition: Array, x1: Array, x2: Array, /) -> Array:
      """
      Array API compatible wrapper for :py:func:`np.where <numpy.where>`.
  
      See its docstring for more information.
      """
      # Call result type here just to raise on disallowed type combinations
      _result_type(x1.dtype, x2.dtype)
      x1, x2 = Array._normalize_two_args(x1, x2)
      return Array._new(np.where(condition._array, x1._array, x2._array))
  