old_contents: from __future__ import annotations
  
  from ._array_object import Array
  
  from typing import NamedTuple
  
  import numpy as np
  
  # Note: np.unique() is split into four functions in the array API:
  # unique_all, unique_counts, unique_inverse, and unique_values (this is done
  # to remove polymorphic return types).
  
  # Note: The various unique() functions are supposed to return multiple NaNs.
  # This does not match the NumPy behavior, however, this is currently left as a
  # TODO in this implementation as this behavior may be reverted in np.unique().
  # See https://github.com/numpy/numpy/issues/20326.
  
  # Note: The functions here return a namedtuple (np.unique() returns a normal
  # tuple).
  
  class UniqueAllResult(NamedTuple):
      values: Array
      indices: Array
      inverse_indices: Array
      counts: Array
  
  
  class UniqueCountsResult(NamedTuple):
      values: Array
      counts: Array
  
  
  class UniqueInverseResult(NamedTuple):
      values: Array
      inverse_indices: Array
  
  
  def unique_all(x: Array, /) -> UniqueAllResult:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      res = np.unique(
          x._array,
          return_counts=True,
          return_index=True,
          return_inverse=True,
      )
  
      return UniqueAllResult(*[Array._new(i) for i in res])
  
  
  def unique_counts(x: Array, /) -> UniqueCountsResult:
      res = np.unique(
          x._array,
          return_counts=True,
          return_index=False,
          return_inverse=False,
      )
  
      return UniqueCountsResult(*[Array._new(i) for i in res])
  
  
  def unique_inverse(x: Array, /) -> UniqueInverseResult:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      res = np.unique(
          x._array,
          return_counts=False,
          return_index=False,
          return_inverse=True,
      )
      return UniqueInverseResult(*[Array._new(i) for i in res])
  
  
  def unique_values(x: Array, /) -> Array:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      res = np.unique(
          x._array,
          return_counts=False,
          return_index=False,
          return_inverse=False,
      )
      return Array._new(res)
  
new_contents: from __future__ import annotations
  
  from ._array_object import Array
  
  from typing import NamedTuple
  
  import numpy as np
  
  # Note: np.unique() is split into four functions in the array API:
  # unique_all, unique_counts, unique_inverse, and unique_values (this is done
  # to remove polymorphic return types).
  
  # Note: The various unique() functions are supposed to return multiple NaNs.
  # This does not match the NumPy behavior, however, this is currently left as a
  # TODO in this implementation as this behavior may be reverted in np.unique().
  # See https://github.com/numpy/numpy/issues/20326.
  
  # Note: The functions here return a namedtuple (np.unique() returns a normal
  # tuple).
  
  class UniqueAllResult(NamedTuple):
      values: Array
      indices: Array
      inverse_indices: Array
      counts: Array
  
  
  class UniqueCountsResult(NamedTuple):
      values: Array
      counts: Array
  
  
  class UniqueInverseResult(NamedTuple):
      values: Array
      inverse_indices: Array
  
  
  def unique_all(x: Array, /) -> UniqueAllResult:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      values, indices, inverse_indices, counts = np.unique(
          x._array,
          return_counts=True,
          return_index=True,
          return_inverse=True,
      )
      # np.unique() flattens inverse indices, but they need to share x's shape
      # See https://github.com/numpy/numpy/issues/20638
      inverse_indices = inverse_indices.reshape(x.shape)
      return UniqueAllResult(
          Array._new(values),
          Array._new(indices),
          Array._new(inverse_indices),
          Array._new(counts),
      )
  
  
  def unique_counts(x: Array, /) -> UniqueCountsResult:
      res = np.unique(
          x._array,
          return_counts=True,
          return_index=False,
          return_inverse=False,
      )
  
      return UniqueCountsResult(*[Array._new(i) for i in res])
  
  
  def unique_inverse(x: Array, /) -> UniqueInverseResult:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      values, inverse_indices = np.unique(
          x._array,
          return_counts=False,
          return_index=False,
          return_inverse=True,
      )
      # np.unique() flattens inverse indices, but they need to share x's shape
      # See https://github.com/numpy/numpy/issues/20638
      inverse_indices = inverse_indices.reshape(x.shape)
      return UniqueInverseResult(Array._new(values), Array._new(inverse_indices))
  
  
  def unique_values(x: Array, /) -> Array:
      """
      Array API compatible wrapper for :py:func:`np.unique <numpy.unique>`.
  
      See its docstring for more information.
      """
      res = np.unique(
          x._array,
          return_counts=False,
          return_index=False,
          return_inverse=False,
      )
      return Array._new(res)
  
subject: Return correctly shaped inverse indices in `array_api`