docs: move notes below the fold and highlight RFC 2119 keywords

src/array_api_stubs/_draft/set_functions.py

@@ -11,40 +11,38 @@ def unique_all(x: array, /) -> Tuple[array, array, array, array]:
     .. admonition:: Data-dependent output shape
         :class: important
 
-        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
-
-    .. note::
-       Uniqueness should be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
-
-       -   As ``nan`` values compare as ``False``, ``nan`` values should be considered distinct.
-       -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components should be considered distinct.
-       -   As ``-0`` and ``+0`` compare as ``True``, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return ``-0`` if ``-0`` occurs before ``+0``).
-
-       As signed zeros are not distinct, using ``inverse_indices`` to reconstruct the input array is not guaranteed to return an array having the exact same values.
-
-       Each ``nan`` value and each complex floating-point value having a ``nan`` component should have a count of one, while the counts for signed zeros should be aggregated as a single count.
+        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, et cetera) can find this function difficult to implement without knowing array values. Accordingly, such libraries **may** choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
 
     Parameters
     ----------
     x: array
-        input array. If ``x`` has more than one dimension, the function must flatten ``x`` and return the unique elements of the flattened array.
+        input array. If ``x`` has more than one dimension, the function **must** flatten ``x`` and return the unique elements of the flattened array.
 
     Returns
     -------
     out: Tuple[array, array, array, array]
         a namedtuple ``(values, indices, inverse_indices, counts)`` whose
 
-        - first element must have the field name ``values`` and must be a one-dimensional array containing the unique elements of ``x``. The array must have the same data type as ``x``.
-        - second element must have the field name ``indices`` and must be an array containing the indices (first occurrences) of a flattened ``x`` that result in ``values``. The array must have the same shape as ``values`` and must have the default array index data type.
-        - third element must have the field name ``inverse_indices`` and must be an array containing the indices of ``values`` that reconstruct ``x``. The array must have the same shape as ``x`` and must have the default array index data type.
-        - fourth element must have the field name ``counts`` and must be an array containing the number of times each unique element occurs in ``x``. The order of the returned counts must match the order of ``values``, such that a specific element in ``counts`` corresponds to the respective unique element in ``values``. The returned array must have same shape as ``values`` and must have the default array index data type.
-
-        .. note::
-           The order of unique elements is not specified and may vary between implementations.
+        -   first element **must** have the field name ``values`` and **must** be a one-dimensional array containing the unique elements of ``x``. The array **must** have the same data type as ``x``.
+        -   second element **must** have the field name ``indices`` and **must** be an array containing the indices (first occurrences) of a flattened ``x`` that result in ``values``. The array **must** have the same shape as ``values`` and **must** have the default array index data type.
+        -   third element **must** have the field name ``inverse_indices`` and **must** be an array containing the indices of ``values`` that reconstruct ``x``. The array **must** have the same shape as ``x`` and **must** have the default array index data type.
+        -   fourth element **must** have the field name ``counts`` and **must** be an array containing the number of times each unique element occurs in ``x``. The order of the returned counts **must** match the order of ``values``, such that a specific element in ``counts`` corresponds to the respective unique element in ``values``. The returned array **must** have same shape as ``values`` and **must** have the default array index data type.
 
     Notes
     -----
 
+    -   The order of unique elements returned by this function is unspecified and thus implementation-defined. As a consequence, element order **may** vary between implementations.
+
+    -   Uniqueness **should** be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
+
+        -   As ``nan`` values compare as ``False``, ``nan`` values **should** be considered distinct.
+        -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components **should** be considered distinct.
+        -   As ``-0`` and ``+0`` compare as ``True``, signed zeros **should not** be considered distinct, and the corresponding unique element **may** be implementation-defined (e.g., an implementation **may** choose to return ``-0`` if ``-0`` occurs before ``+0``).
+
+        As signed zeros are not distinct, using ``inverse_indices`` to reconstruct the input array is not guaranteed to return an array having the exact same values.
+
+        Each ``nan`` value and each complex floating-point value having a ``nan`` component **should** have a count of one, while the counts for signed zeros **should** be aggregated as a single count.
+
     .. versionchanged:: 2022.12
        Added complex data type support.
 
@@ -60,36 +58,34 @@ def unique_counts(x: array, /) -> Tuple[array, array]:
     .. admonition:: Data-dependent output shape
         :class: important
 
-        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
-
-    .. note::
-       Uniqueness should be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
-
-       -   As ``nan`` values compare as ``False``, ``nan`` values should be considered distinct.
-       -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components should be considered distinct.
-       -   As ``-0`` and ``+0`` compare as ``True``, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return ``-0`` if ``-0`` occurs before ``+0``).
-
-       Each ``nan`` value and each complex floating-point value having a ``nan`` component should have a count of one, while the counts for signed zeros should be aggregated as a single count.
+        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) can find this function difficult to implement without knowing array values. Accordingly, such libraries **may** choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
 
     Parameters
     ----------
     x: array
-        input array. If ``x`` has more than one dimension, the function must flatten ``x`` and return the unique elements of the flattened array.
+        input array. If ``x`` has more than one dimension, the function **must** flatten ``x`` and return the unique elements of the flattened array.
 
     Returns
     -------
     out: Tuple[array, array]
         a namedtuple `(values, counts)` whose
 
-        -   first element must have the field name ``values`` and must be a one-dimensional array containing the unique elements of ``x``. The array must have the same data type as ``x``.
-        -   second element must have the field name `counts` and must be an array containing the number of times each unique element occurs in ``x``. The order of the returned counts must match the order of ``values``, such that a specific element in ``counts`` corresponds to the respective unique element in ``values``. The returned array must have same shape as ``values`` and must have the default array index data type.
-
-        .. note::
-           The order of unique elements is not specified and may vary between implementations.
+        -   first element **must** have the field name ``values`` and **must** be a one-dimensional array containing the unique elements of ``x``. The array **must** have the same data type as ``x``.
+        -   second element **must** have the field name `counts` and **must** be an array containing the number of times each unique element occurs in ``x``. The order of the returned counts **must** match the order of ``values``, such that a specific element in ``counts`` corresponds to the respective unique element in ``values``. The returned array **must** have same shape as ``values`` and **must** have the default array index data type.
 
     Notes
     -----
 
+    -   The order of unique elements returned by this function is unspecified and thus implementation-defined. As a consequence, element order **may** vary between implementations.
+
+    -   Uniqueness **should** be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
+
+        -   As ``nan`` values compare as ``False``, ``nan`` values **should** be considered distinct.
+        -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components **should** be considered distinct.
+        -   As ``-0`` and ``+0`` compare as ``True``, signed zeros **should not** be considered distinct, and the corresponding unique element **may** be implementation-defined (e.g., an implementation **may** choose to return ``-0`` if ``-0`` occurs before ``+0``).
+
+        Each ``nan`` value and each complex floating-point value having a ``nan`` component **should** have a count of one, while the counts for signed zeros **should** be aggregated as a single count.
+
     .. versionchanged:: 2022.12
        Added complex data type support.
 
@@ -105,36 +101,34 @@ def unique_inverse(x: array, /) -> Tuple[array, array]:
     .. admonition:: Data-dependent output shape
         :class: important
 
-        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
-
-    .. note::
-       Uniqueness should be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
-
-       -   As ``nan`` values compare as ``False``, ``nan`` values should be considered distinct.
-       -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components should be considered distinct.
-       -   As ``-0`` and ``+0`` compare as ``True``, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return ``-0`` if ``-0`` occurs before ``+0``).
-
-       As signed zeros are not distinct, using ``inverse_indices`` to reconstruct the input array is not guaranteed to return an array having the exact same values.
+        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) can find this function difficult to implement without knowing array values. Accordingly, such libraries **may** choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
 
     Parameters
     ----------
     x: array
-        input array. If ``x`` has more than one dimension, the function must flatten ``x`` and return the unique elements of the flattened array.
+        input array. If ``x`` has more than one dimension, the function **must** flatten ``x`` and return the unique elements of the flattened array.
 
     Returns
     -------
     out: Tuple[array, array]
         a namedtuple ``(values, inverse_indices)`` whose
 
-        -   first element must have the field name ``values`` and must be a one-dimensional array containing the unique elements of ``x``. The array must have the same data type as ``x``.
-        -   second element must have the field name ``inverse_indices`` and must be an array containing the indices of ``values`` that reconstruct ``x``. The array must have the same shape as ``x`` and have the default array index data type.
-
-        .. note::
-           The order of unique elements is not specified and may vary between implementations.
+        -   first element **must** have the field name ``values`` and **must** be a one-dimensional array containing the unique elements of ``x``. The array **must** have the same data type as ``x``.
+        -   second element **must** have the field name ``inverse_indices`` and **must** be an array containing the indices of ``values`` that reconstruct ``x``. The array **must** have the same shape as ``x`` and have the default array index data type.
 
     Notes
     -----
 
+    -   The order of unique elements returned by this function is unspecified and thus implementation-defined. As a consequence, element order **may** vary between implementations.
+
+    -   Uniqueness **should** be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
+
+        -   As ``nan`` values compare as ``False``, ``nan`` values **should** be considered distinct.
+        -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components **should** be considered distinct.
+        -   As ``-0`` and ``+0`` compare as ``True``, signed zeros **should not** be considered distinct, and the corresponding unique element **may** be implementation-defined (e.g., an implementation **may** choose to return ``-0`` if ``-0`` occurs before ``+0``).
+
+        As signed zeros are not distinct, using ``inverse_indices`` to reconstruct the input array is not guaranteed to return an array having the exact same values.
+
     .. versionchanged:: 2022.12
        Added complex data type support.
 
@@ -150,31 +144,29 @@ def unique_values(x: array, /) -> array:
     .. admonition:: Data-dependent output shape
         :class: important
 
-        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) may find this function difficult to implement without knowing array values. Accordingly, such libraries may choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
-
-    .. note::
-       Uniqueness should be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
-
-       -   As ``nan`` values compare as ``False``, ``nan`` values should be considered distinct.
-       -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components should be considered distinct.
-       -   As ``-0`` and ``+0`` compare as ``True``, signed zeros should not be considered distinct, and the corresponding unique element will be implementation-dependent (e.g., an implementation could choose to return ``-0`` if ``-0`` occurs before ``+0``).
+        The shapes of two of the output arrays for this function depend on the data values in the input array; hence, array libraries which build computation graphs (e.g., JAX, Dask, etc.) can find this function difficult to implement without knowing array values. Accordingly, such libraries **may** choose to omit this function. See :ref:`data-dependent-output-shapes` section for more details.
 
     Parameters
     ----------
     x: array
-        input array. If ``x`` has more than one dimension, the function must flatten ``x`` and return the unique elements of the flattened array.
+        input array. If ``x`` has more than one dimension, the function **must** flatten ``x`` and return the unique elements of the flattened array.
 
     Returns
     -------
     out: array
-        a one-dimensional array containing the set of unique elements in ``x``. The returned array must have the same data type as ``x``.
-
-        .. note::
-           The order of unique elements is not specified and may vary between implementations.
+        a one-dimensional array containing the set of unique elements in ``x``. The returned array **must** have the same data type as ``x``.
 
     Notes
     -----
 
+    -   The order of unique elements returned by this function is unspecified and thus implementation-defined. As a consequence, element order **may** vary between implementations.
+
+    -   Uniqueness **should** be determined based on value equality (see :func:`~array_api.equal`). For input arrays having floating-point data types, value-based equality implies the following behavior.
+
+        -   As ``nan`` values compare as ``False``, ``nan`` values **should** be considered distinct.
+        -   As complex floating-point values having at least one ``nan`` component compare as ``False``, complex floating-point values having ``nan`` components **should** be considered distinct.
+        -   As ``-0`` and ``+0`` compare as ``True``, signed zeros **should not** be considered distinct, and the corresponding unique element **may** be implementation-defined (e.g., an implementation **may** choose to return ``-0`` if ``-0`` occurs before ``+0``).
+
     .. versionchanged:: 2022.12
        Added complex data type support.