Doc/howto/descriptor.rst

.. _descriptorhowto:

======================
Descriptor HowTo Guide
======================

:Author: Raymond Hettinger
:Contact: <python at rcn dot com>

.. Contents::


:term:`Descriptors <descriptor>` let objects customize attribute lookup,
storage, and deletion.

This HowTo guide has three major sections:

1) The "primer" gives a basic overview, moving gently from simple examples,
   adding one feature at a time.  It is a great place to start.

2) The second section shows a complete, practical descriptor example.  If you
   already know the basics, start there.

3) The third section provides a more technical tutorial that goes into the
   detailed mechanics of how descriptors work.  Most people don't need this
   level of detail.


Primer
^^^^^^

In this primer, we start with most basic possible example and then we'll add
new capabilities one by one.


Simple example: A descriptor that returns a constant
----------------------------------------------------

The :class:`Ten` class is a descriptor that always returns the constant ``10``::


    class Ten:
        def __get__(self, obj, objtype=None):
            return 10

To use the descriptor, it must be stored as a class variable in another class::

    class A:
        x = 5                       # Regular class attribute
        y = Ten()                   # Descriptor

An interactive session shows the difference between normal attribute lookup
and descriptor lookup::

    >>> a = A()                     # Make an instance of class A
    >>> a.x                         # Normal attribute lookup
    5
    >>> a.y                         # Descriptor lookup
    10

In the ``a.x`` attribute lookup, the dot operator finds the value ``5`` stored
in the class dictionary.  In the ``a.y`` descriptor lookup, the dot operator
calls the descriptor's :meth:`__get__()` method.  That method returns ``10``.
Note that the value ``10`` is not stored in either the class dictionary or the
instance dictionary.  Instead, the value ``10`` is computed on demand.

This example shows how a simple descriptor works, but it isn't very useful.
For retrieving constants, normal attribute lookup would be better.

In the next section, we'll create something more useful, a dynamic lookup.


Dynamic lookups
---------------

Interesting descriptors typically run computations instead of doing lookups::


    import os

    class DirectorySize:

        def __get__(self, obj, objtype=None):
            return len(os.listdir(obj.dirname))

    class Directory:

        size = DirectorySize()              # Descriptor

        def __init__(self, dirname):
            self.dirname = dirname          # Regular instance attribute

An interactive session shows that the lookup is dynamic — it computes
different, updated answers each time::

    >>> g = Directory('games')
    >>> s = Directory('songs')
    >>> g.size                              # The games directory has three files
    3
    >>> os.system('touch games/newfile')    # Add a fourth file to the directory
    0
    >>> g.size
    4
    >>> s.size                              # The songs directory has twenty files
    20

Besides showing how descriptors can run computations, this example also
reveals the purpose of the parameters to :meth:`__get__`.  The *self*
parameter is *size*, an instance of *DirectorySize*.  The *obj* parameter is
either *g* or *s*, an instance of *Directory*.  It is *obj* parameter that
lets the :meth:`__get__` method learn the target directory.  The *objtype*
parameter is the class *Directory*.


Managed attributes
------------------

A popular use for descriptors is managing access to instance data.  The
descriptor is assigned to a public attribute in the class dictionary while the
actual data is stored as a private attribute in the instance dictionary.  The
descriptor's :meth:`__get__` and :meth:`__set__` methods are triggered when
the public attribute is accessed.

In the following example, *age* is the public attribute and *_age* is the
private attribute.  When the public attribute is accessed, the descriptor logs
the lookup or update::

    import logging

    logging.basicConfig(level=logging.INFO)

    class LoggedAgeAccess:

        def __get__(self, obj, objtype=None):
            value = obj._age
            logging.info('Accessing %r giving %r', 'age', value)
            return value

        def __set__(self, obj, value):
            logging.info('Updating %r to %r', 'age', value)
            obj._age = value

    class Person:

        age = LoggedAgeAccess()             # Descriptor

        def __init__(self, name, age):
            self.name = name                # Regular instance attribute
            self.age = age                  # Calls the descriptor

        def birthday(self):
            self.age += 1                   # Calls both __get__() and __set__()


An interactive session shows that all access to the managed attribute *age* is
logged, but that the regular attribute *name* is not logged::

    >>> mary = Person('Mary M', 30)         # The initial age update is logged
    INFO:root:Updating 'age' to 30
    >>> dave = Person('David D', 40)
    INFO:root:Updating 'age' to 40

    >>> vars(mary)                          # The actual data is in a private attribute
    {'name': 'Mary M', '_age': 30}
    >>> vars(dave)
    {'name': 'David D', '_age': 40}

    >>> mary.age                            # Access the data and log the lookup
    INFO:root:Accessing 'age' giving 30
    30
    >>> mary.birthday()                     # Updates are logged as well
    INFO:root:Accessing 'age' giving 30
    INFO:root:Updating 'age' to 31

    >>> dave.name                           # Regular attribute lookup isn't logged
    'David D'
    >>> dave.age                            # Only the managed attribute is logged
    INFO:root:Accessing 'age' giving 40
    40

One major issue with this example is the private name *_age* is hardwired in
the *LoggedAgeAccess* class.  That means that each instance can only have one
logged attribute and that its name is unchangeable.  In the next example,
we'll fix that problem.


Customized Names
----------------

When a class uses descriptors, it can inform each descriptor about what
variable name was used.

In this example, the :class:`Person` class has two descriptor instances,
*name* and *age*.  When the :class:`Person` class is defined, it makes a
callback to :meth:`__set_name__` in *LoggedAccess* so that the field names can
be recorded, giving each descriptor its own *public_name* and *private_name*::

    import logging

    logging.basicConfig(level=logging.INFO)

    class LoggedAccess:

        def __set_name__(self, owner, name):
            self.public_name = name
            self.private_name = f'_{name}'

        def __get__(self, obj, objtype=None):
            value = getattr(obj, self.private_name)
            logging.info('Accessing %r giving %r', self.public_name, value)
            return value

        def __set__(self, obj, value):
            logging.info('Updating %r to %r', self.public_name, value)
            setattr(obj, self.private_name, value)

    class Person:

        name = LoggedAccess()                # First descriptor
        age = LoggedAccess()                 # Second descriptor

        def __init__(self, name, age):
            self.name = name                 # Calls the first descriptor
            self.age = age                   # Calls the second descriptor

        def birthday(self):
            self.age += 1

An interactive session shows that the :class:`Person` class has called
:meth:`__set_name__` so that the field names would be recorded.  Here
we call :func:`vars` to lookup the descriptor without triggering it::

    >>> vars(vars(Person)['name'])
    {'public_name': 'name', 'private_name': '_name'}
    >>> vars(vars(Person)['age'])
    {'public_name': 'age', 'private_name': '_age'}

The new class now logs access to both *name* and *age*::

    >>> pete = Person('Peter P', 10)
    INFO:root:Updating 'name' to 'Peter P'
    INFO:root:Updating 'age' to 10
    >>> kate = Person('Catherine C', 20)
    INFO:root:Updating 'name' to 'Catherine C'
    INFO:root:Updating 'age' to 20

The two *Person* instances contain only the private names::

    >>> vars(pete)
    {'_name': 'Peter P', '_age': 10}
    >>> vars(kate)
    {'_name': 'Catherine C', '_age': 20}


Closing thoughts
----------------

A :term:`descriptor` is what we call any object that defines :meth:`__get__`,
:meth:`__set__`, or :meth:`__delete__`.

Descriptors get invoked by the dot operator during attribute lookup.  If a
descriptor is accessed indirectly with ``vars(some_class)[descriptor_name]``,
the descriptor instance is returned without invoking it.

Descriptors only work when used as class variables.  When put in instances,
they have no effect.

The main motivation for descriptors is to provide a hook allowing objects
stored in class variables to control what happens during dotted lookup.

Traditionally, the calling class controls what happens during lookup.
Descriptors invert that relationship and allow the data being looked-up to
have a say in the matter.

Descriptors are used throughout the language.  It is how functions turn into
bound methods.  Common tools like :func:`classmethod`, :func:`staticmethod`,
:func:`property`, and :func:`functools.cached_property` are all implemented as
descriptors.


Complete Practical Example
^^^^^^^^^^^^^^^^^^^^^^^^^^

In this example, we create a practical and powerful tool for locating
notoriously hard to find data corruption bugs.


Validator class
---------------

A validator is a descriptor for managed attribute access.  Prior to storing
any data, it verifies that the new value meets various type and range
restrictions.  If those restrictions aren't met, it raises an exception to
prevents data corruption at its source.

This :class:`Validator` class is both an :term:`abstract base class` and a
managed attribute descriptor::

    from abc import ABC, abstractmethod

    class Validator(ABC):

        def __set_name__(self, owner, name):
            self.private_name = f'_{name}'

        def __get__(self, obj, objtype=None):
            return getattr(obj, self.private_name)

        def __set__(self, obj, value):
            self.validate(value)
            setattr(obj, self.private_name, value)

        @abstractmethod
        def validate(self, value):
            pass

Custom validators need to subclass from :class:`Validator` and supply a
:meth:`validate` method to test various restrictions as needed.


Custom validators
-----------------

Here are three practical data validation utilities:

1) :class:`OneOf` verifies that a value is one of a restricted set of options.

2) :class:`Number` verifies that a value is either an :class:`int` or
   :class:`float`.  Optionally, it verifies that a value is between a given
   minimum or maximum.

3) :class:`String` verifies that a value is a :class:`str`.  Optionally, it
   validates a given minimum or maximum length.  Optionally, it can test for
   another predicate as well.

::

    class OneOf(Validator):

        def __init__(self, *options):
            self.options = set(options)

        def validate(self, value):
            if value not in self.options:
                raise ValueError(f'Expected {value!r} to be one of {self.options!r}')

    class Number(Validator):

        def __init__(self, minvalue=None, maxvalue=None):
            self.minvalue = minvalue
            self.maxvalue = maxvalue

        def validate(self, value):
            if not isinstance(value, (int, float)):
                raise TypeError(f'Expected {value!r} to be an int or float')
            if self.minvalue is not None and value < self.minvalue:
                raise ValueError(
                    f'Expected {value!r} to be at least {self.minvalue!r}'
                )
            if self.maxvalue is not None and value > self.maxvalue:
                raise ValueError(
                    f'Expected {value!r} to be no more than {self.maxvalue!r}'
                )

    class String(Validator):

        def __init__(self, minsize=None, maxsize=None, predicate=None):
            self.minsize = minsize
            self.maxsize = maxsize
            self.predicate = predicate

        def validate(self, value):
            if not isinstance(value, str):
                raise TypeError(f'Expected {value!r} to be an str')
            if self.minsize is not None and len(value) < self.minsize:
                raise ValueError(
                    f'Expected {value!r} to be no smaller than {self.minsize!r}'
                )
            if self.maxsize is not None and len(value) > self.maxsize:
                raise ValueError(
                    f'Expected {value!r} to be no bigger than {self.maxsize!r}'
                )
            if self.predicate is not None and not self.predicate(value):
                raise ValueError(
                    f'Expected {self.predicate} to be true for {value!r}'
                )


Practical use
-------------

Here's how the data validators can be used in a real class::

    class Component:

        name = String(minsize=3, maxsize=10, predicate=str.isupper)
        kind = OneOf('plastic', 'metal')
        quantity = Number(minvalue=0)

        def __init__(self, name, kind, quantity):
            self.name = name
            self.kind = kind
            self.quantity = quantity

The descriptors prevent invalid instances from being created::

    Component('WIDGET', 'metal', 5)     # Allowed.
    Component('Widget', 'metal', 5)     # Blocked: 'Widget' is not all uppercase
    Component('WIDGET', 'metle', 5)     # Blocked: 'metle' is misspelled
    Component('WIDGET', 'metal', -5)    # Blocked: -5 is negative
    Component('WIDGET', 'metal', 'V')   # Blocked: 'V' isn't a number


Technical Tutorial
^^^^^^^^^^^^^^^^^^

What follows is a more technical tutorial for the mechanics and details of how
descriptors work.


Abstract
--------

Defines descriptors, summarizes the protocol, and shows how descriptors are
called.  Examines a custom descriptor and several built-in Python descriptors
including functions, properties, static methods, and class methods.  Shows how
each works by giving a pure Python equivalent and a sample application.

Learning about descriptors not only provides access to a larger toolset, it
creates a deeper understanding of how Python works and an appreciation for the
elegance of its design.


Definition and Introduction
---------------------------

In general, a descriptor is an object attribute with "binding behavior", one
whose attribute access has been overridden by methods in the descriptor
protocol.  Those methods are :meth:`__get__`, :meth:`__set__`, and
:meth:`__delete__`.  If any of those methods are defined for an object, it is
said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the
attribute from an object's dictionary.  For instance, ``a.x`` has a lookup chain
starting with ``a.__dict__['x']``, then ``type(a).__dict__['x']``, and
continuing through the base classes of ``type(a)`` excluding metaclasses. If the
looked-up value is an object defining one of the descriptor methods, then Python
may override the default behavior and invoke the descriptor method instead.
Where this occurs in the precedence chain depends on which descriptor methods
were defined.

Descriptors are a powerful, general purpose protocol.  They are the mechanism
behind properties, methods, static methods, class methods, and
:func:`super()`.  They are used throughout Python itself.  Descriptors
simplify the underlying C code and offer a flexible set of new tools for
everyday Python programs.


Descriptor Protocol
-------------------

``descr.__get__(self, obj, type=None) -> value``

``descr.__set__(self, obj, value) -> None``

``descr.__delete__(self, obj) -> None``

That is all there is to it.  Define any of these methods and an object is
considered a descriptor and can override default behavior upon being looked up
as an attribute.

If an object defines :meth:`__set__` or :meth:`__delete__`, it is considered
a data descriptor.  Descriptors that only define :meth:`__get__` are called
non-data descriptors (they are typically used for methods but other uses are
possible).

Data and non-data descriptors differ in how overrides are calculated with
respect to entries in an instance's dictionary.  If an instance's dictionary
has an entry with the same name as a data descriptor, the data descriptor
takes precedence.  If an instance's dictionary has an entry with the same
name as a non-data descriptor, the dictionary entry takes precedence.

To make a read-only data descriptor, define both :meth:`__get__` and
:meth:`__set__` with the :meth:`__set__` raising an :exc:`AttributeError` when
called.  Defining the :meth:`__set__` method with an exception raising
placeholder is enough to make it a data descriptor.


Invoking Descriptors
--------------------

A descriptor can be called directly by its method name.  For example,
``d.__get__(obj)``.

Alternatively, it is more common for a descriptor to be invoked automatically
upon attribute access.  For example, ``obj.d`` looks up ``d`` in the dictionary
of ``obj``.  If ``d`` defines the method :meth:`__get__`, then ``d.__get__(obj)``
is invoked according to the precedence rules listed below.

The details of invocation depend on whether ``obj`` is an object or a class.

For objects, the machinery is in :meth:`object.__getattribute__` which
transforms ``b.x`` into ``type(b).__dict__['x'].__get__(b, type(b))``.  The
implementation works through a precedence chain that gives data descriptors
priority over instance variables, instance variables priority over non-data
descriptors, and assigns lowest priority to :meth:`__getattr__` if provided.
The full C implementation can be found in :c:func:`PyObject_GenericGetAttr()` in
:source:`Objects/object.c`.

For classes, the machinery is in :meth:`type.__getattribute__` which transforms
``B.x`` into ``B.__dict__['x'].__get__(None, B)``.  In pure Python, it looks
like::

    def __getattribute__(self, key):
        "Emulate type_getattro() in Objects/typeobject.c"
        v = object.__getattribute__(self, key)
        if hasattr(v, '__get__'):
            return v.__get__(None, self)
        return v

The important points to remember are:

* descriptors are invoked by the :meth:`__getattribute__` method
* overriding :meth:`__getattribute__` prevents automatic descriptor calls
* :meth:`object.__getattribute__` and :meth:`type.__getattribute__` make
  different calls to :meth:`__get__`.
* data descriptors always override instance dictionaries.
* non-data descriptors may be overridden by instance dictionaries.

The object returned by ``super()`` also has a custom :meth:`__getattribute__`
method for invoking descriptors.  The attribute lookup ``super(B, obj).m`` searches
``obj.__class__.__mro__`` for the base class ``A`` immediately following ``B``
and then returns ``A.__dict__['m'].__get__(obj, B)``.  If not a descriptor,
``m`` is returned unchanged.  If not in the dictionary, ``m`` reverts to a
search using :meth:`object.__getattribute__`.

The implementation details are in :c:func:`super_getattro()` in
:source:`Objects/typeobject.c`.  and a pure Python equivalent can be found in
`Guido's Tutorial`_.

.. _`Guido's Tutorial`: https://www.python.org/download/releases/2.2.3/descrintro/#cooperation

The details above show that the mechanism for descriptors is embedded in the
:meth:`__getattribute__()` methods for :class:`object`, :class:`type`, and
:func:`super`.  Classes inherit this machinery when they derive from
:class:`object` or if they have a metaclass providing similar functionality.
Likewise, classes can turn-off descriptor invocation by overriding
:meth:`__getattribute__()`.


Automatic Name Notification
---------------------------

Sometimes it is desirable for a descriptor to know what class variable name it
was assigned to.  When a new class is created, the :class:`type` metaclass
scans the dictionary of the new class.  If any of the entries are descriptors
and if they define :meth:`__set_name__`, that method is called with two
arguments.  The *owner* is the class where the descriptor is used, the *name*
is class variable the descriptor was assigned to.

The implementation details are in :c:func:`type_new()` and
:c:func:`set_names()` in :source:`Objects/typeobject.c`.

Since the update logic is in :meth:`type.__new__`, notifications only take
place at the time of class creation.  If descriptors are added to the class
afterwards, :meth:`__set_name__` will need to be called manually.


Descriptor Example
------------------

The following code creates a class whose objects are data descriptors which
print a message for each get or set.  Overriding :meth:`__getattribute__` is
alternate approach that could do this for every attribute.  However, this
descriptor is useful for monitoring just a few chosen attributes::

    class RevealAccess:
        """A data descriptor that sets and returns values
           normally and prints a message logging their access.
        """

        def __init__(self, initval=None, name='var'):
            self.val = initval
            self.name = name

        def __get__(self, obj, objtype=None):
            print('Retrieving', self.name)
            return self.val

        def __set__(self, obj, val):
            print('Updating', self.name)
            self.val = val

    class B:
        x = RevealAccess(10, 'var "x"')
        y = 5

    >>> m = B()
    >>> m.x
    Retrieving var "x"
    10
    >>> m.x = 20
    Updating var "x"
    >>> m.x
    Retrieving var "x"
    20
    >>> m.y
    5

The protocol is simple and offers exciting possibilities.  Several use cases are
so common that they have been packaged into individual function calls.
Properties, bound methods, static methods, and class methods are all
based on the descriptor protocol.


Properties
----------

Calling :func:`property` is a succinct way of building a data descriptor that
triggers function calls upon access to an attribute.  Its signature is::

    property(fget=None, fset=None, fdel=None, doc=None) -> property attribute

The documentation shows a typical use to define a managed attribute ``x``::

    class C:
        def getx(self): return self.__x
        def setx(self, value): self.__x = value
        def delx(self): del self.__x
        x = property(getx, setx, delx, "I'm the 'x' property.")

To see how :func:`property` is implemented in terms of the descriptor protocol,
here is a pure Python equivalent::

    class Property:
        "Emulate PyProperty_Type() in Objects/descrobject.c"

        def __init__(self, fget=None, fset=None, fdel=None, doc=None):
            self.fget = fget
            self.fset = fset
            self.fdel = fdel
            if doc is None and fget is not None:
                doc = fget.__doc__
            self.__doc__ = doc

        def __get__(self, obj, objtype=None):
            if obj is None:
                return self
            if self.fget is None:
                raise AttributeError("unreadable attribute")
            return self.fget(obj)

        def __set__(self, obj, value):
            if self.fset is None:
                raise AttributeError("can't set attribute")
            self.fset(obj, value)

        def __delete__(self, obj):
            if self.fdel is None:
                raise AttributeError("can't delete attribute")
            self.fdel(obj)

        def getter(self, fget):
            return type(self)(fget, self.fset, self.fdel, self.__doc__)

        def setter(self, fset):
            return type(self)(self.fget, fset, self.fdel, self.__doc__)

        def deleter(self, fdel):
            return type(self)(self.fget, self.fset, fdel, self.__doc__)

The :func:`property` builtin helps whenever a user interface has granted
attribute access and then subsequent changes require the intervention of a
method.

For instance, a spreadsheet class may grant access to a cell value through
``Cell('b10').value``. Subsequent improvements to the program require the cell
to be recalculated on every access; however, the programmer does not want to
affect existing client code accessing the attribute directly.  The solution is
to wrap access to the value attribute in a property data descriptor::

    class Cell:
        ...

        @property
        def value(self):
            "Recalculate the cell before returning value"
            self.recalc()
            return self._value


Functions and Methods
---------------------

Python's object oriented features are built upon a function based environment.
Using non-data descriptors, the two are merged seamlessly.

Class dictionaries store methods as functions.  In a class definition, methods
are written using :keyword:`def` or :keyword:`lambda`, the usual tools for
creating functions.  Methods only differ from regular functions in that the
first argument is reserved for the object instance.  By Python convention, the
instance reference is called *self* but may be called *this* or any other
variable name.

To support method calls, functions include the :meth:`__get__` method for
binding methods during attribute access.  This means that all functions are
non-data descriptors which return bound methods when they are invoked from an
object.  In pure Python, it works like this::

    class Function:
        ...

        def __get__(self, obj, objtype=None):
            "Simulate func_descr_get() in Objects/funcobject.c"
            if obj is None:
                return self
            return types.MethodType(self, obj)

Running the following in class in the interpreter shows how the function
descriptor works in practice::

    class D:
        def f(self, x):
             return x

Access through the class dictionary does not invoke :meth:`__get__`.  Instead,
it just returns the underlying function object::

    >>> D.__dict__['f']
    <function D.f at 0x00C45070>

Dotted access from a class calls :meth:`__get__` which just returns the
underlying function unchanged::

    >>> D.f
    <function D.f at 0x00C45070>

The function has a :term:`qualified name` attribute to support introspection::

    >>> D.f.__qualname__
    'D.f'

Dotted access from an instance calls :meth:`__get__` which returns a bound
method object::

    >>> d = D()
    >>> d.f
    <bound method D.f of <__main__.D object at 0x00B18C90>>

Internally, the bound method stores the underlying function and the bound
instance::

    >>> d.f.__func__
    <function D.f at 0x1012e5ae8>
    >>> d.f.__self__
    <__main__.D object at 0x1012e1f98>


Static Methods and Class Methods
--------------------------------

Non-data descriptors provide a simple mechanism for variations on the usual
patterns of binding functions into methods.

To recap, functions have a :meth:`__get__` method so that they can be converted
to a method when accessed as attributes.  The non-data descriptor transforms an
``obj.f(*args)`` call into ``f(obj, *args)``.  Calling ``cls.f(*args)``
becomes ``f(*args)``.

This chart summarizes the binding and its two most useful variants:

      +-----------------+----------------------+------------------+
      | Transformation  | Called from an       | Called from a    |
      |                 | object               | class            |
      +=================+======================+==================+
      | function        | f(obj, \*args)       | f(\*args)        |
      +-----------------+----------------------+------------------+
      | staticmethod    | f(\*args)            | f(\*args)        |
      +-----------------+----------------------+------------------+
      | classmethod     | f(type(obj), \*args) | f(cls, \*args)   |
      +-----------------+----------------------+------------------+

Static methods return the underlying function without changes.  Calling either
``c.f`` or ``C.f`` is the equivalent of a direct lookup into
``object.__getattribute__(c, "f")`` or ``object.__getattribute__(C, "f")``. As a
result, the function becomes identically accessible from either an object or a
class.

Good candidates for static methods are methods that do not reference the
``self`` variable.

For instance, a statistics package may include a container class for
experimental data.  The class provides normal methods for computing the average,
mean, median, and other descriptive statistics that depend on the data. However,
there may be useful functions which are conceptually related but do not depend
on the data.  For instance, ``erf(x)`` is handy conversion routine that comes up
in statistical work but does not directly depend on a particular dataset.
It can be called either from an object or the class:  ``s.erf(1.5) --> .9332`` or
``Sample.erf(1.5) --> .9332``.

Since staticmethods return the underlying function with no changes, the example
calls are unexciting::

    class E:
        @staticmethod
        def f(x):
            print(x)

    >>> E.f(3)
    3
    >>> E().f(3)
    3

Using the non-data descriptor protocol, a pure Python version of
:func:`staticmethod` would look like this::

    class StaticMethod:
        "Emulate PyStaticMethod_Type() in Objects/funcobject.c"

        def __init__(self, f):
            self.f = f

        def __get__(self, obj, objtype=None):
            return self.f

Unlike static methods, class methods prepend the class reference to the
argument list before calling the function.  This format is the same
for whether the caller is an object or a class::

    class F:
        @classmethod
        def f(cls, x):
            return cls.__name__, x

    >>> print(F.f(3))
    ('F', 3)
    >>> print(F().f(3))
    ('F', 3)


This behavior is useful whenever the function only needs to have a class
reference and does not care about any underlying data.  One use for
classmethods is to create alternate class constructors.  The classmethod
:func:`dict.fromkeys` creates a new dictionary from a list of keys.  The pure
Python equivalent is::

    class Dict:
        ...

        @classmethod
        def fromkeys(cls, iterable, value=None):
            "Emulate dict_fromkeys() in Objects/dictobject.c"
            d = cls()
            for key in iterable:
                d[key] = value
            return d

Now a new dictionary of unique keys can be constructed like this::

    >>> Dict.fromkeys('abracadabra')
    {'a': None, 'r': None, 'b': None, 'c': None, 'd': None}

Using the non-data descriptor protocol, a pure Python version of
:func:`classmethod` would look like this::

    class ClassMethod:
        "Emulate PyClassMethod_Type() in Objects/funcobject.c"

        def __init__(self, f):
            self.f = f

        def __get__(self, obj, cls=None):
            if cls is None:
                cls = type(obj)
            if hasattr(obj, '__get__'):
                return self.f.__get__(cls)
            return types.MethodType(self.f, cls)

The code path for ``hasattr(obj, '__get__')`` was added in Python 3.9 and
makes it possible for :func:`classmethod` to support chained decorators.
For example, a classmethod and property could be chained together::

    class G:
        @classmethod
        @property
        def __doc__(cls):
            return f'A doc for {cls.__name__!r}'