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Split-out a fourth section in the descriptor HowTo guide (GH-22965)
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Doc/howto/descriptor.rst

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@@ -13,7 +13,7 @@ Descriptor HowTo Guide
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:term:`Descriptors <descriptor>` let objects customize attribute lookup,
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storage, and deletion.
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This HowTo guide has three major sections:
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This guide has four major sections:
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1) The "primer" gives a basic overview, moving gently from simple examples,
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adding one feature at a time. It is a great place to start.
@@ -25,6 +25,11 @@ This HowTo guide has three major sections:
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detailed mechanics of how descriptors work. Most people don't need this
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level of detail.
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4) The last section has pure Python equivalents for built-in descriptors that
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are written in C. Read this if you're curious about how functions turn
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into bound methods or about how to implement common tools like
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:func:`classmethod`, :func:`staticmethod`, and :func:`property`.
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Primer
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^^^^^^
@@ -99,7 +104,7 @@ different, updated answers each time::
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3
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>>> os.system('touch games/newfile') # Add a fourth file to the directory
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0
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>>> g.size
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>>> g.size # Automatically updated
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4
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>>> s.size # The songs directory has twenty files
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@@ -197,7 +202,7 @@ be recorded, giving each descriptor its own *public_name* and *private_name*::
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import logging
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logging.basicConfig(level=logging.INFO, force=True)
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logging.basicConfig(level=logging.INFO)
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class LoggedAccess:
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@@ -259,7 +264,7 @@ A :term:`descriptor` is what we call any object that defines :meth:`__get__`,
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:meth:`__set__`, or :meth:`__delete__`.
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Optionally, descriptors can have a :meth:`__set_name__` method. This is only
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used in cases where a descriptor needs to know either the class where it is
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used in cases where a descriptor needs to know either the class where it was
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created or the name of class variable it was assigned to.
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Descriptors get invoked by the dot operator during attribute lookup. If a
@@ -318,7 +323,7 @@ managed attribute descriptor::
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def validate(self, value):
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pass
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Custom validators need to subclass from :class:`Validator` and supply a
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Custom validators need to inherit from :class:`Validator` and must supply a
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:meth:`validate` method to test various restrictions as needed.
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@@ -334,8 +339,9 @@ Here are three practical data validation utilities:
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minimum or maximum.
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3) :class:`String` verifies that a value is a :class:`str`. Optionally, it
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validates a given minimum or maximum length. Optionally, it can test for
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another predicate as well.
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validates a given minimum or maximum length. It can validate a
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user-defined `predicate
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<https://en.wikipedia.org/wiki/Predicate_(mathematical_logic)>`_ as well.
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::
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@@ -398,7 +404,7 @@ Here's how the data validators can be used in a real class::
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class Component:
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name = String(minsize=3, maxsize=10, predicate=str.isupper)
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kind = OneOf('plastic', 'metal')
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kind = OneOf('wood', 'metal', 'plastic')
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quantity = Number(minvalue=0)
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def __init__(self, name, kind, quantity):
@@ -426,9 +432,7 @@ Abstract
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--------
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Defines descriptors, summarizes the protocol, and shows how descriptors are
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called. Examines a custom descriptor and several built-in Python descriptors
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including functions, properties, static methods, and class methods. Shows how
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each works by giving a pure Python equivalent and a sample application.
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called. Provides an example showing how object relational mappings work.
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Learning about descriptors not only provides access to a larger toolset, it
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creates a deeper understanding of how Python works and an appreciation for the
@@ -519,34 +523,27 @@ The full C implementation can be found in :c:func:`PyObject_GenericGetAttr()` in
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It transforms ``A.x`` into ``A.__dict__['x'].__get__(None, A)``.
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In pure Python, it looks like this::
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def __getattribute__(cls, key):
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"Emulate type_getattro() in Objects/typeobject.c"
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v = object.__getattribute__(cls, key)
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if hasattr(v, '__get__'):
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return v.__get__(None, cls)
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return v
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The full C implementation can be found in :c:func:`type_getattro()` in
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:source:`Objects/typeobject.c`.
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**Super**: The machinery is in the custom :meth:`__getattribute__` method for
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object returned by :class:`super()`.
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The attribute lookup ``super(A, obj).m`` searches ``obj.__class__.__mro__`` for
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the base class ``B`` immediately following ``A`` and then returns
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``B.__dict__['m'].__get__(obj, A)``.
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If not a descriptor, ``m`` is returned unchanged. If not in the dictionary,
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``m`` reverts to a search using :meth:`object.__getattribute__`.
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``B.__dict__['m'].__get__(obj, A)``. If not a descriptor, ``m`` is returned
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unchanged. If not in the dictionary, ``m`` reverts to a search using
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:meth:`object.__getattribute__`.
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The implementation details are in :c:func:`super_getattro()` in
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:source:`Objects/typeobject.c`. A pure Python equivalent can be found in
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`Guido's Tutorial`_.
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.. _`Guido's Tutorial`: https://www.python.org/download/releases/2.2.3/descrintro/#cooperation
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**Summary**: The details listed above show that the mechanism for descriptors is
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embedded in the :meth:`__getattribute__()` methods for :class:`object`,
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:class:`type`, and :func:`super`.
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**Summary**: The mechanism for descriptors is embedded in the
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:meth:`__getattribute__()` methods for :class:`object`, :class:`type`, and
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:func:`super`.
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The important points to remember are:
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@@ -586,15 +583,16 @@ place at the time of class creation. If descriptors are added to the class
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afterwards, :meth:`__set_name__` will need to be called manually.
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Descriptor Example
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------------------
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ORM Example
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-----------
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The following code is simplified skeleton showing how data descriptors could
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be used to implement an `object relational mapping
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<https://en.wikipedia.org/wiki/Object%E2%80%93relational_mapping>`_.
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The essential idea is that instances only hold keys to a database table. The
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actual data is stored in an external table that is being dynamically updated::
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The essential idea is that the data is stored in an external database. The
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Python instances only hold keys to the database's tables. Descriptors take
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care of lookups or updates::
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class Field:
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conn.execute(self.store, [value, obj.key])
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conn.commit()
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We can use the :class:`Field` to define "models" that describe the schema for
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each table in a database::
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We can use the :class:`Field` class to define "models" that describe the schema
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for each table in a database::
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class Movie:
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table = 'Movies' # Table name
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>>> Movie('Star Wars').director
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'J.J. Abrams'
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Pure Python Equivalents
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^^^^^^^^^^^^^^^^^^^^^^^
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The descriptor protocol is simple and offers exciting possibilities. Several
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use cases are so common that they have been packaged into individual function
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calls. Properties, bound methods, static methods, and class methods are all
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based on the descriptor protocol.
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use cases are so common that they have been prepackaged into builtin tools.
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Properties, bound methods, static methods, and class methods are all based on
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the descriptor protocol.
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Properties
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Methods can be created manually with :class:`types.MethodType` which is
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roughly equivalent to::
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class Method:
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class MethodType:
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"Emulate Py_MethodType in Objects/classobject.c"
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def __init__(self, func, obj):
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"Simulate func_descr_get() in Objects/funcobject.c"
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if obj is None:
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return self
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return types.MethodType(self, obj)
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return MethodType(self, obj)
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Running the following class in the interpreter shows how the function
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descriptor works in practice::
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*cls* comes from in class methods, this is it!
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Static Methods and Class Methods
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--------------------------------
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Static Methods
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--------------
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Non-data descriptors provide a simple mechanism for variations on the usual
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patterns of binding functions into methods.
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def __get__(self, obj, objtype=None):
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return self.f
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Class Methods
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-------------
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Unlike static methods, class methods prepend the class reference to the
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argument list before calling the function. This format is the same
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for whether the caller is an object or a class::
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>>> print(F().f(3))
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('F', 3)
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This behavior is useful whenever the function only needs to have a class
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reference and does not care about any underlying data. One use for
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class methods is to create alternate class constructors. The classmethod
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:func:`dict.fromkeys` creates a new dictionary from a list of keys. The pure
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Python equivalent is::
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This behavior is useful whenever the method only needs to have a class
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reference and does rely on data stored in a specific instance. One use for
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class methods is to create alternate class constructors. For example, the
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classmethod :func:`dict.fromkeys` creates a new dictionary from a list of
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keys. The pure Python equivalent is::
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class Dict:
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...
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cls = type(obj)
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if hasattr(obj, '__get__'):
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return self.f.__get__(cls)
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return types.MethodType(self.f, cls)
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return MethodType(self.f, cls)
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The code path for ``hasattr(obj, '__get__')`` was added in Python 3.9 and
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makes it possible for :func:`classmethod` to support chained decorators.

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