pep-0252.txt

PEP: 252
Title: Making Types Look More Like Classes
Version: $Revision$
Last-Modified: $Date$
Author: guido@python.org (Guido van Rossum)
Status: Final
Type: Standards Track
Content-Type: text/x-rst
Created: 19-Apr-2001
Python-Version: 2.2
Post-History:

Abstract
========

This PEP proposes changes to the introspection API for types that
makes them look more like classes, and their instances more like
class instances.  For example, ``type(x)`` will be equivalent to
``x.__class__`` for most built-in types.  When C is ``x.__class__``,
``x.meth(a)`` will generally be equivalent to ``C.meth(x, a)``, and
``C.__dict__`` contains x's methods and other attributes.

This PEP also introduces a new approach to specifying attributes,
using attribute descriptors, or descriptors for short.
Descriptors unify and generalize several different common
mechanisms used for describing attributes: a descriptor can
describe a method, a typed field in the object structure, or a
generalized attribute represented by getter and setter functions.

Based on the generalized descriptor API, this PEP also introduces
a way to declare class methods and static methods.

[Editor's note: the ideas described in this PEP have been incorporated
into Python.  The PEP no longer accurately describes the implementation.]


Introduction
============

One of Python's oldest language warts is the difference between
classes and types.  For example, you can't directly subclass the
dictionary type, and the introspection interface for finding out
what methods and instance variables an object has is different for
types and for classes.

Healing the class/type split is a big effort, because it affects
many aspects of how Python is implemented.  This PEP concerns
itself with making the introspection API for types look the same
as that for classes.  Other PEPs will propose making classes look
more like types, and subclassing from built-in types; these topics
are not on the table for this PEP.


Introspection APIs
==================

Introspection concerns itself with finding out what attributes an
object has.  Python's very general getattr/setattr API makes it
impossible to guarantee that there always is a way to get a list
of all attributes supported by a specific object, but in practice
two conventions have appeared that together work for almost all
objects.  I'll call them the class-based introspection API and the
type-based introspection API; class API and type API for short.

The class-based introspection API is used primarily for class
instances; it is also used by Jim Fulton's ExtensionClasses.  It
assumes that all data attributes of an object x are stored in the
dictionary ``x.__dict__``, and that all methods and class variables
can be found by inspection of x's class, written as ``x.__class__``.
Classes have a ``__dict__`` attribute, which yields a dictionary
containing methods and class variables defined by the class
itself, and a ``__bases__`` attribute, which is a tuple of base
classes that must be inspected recursively.  Some assumptions here
are:

- attributes defined in the instance dict override attributes
  defined by the object's class;

- attributes defined in a derived class override attributes
  defined in a base class;

- attributes in an earlier base class (meaning occurring earlier
  in ``__bases__``) override attributes in a later base class.

(The last two rules together are often summarized as the
left-to-right, depth-first rule for attribute search.  This is the
classic Python attribute lookup rule.  Note that PEP 253 will
propose to change the attribute lookup order, and if accepted,
this PEP will follow suit.)

The type-based introspection API is supported in one form or
another by most built-in objects.  It uses two special attributes,
``__members__`` and ``__methods__``.  The ``__methods__`` attribute, if
present, is a list of method names supported by the object.  The
``__members__`` attribute, if present, is a list of data attribute
names supported by the object.

The type API is sometimes combined with a ``__dict__`` that works the
same as for instances (for example for function objects in
Python 2.1, ``f.__dict__`` contains f's dynamic attributes, while
``f.__members__`` lists the names of f's statically defined
attributes).

Some caution must be exercised: some objects don't list their
"intrinsic" attributes (like ``__dict__`` and ``__doc__``) in ``__members__``,
while others do; sometimes attribute names occur both in
``__members__`` or ``__methods__`` and as keys in ``__dict__``, in which case
it's anybody's guess whether the value found in ``__dict__`` is used
or not.

The type API has never been carefully specified.  It is part of
Python folklore, and most third party extensions support it
because they follow examples that support it.  Also, any type that
uses ``Py_FindMethod()`` and/or ``PyMember_Get()`` in its tp_getattr
handler supports it, because these two functions special-case the
attribute names ``__methods__`` and ``__members__``, respectively.

Jim Fulton's ExtensionClasses ignore the type API, and instead
emulate the class API, which is more powerful.  In this PEP, I
propose to phase out the type API in favor of supporting the class
API for all types.

One argument in favor of the class API is that it doesn't require
you to create an instance in order to find out which attributes a
type supports; this in turn is useful for documentation
processors.  For example, the socket module exports the SocketType
object, but this currently doesn't tell us what methods are
defined on socket objects.  Using the class API, SocketType would
show exactly what the methods for socket objects are, and we can
even extract their docstrings, without creating a socket.  (Since
this is a C extension module, the source-scanning approach to
docstring extraction isn't feasible in this case.)


Specification of the class-based introspection API
==================================================

Objects may have two kinds of attributes: static and dynamic.  The
names and sometimes other properties of static attributes are
knowable by inspection of the object's type or class, which is
accessible through ``obj.__class__`` or ``type(obj)``.  (I'm using type
and class interchangeably; a clumsy but descriptive term that fits
both is "meta-object".)

(XXX static and dynamic are not great terms to use here, because
"static" attributes may actually behave quite dynamically, and
because they have nothing to do with static class members in C++
or Java.  Barry suggests to use immutable and mutable instead, but
those words already have precise and different meanings in
slightly different contexts, so I think that would still be
confusing.)

Examples of dynamic attributes are instance variables of class
instances, module attributes, etc.  Examples of static attributes
are the methods of built-in objects like lists and dictionaries,
and the attributes of frame and code objects (``f.f_code``,
``c.co_filename``, etc.).  When an object with dynamic attributes
exposes these through its ``__dict__`` attribute, ``__dict__`` is a static
attribute.

The names and values of dynamic properties are typically stored in
a dictionary, and this dictionary is typically accessible as
``obj.__dict__``.  The rest of this specification is more concerned
with discovering the names and properties of static attributes
than with dynamic attributes; the latter are easily discovered by
inspection of ``obj.__dict__``.

In the discussion below, I distinguish two kinds of objects:
regular objects (like lists, ints, functions) and meta-objects.
Types and classes are meta-objects.  Meta-objects are also regular
objects, but we're mostly interested in them because they are
referenced by the ``__class__`` attribute of regular objects (or by
the ``__bases__`` attribute of other meta-objects).

The class introspection API consists of the following elements:

- the ``__class__`` and ``__dict__`` attributes on regular objects;

- the ``__bases__`` and ``__dict__`` attributes on meta-objects;

- precedence rules;

- attribute descriptors.

Together, these not only tell us about **all** attributes defined by
a meta-object, but they also help us calculate the value of a
specific attribute of a given object.

1. The ``__dict__`` attribute on regular objects

   A regular object may have a ``__dict__`` attribute.  If it does,
   this should be a mapping (not necessarily a dictionary)
   supporting at least ``__getitem__()``, ``keys()``, and ``has_key()``.  This
   gives the dynamic attributes of the object.  The keys in the
   mapping give attribute names, and the corresponding values give
   their values.

   Typically, the value of an attribute with a given name is the
   same object as the value corresponding to that name as a key in
   the ``__dict__``.  In other words, ``obj.__dict__['spam']`` is ``obj.spam``.
   (But see the precedence rules below; a static attribute with
   the same name **may** override the dictionary item.)

2. The ``__class__`` attribute on regular objects

   A regular object usually has a ``__class__`` attribute.  If it
   does, this references a meta-object.  A meta-object can define
   static attributes for the regular object whose ``__class__`` it
   is.  This is normally done through the following mechanism:

3. The ``__dict__`` attribute on meta-objects

   A meta-object may have a ``__dict__`` attribute, of the same form
   as the ``__dict__`` attribute for regular objects (a mapping but
   not necessarily a dictionary).  If it does, the keys of the
   meta-object's ``__dict__`` are names of static attributes for the
   corresponding regular object.  The values are attribute
   descriptors; we'll explain these later.  An unbound method is a
   special case of an attribute descriptor.

   Because a meta-object is also a regular object, the items in a
   meta-object's ``__dict__`` correspond to attributes of the
   meta-object; however, some transformation may be applied, and
   bases (see below) may define additional dynamic attributes.  In
   other words, ``mobj.spam`` is not always ``mobj.__dict__['spam']``.
   (This rule contains a loophole because for classes, if
   ``C.__dict__['spam']`` is a function, ``C.spam`` is an unbound method
   object.)

4. The ``__bases__`` attribute on meta-objects

   A meta-object may have a ``__bases__`` attribute.  If it does, this
   should be a sequence (not necessarily a tuple) of other
   meta-objects, the bases.  An absent ``__bases__`` is equivalent to
   an empty sequence of bases.  There must never be a cycle in the
   relationship between meta-objects defined by ``__bases__``
   attributes; in other words, the ``__bases__`` attributes define a
   directed acyclic graph, with arcs pointing from derived
   meta-objects to their base meta-objects.  (It is not
   necessarily a tree, since multiple classes can have the same
   base class.)  The ``__dict__`` attributes of a meta-object in the
   inheritance graph supply attribute descriptors for the regular
   object whose ``__class__`` attribute points to the root of the
   inheritance tree (which is not the same as the root of the
   inheritance hierarchy -- rather more the opposite, at the
   bottom given how inheritance trees are typically drawn).
   Descriptors are first searched in the dictionary of the root
   meta-object, then in its bases, according to a precedence rule
   (see the next paragraph).

5. Precedence rules

   When two meta-objects in the inheritance graph for a given
   regular object both define an attribute descriptor with the
   same name, the search order is up to the meta-object.  This
   allows different meta-objects to define different search
   orders.  In particular, classic classes use the old
   left-to-right depth-first rule, while new-style classes use a
   more advanced rule (see the section on method resolution order
   in PEP 253).

   When a dynamic attribute (one defined in a regular object's
   ``__dict__``) has the same name as a static attribute (one defined
   by a meta-object in the inheritance graph rooted at the regular
   object's ``__class__``), the static attribute has precedence if it
   is a descriptor that defines a ``__set__`` method (see below);
   otherwise (if there is no ``__set__`` method) the dynamic attribute
   has precedence.  In other words, for data attributes (those
   with a ``__set__`` method), the static definition overrides the
   dynamic definition, but for other attributes, dynamic overrides
   static.

   Rationale: we can't have a simple rule like "static overrides
   dynamic" or "dynamic overrides static", because some static
   attributes indeed override dynamic attributes; for example, a
   key '__class__' in an instance's ``__dict__`` is ignored in favor
   of the statically defined ``__class__`` pointer, but on the other
   hand most keys in ``inst.__dict__`` override attributes defined in
   ``inst.__class__``.  Presence of a ``__set__`` method on a descriptor
   indicates that this is a data descriptor.  (Even read-only data
   descriptors have a ``__set__`` method: it always raises an
   exception.)  Absence of a ``__set__`` method on a descriptor
   indicates that the descriptor isn't interested in intercepting
   assignment, and then the classic rule applies: an instance
   variable with the same name as a method hides the method until
   it is deleted.

6. Attribute descriptors

   This is where it gets interesting -- and messy.  Attribute
   descriptors (descriptors for short) are stored in the
   meta-object's ``__dict__`` (or in the ``__dict__`` of one of its
   ancestors), and have two uses: a descriptor can be used to get
   or set the corresponding attribute value on the (regular,
   non-meta) object, and it has an additional interface that
   describes the attribute for documentation and introspection
   purposes.

   There is little prior art in Python for designing the
   descriptor's interface, neither for getting/setting the value
   nor for describing the attribute otherwise, except some trivial
   properties (it's reasonable to assume that ``__name__`` and ``__doc__``
   should be the attribute's name and docstring).  I will propose
   such an API below.

   If an object found in the meta-object's ``__dict__`` is not an
   attribute descriptor, backward compatibility dictates certain
   minimal semantics.  This basically means that if it is a Python
   function or an unbound method, the attribute is a method;
   otherwise, it is the default value for a dynamic data
   attribute.  Backwards compatibility also dictates that (in the
   absence of a ``__setattr__`` method) it is legal to assign to an
   attribute corresponding to a method, and that this creates a
   data attribute shadowing the method for this particular
   instance.  However, these semantics are only required for
   backwards compatibility with regular classes.

The introspection API is a read-only API.  We don't define the
effect of assignment to any of the special attributes (``__dict__``,
``__class__`` and ``__bases__``), nor the effect of assignment to the
items of a ``__dict__``.  Generally, such assignments should be
considered off-limits.  A future PEP may define some semantics for
some such assignments.  (Especially because currently instances
support assignment to ``__class__`` and ``__dict__``, and classes support
assignment to ``__bases__`` and ``__dict__``.)


Specification of the attribute descriptor API
=============================================

Attribute descriptors may have the following attributes.  In the
examples, x is an object, C is ``x.__class__``, ``x.meth()`` is a method,
and ``x.ivar`` is a data attribute or instance variable.  All
attributes are optional -- a specific attribute may or may not be
present on a given descriptor.  An absent attribute means that the
corresponding information is not available or the corresponding
functionality is not implemented.

- ``__name__``: the attribute name.  Because of aliasing and renaming,
  the attribute may (additionally or exclusively) be known under a
  different name, but this is the name under which it was born.
  Example: ``C.meth.__name__ == 'meth'``.

- ``__doc__``: the attribute's documentation string.  This may be
  None.

- ``__objclass__``: the class that declared this attribute.  The
  descriptor only applies to objects that are instances of this
  class (this includes instances of its subclasses).  Example:
  ``C.meth.__objclass__ is C``.

- ``__get__()``: a function callable with one or two arguments that
  retrieves the attribute value from an object.  This is also
  referred to as a "binding" operation, because it may return a
  "bound method" object in the case of method descriptors.  The
  first argument, X, is the object from which the attribute must
  be retrieved or to which it must be bound.  When X is None, the
  optional second argument, T, should be meta-object and the
  binding operation may return an **unbound** method restricted to
  instances of T.  When both X and T are specified, X should be an
  instance of T.  Exactly what is returned by the binding
  operation depends on the semantics of the descriptor; for
  example, static methods and class methods (see below) ignore the
  instance and bind to the type instead.

- ``__set__()``: a function of two arguments that sets the attribute
  value on the object.  If the attribute is read-only, this method
  may raise a TypeError or ``AttributeError`` exception (both are
  allowed, because both are historically found for undefined or
  unsettable attributes).  Example:
  ``C.ivar.set(x, y) ~~ x.ivar = y``.


Static methods and class methods
================================

The descriptor API makes it possible to add static methods and
class methods.  Static methods are easy to describe: they behave
pretty much like static methods in C++ or Java.  Here's an
example::

    class C:

        def foo(x, y):
            print "staticmethod", x, y
        foo = staticmethod(foo)

    C.foo(1, 2)
    c = C()
    c.foo(1, 2)

Both the call ``C.foo(1, 2)`` and the call ``c.foo(1, 2)`` call ``foo()`` with
two arguments, and print "staticmethod 1 2".  No "self" is declared in
the definition of ``foo()``, and no instance is required in the call.

The line "foo = staticmethod(foo)" in the class statement is the
crucial element: this makes ``foo()`` a static method.  The built-in
``staticmethod()`` wraps its function argument in a special kind of
descriptor whose ``__get__()`` method returns the original function
unchanged.  Without this, the ``__get__()`` method of standard
function objects would have created a bound method object for
'c.foo' and an unbound method object for 'C.foo'.

(XXX Barry suggests to use "sharedmethod" instead of
"staticmethod", because the word static is being overloaded in so
many ways already.  But I'm not sure if shared conveys the right
meaning.)

Class methods use a similar pattern to declare methods that
receive an implicit first argument that is the *class* for which
they are invoked.  This has no C++ or Java equivalent, and is not
quite the same as what class methods are in Smalltalk, but may
serve a similar purpose.  According to Armin Rigo, they are
similar to "virtual class methods" in Borland Pascal dialect
Delphi.  (Python also has real metaclasses, and perhaps methods
defined in a metaclass have more right to the name "class method";
but I expect that most programmers won't be using metaclasses.)
Here's an example::

    class C:

        def foo(cls, y):
            print "classmethod", cls, y
        foo = classmethod(foo)

    C.foo(1)
    c = C()
    c.foo(1)

Both the call ``C.foo(1)`` and the call ``c.foo(1)`` end up calling ``foo()``
with **two** arguments, and print "classmethod __main__.C 1".  The
first argument of ``foo()`` is implied, and it is the class, even if
the method was invoked via an instance.  Now let's continue the
example::

    class D(C):
        pass

    D.foo(1)
    d = D()
    d.foo(1)

This prints "classmethod __main__.D 1" both times; in other words,
the class passed as the first argument of ``foo()`` is the class
involved in the call, not the class involved in the definition of
``foo()``.

But notice this::

    class E(C):
        def foo(cls, y): # override C.foo
            print "E.foo() called"
            C.foo(y)
        foo = classmethod(foo)

    E.foo(1)
    e = E()
    e.foo(1)

In this example, the call to ``C.foo()`` from ``E.foo()`` will see class C
as its first argument, not class E.  This is to be expected, since
the call specifies the class C.  But it stresses the difference
between these class methods and methods defined in metaclasses,
where an upcall to a metamethod would pass the target class as an
explicit first argument.  (If you don't understand this, don't
worry, you're not alone.)  Note that calling ``cls.foo(y)`` would be a
mistake -- it would cause infinite recursion.  Also note that you
can't specify an explicit 'cls' argument to a class method.  If
you want this (e.g. the ``__new__`` method in PEP 253 requires this),
use a static method with a class as its explicit first argument
instead.


C API
=====

XXX The following is VERY rough text that I wrote with a different
audience in mind; I'll have to go through this to edit it more.
XXX It also doesn't go into enough detail for the C API.

A built-in type can declare special data attributes in two ways:
using a struct memberlist (defined in structmember.h) or a struct
getsetlist (defined in descrobject.h).  The struct memberlist is
an old mechanism put to new use: each attribute has a descriptor
record including its name, an enum giving its type (various C
types are supported as well as ``PyObject *``), an offset from the
start of the instance, and a read-only flag.

The struct getsetlist mechanism is new, and intended for cases
that don't fit in that mold, because they either require
additional checking, or are plain calculated attributes.  Each
attribute here has a name, a getter C function pointer, a setter C
function pointer, and a context pointer.  The function pointers
are optional, so that for example setting the setter function
pointer to ``NULL`` makes a read-only attribute.  The context pointer
is intended to pass auxiliary information to generic getter/setter
functions, but I haven't found a need for this yet.

Note that there is also a similar mechanism to declare built-in
methods: these are ``PyMethodDef`` structures, which contain a name
and a C function pointer (and some flags for the calling
convention).

Traditionally, built-in types have had to define their own
``tp_getattro`` and ``tp_setattro`` slot functions to make these attribute
definitions work (``PyMethodDef`` and struct memberlist are quite
old).  There are convenience functions that take an array of
``PyMethodDef`` or memberlist structures, an object, and an attribute
name, and return or set the attribute if found in the list, or
raise an exception if not found.  But these convenience functions
had to be explicitly called by the ``tp_getattro`` or ``tp_setattro``
method of the specific type, and they did a linear search of the
array using ``strcmp()`` to find the array element describing the
requested attribute.

I now have a brand spanking new generic mechanism that improves
this situation substantially.

- Pointers to arrays of ``PyMethodDef``, memberlist, getsetlist
  structures are part of the new type object (``tp_methods``,
  ``tp_members``, ``tp_getset``).

- At type initialization time (in ``PyType_InitDict()``), for each
  entry in those three arrays, a descriptor object is created and
  placed in a dictionary that belongs to the type (``tp_dict``).

- Descriptors are very lean objects that mostly point to the
  corresponding structure.  An implementation detail is that all
  descriptors share the same object type, and a discriminator
  field tells what kind of descriptor it is (method, member, or
  getset).

- As explained in PEP 252, descriptors have a ``get()`` method that
  takes an object argument and returns that object's attribute;
  descriptors for writable attributes also have a ``set()`` method
  that takes an object and a value and set that object's
  attribute.  Note that the ``get()`` object also serves as a ``bind()``
  operation for methods, binding the unbound method implementation
  to the object.

- Instead of providing their own tp_getattro and tp_setattro
  implementation, almost all built-in objects now place
  ``PyObject_GenericGetAttr`` and (if they have any writable
  attributes) ``PyObject_GenericSetAttr`` in their ``tp_getattro`` and
  ``tp_setattro`` slots.  (Or, they can leave these ``NULL``, and inherit
  them from the default base object, if they arrange for an
  explicit call to ``PyType_InitDict()`` for the type before the first
  instance is created.)

- In the simplest case, ``PyObject_GenericGetAttr()`` does exactly one
  dictionary lookup: it looks up the attribute name in the type's
  dictionary (obj->ob_type->tp_dict).  Upon success, there are two
  possibilities: the descriptor has a get method, or it doesn't.
  For speed, the get and set methods are type slots: ``tp_descr_get``
  and ``tp_descr_set``.  If the ``tp_descr_get`` slot is non-NULL, it is
  called, passing the object as its only argument, and the return
  value from this call is the result of the getattr operation.  If
  the ``tp_descr_get`` slot is ``NULL``, as a fallback the descriptor
  itself is returned (compare class attributes that are not
  methods but simple values).

- ``PyObject_GenericSetAttr()`` works very similar but uses the
  ``tp_descr_set`` slot and calls it with the object and the new
  attribute value; if the ``tp_descr_set`` slot is ``NULL``, an
  ``AttributeError`` is raised.

- But now for a more complicated case.  The approach described
  above is suitable for most built-in objects such as lists,
  strings, numbers.  However, some object types have a dictionary
  in each instance that can store arbitrary attributes.  In fact,
  when you use a class statement to subtype an existing built-in
  type, you automatically get such a dictionary (unless you
  explicitly turn it off, using another advanced feature,
  ``__slots__``).  Let's call this the instance dict, to distinguish
  it from the type dict.

- In the more complicated case, there's a conflict between names
  stored in the instance dict and names stored in the type dict.
  If both dicts have an entry with the same key, which one should
  we return?  Looking at classic Python for guidance, I find
  conflicting rules: for class instances, the instance dict
  overrides the class dict, **except** for the special attributes
  (like ``__dict__`` and ``__class__``), which have priority over the
  instance dict.

- I resolved this with the following set of rules, implemented in
  ``PyObject_GenericGetAttr()``:

  1. Look in the type dict.  If you find a **data** descriptor, use
     its ``get()`` method to produce the result.  This takes care of
     special attributes like ``__dict__`` and ``__class__``.

  2. Look in the instance dict.  If you find anything, that's it.
     (This takes care of the requirement that normally the
     instance dict overrides the class dict.)

  3. Look in the type dict again (in reality this uses the saved
     result from step 1, of course).  If you find a descriptor,
     use its ``get()`` method; if you find something else, that's it;
     if it's not there, raise ``AttributeError``.

  This requires a classification of descriptors as data and
  nondata descriptors.  The current implementation quite sensibly
  classifies member and getset descriptors as data (even if they
  are read-only!)  and method descriptors as nondata.
  Non-descriptors (like function pointers or plain values) are
  also classified as non-data (!).

- This scheme has one drawback: in what I assume to be the most
  common case, referencing an instance variable stored in the
  instance dict, it does **two** dictionary lookups, whereas the
  classic scheme did a quick test for attributes starting with two
  underscores plus a single dictionary lookup.  (Although the
  implementation is sadly structured as ``instance_getattr()`` calling
  ``instance_getattr1()`` calling ``instance_getattr2()`` which finally
  calls ``PyDict_GetItem()``, and the underscore test calls
  ``PyString_AsString()`` rather than inlining this.  I wonder if
  optimizing the snot out of this might not be a good idea to
  speed up Python 2.2, if we weren't going to rip it all out. :-)

- A benchmark verifies that in fact this is as fast as classic
  instance variable lookup, so I'm no longer worried.

- Modification for dynamic types: step 1 and 3 look in the
  dictionary of the type and all its base classes (in MRO
  sequence, or couse).


Discussion
==========

XXX


Examples
========

Let's look at lists.  In classic Python, the method names of
lists were available as the __methods__ attribute of list objects::

    >>> [].__methods__
    ['append', 'count', 'extend', 'index', 'insert', 'pop',
    'remove', 'reverse', 'sort']
    >>>

Under the new proposal, the __methods__ attribute no longer exists::

    >>> [].__methods__
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    AttributeError: 'list' object has no attribute '__methods__'
    >>>

Instead, you can get the same information from the list type::

    >>> T = [].__class__
    >>> T
    <type 'list'>
    >>> dir(T)                # like T.__dict__.keys(), but sorted
    ['__add__', '__class__', '__contains__', '__eq__', '__ge__',
    '__getattr__', '__getitem__', '__getslice__', '__gt__',
    '__iadd__', '__imul__', '__init__', '__le__', '__len__',
    '__lt__', '__mul__', '__ne__', '__new__', '__radd__',
    '__repr__', '__rmul__', '__setitem__', '__setslice__', 'append',
    'count', 'extend', 'index', 'insert', 'pop', 'remove',
    'reverse', 'sort']
    >>>

The new introspection API gives more information than the old one:
in addition to the regular methods, it also shows the methods that
are normally invoked through special notations, e.g.  ``__iadd__``
(``+=``), ``__len__`` (``len``), ``__ne__`` (``!=``).
You can invoke any method from this list directly::

    >>> a = ['tic', 'tac']
    >>> T.__len__(a)          # same as len(a)
    2
    >>> T.append(a, 'toe')    # same as a.append('toe')
    >>> a
    ['tic', 'tac', 'toe']
    >>>

This is just like it is for user-defined classes.

Notice a familiar yet surprising name in the list: ``__init__``.  This
is the domain of PEP 253.


Backwards compatibility
=======================

XXX


Warnings and Errors
===================

XXX


Implementation
==============

A partial implementation of this PEP is available from CVS as a
branch named "descr-branch".  To experiment with this
implementation, proceed to check out Python from CVS according to
the instructions at http://sourceforge.net/cvs/?group_id=5470 but
add the arguments "-r descr-branch" to the cvs checkout command.
(You can also start with an existing checkout and do "cvs update
-r descr-branch".)  For some examples of the features described
here, see the file Lib/test/test_descr.py.

Note: the code in this branch goes way beyond this PEP; it is also
the experimentation area for PEP 253 (Subtyping Built-in Types).


References
==========

XXX


Copyright
=========

This document has been placed in the public domain.

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