
Data model
**********


Objects, values and types
=========================

*Objects* are Python's abstraction for data.  All data in a Python
program is represented by objects or by relations between objects. (In
a sense, and in conformance to Von Neumann's model of a "stored
program computer," code is also represented by objects.)

Every object has an identity, a type and a value.  An object's
*identity* never changes once it has been created; you may think of it
as the object's address in memory.  The '``is``' operator compares the
identity of two objects; the ``id()`` function returns an integer
representing its identity (currently implemented as its address). An
object's *type* is also unchangeable. [1] An object's type determines
the operations that the object supports (e.g., "does it have a
length?") and also defines the possible values for objects of that
type.  The ``type()`` function returns an object's type (which is an
object itself).  The *value* of some objects can change.  Objects
whose value can change are said to be *mutable*; objects whose value
is unchangeable once they are created are called *immutable*. (The
value of an immutable container object that contains a reference to a
mutable object can change when the latter's value is changed; however
the container is still considered immutable, because the collection of
objects it contains cannot be changed.  So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.)
An object's mutability is determined by its type; for instance,
numbers, strings and tuples are immutable, while dictionaries and
lists are mutable.

Objects are never explicitly destroyed; however, when they become
unreachable they may be garbage-collected.  An implementation is
allowed to postpone garbage collection or omit it altogether --- it is
a matter of implementation quality how garbage collection is
implemented, as long as no objects are collected that are still
reachable.

**CPython implementation detail:** CPython currently uses a reference-
counting scheme with (optional) delayed detection of cyclically linked
garbage, which collects most objects as soon as they become
unreachable, but is not guaranteed to collect garbage containing
circular references.  See the documentation of the ``gc`` module for
information on controlling the collection of cyclic garbage. Other
implementations act differently and CPython may change.

Note that the use of the implementation's tracing or debugging
facilities may keep objects alive that would normally be collectable.
Also note that catching an exception with a '``try``...``except``'
statement may keep objects alive.

Some objects contain references to "external" resources such as open
files or windows.  It is understood that these resources are freed
when the object is garbage-collected, but since garbage collection is
not guaranteed to happen, such objects also provide an explicit way to
release the external resource, usually a ``close()`` method. Programs
are strongly recommended to explicitly close such objects.  The
'``try``...``finally``' statement provides a convenient way to do
this.

Some objects contain references to other objects; these are called
*containers*. Examples of containers are tuples, lists and
dictionaries.  The references are part of a container's value.  In
most cases, when we talk about the value of a container, we imply the
values, not the identities of the contained objects; however, when we
talk about the mutability of a container, only the identities of the
immediately contained objects are implied.  So, if an immutable
container (like a tuple) contains a reference to a mutable object, its
value changes if that mutable object is changed.

Types affect almost all aspects of object behavior.  Even the
importance of object identity is affected in some sense: for immutable
types, operations that compute new values may actually return a
reference to any existing object with the same type and value, while
for mutable objects this is not allowed.  E.g., after ``a = 1; b =
1``, ``a`` and ``b`` may or may not refer to the same object with the
value one, depending on the implementation, but after ``c = []; d =
[]``, ``c`` and ``d`` are guaranteed to refer to two different,
unique, newly created empty lists. (Note that ``c = d = []`` assigns
the same object to both ``c`` and ``d``.)


The standard type hierarchy
===========================

Below is a list of the types that are built into Python.  Extension
modules (written in C, Java, or other languages, depending on the
implementation) can define additional types.  Future versions of
Python may add types to the type hierarchy (e.g., rational numbers,
efficiently stored arrays of integers, etc.).

Some of the type descriptions below contain a paragraph listing
'special attributes.'  These are attributes that provide access to the
implementation and are not intended for general use.  Their definition
may change in the future.

None
   This type has a single value.  There is a single object with this
   value. This object is accessed through the built-in name ``None``.
   It is used to signify the absence of a value in many situations,
   e.g., it is returned from functions that don't explicitly return
   anything. Its truth value is false.

NotImplemented
   This type has a single value.  There is a single object with this
   value. This object is accessed through the built-in name
   ``NotImplemented``. Numeric methods and rich comparison methods may
   return this value if they do not implement the operation for the
   operands provided.  (The interpreter will then try the reflected
   operation, or some other fallback, depending on the operator.)  Its
   truth value is true.

Ellipsis
   This type has a single value.  There is a single object with this
   value. This object is accessed through the built-in name
   ``Ellipsis``. It is used to indicate the presence of the ``...``
   syntax in a slice.  Its truth value is true.

``numbers.Number``
   These are created by numeric literals and returned as results by
   arithmetic operators and arithmetic built-in functions.  Numeric
   objects are immutable; once created their value never changes.
   Python numbers are of course strongly related to mathematical
   numbers, but subject to the limitations of numerical representation
   in computers.

   Python distinguishes between integers, floating point numbers, and
   complex numbers:

   ``numbers.Integral``
      These represent elements from the mathematical set of integers
      (positive and negative).

      There are three types of integers:

      Plain integers
         These represent numbers in the range -2147483648 through
         2147483647. (The range may be larger on machines with a
         larger natural word size, but not smaller.)  When the result
         of an operation would fall outside this range, the result is
         normally returned as a long integer (in some cases, the
         exception ``OverflowError`` is raised instead).  For the
         purpose of shift and mask operations, integers are assumed to
         have a binary, 2's complement notation using 32 or more bits,
         and hiding no bits from the user (i.e., all 4294967296
         different bit patterns correspond to different values).

      Long integers
         These represent numbers in an unlimited range, subject to
         available (virtual) memory only.  For the purpose of shift
         and mask operations, a binary representation is assumed, and
         negative numbers are represented in a variant of 2's
         complement which gives the illusion of an infinite string of
         sign bits extending to the left.

      Booleans
         These represent the truth values False and True.  The two
         objects representing the values False and True are the only
         Boolean objects. The Boolean type is a subtype of plain
         integers, and Boolean values behave like the values 0 and 1,
         respectively, in almost all contexts, the exception being
         that when converted to a string, the strings ``"False"`` or
         ``"True"`` are returned, respectively.

      The rules for integer representation are intended to give the
      most meaningful interpretation of shift and mask operations
      involving negative integers and the least surprises when
      switching between the plain and long integer domains.  Any
      operation, if it yields a result in the plain integer domain,
      will yield the same result in the long integer domain or when
      using mixed operands.  The switch between domains is transparent
      to the programmer.

   ``numbers.Real`` (``float``)
      These represent machine-level double precision floating point
      numbers. You are at the mercy of the underlying machine
      architecture (and C or Java implementation) for the accepted
      range and handling of overflow. Python does not support single-
      precision floating point numbers; the savings in processor and
      memory usage that are usually the reason for using these is
      dwarfed by the overhead of using objects in Python, so there is
      no reason to complicate the language with two kinds of floating
      point numbers.

   ``numbers.Complex``
      These represent complex numbers as a pair of machine-level
      double precision floating point numbers.  The same caveats apply
      as for floating point numbers. The real and imaginary parts of a
      complex number ``z`` can be retrieved through the read-only
      attributes ``z.real`` and ``z.imag``.

Sequences
   These represent finite ordered sets indexed by non-negative
   numbers. The built-in function ``len()`` returns the number of
   items of a sequence. When the length of a sequence is *n*, the
   index set contains the numbers 0, 1, ..., *n*-1.  Item *i* of
   sequence *a* is selected by ``a[i]``.

   Sequences also support slicing: ``a[i:j]`` selects all items with
   index *k* such that *i* ``<=`` *k* ``<`` *j*.  When used as an
   expression, a slice is a sequence of the same type.  This implies
   that the index set is renumbered so that it starts at 0.

   Some sequences also support "extended slicing" with a third "step"
   parameter: ``a[i:j:k]`` selects all items of *a* with index *x*
   where ``x = i + n*k``, *n* ``>=`` ``0`` and *i* ``<=`` *x* ``<``
   *j*.

   Sequences are distinguished according to their mutability:

   Immutable sequences
      An object of an immutable sequence type cannot change once it is
      created.  (If the object contains references to other objects,
      these other objects may be mutable and may be changed; however,
      the collection of objects directly referenced by an immutable
      object cannot change.)

      The following types are immutable sequences:

      Strings
         The items of a string are characters.  There is no separate
         character type; a character is represented by a string of one
         item. Characters represent (at least) 8-bit bytes.  The
         built-in functions ``chr()`` and ``ord()`` convert between
         characters and nonnegative integers representing the byte
         values.  Bytes with the values 0-127 usually represent the
         corresponding ASCII values, but the interpretation of values
         is up to the program.  The string data type is also used to
         represent arrays of bytes, e.g., to hold data read from a
         file.

         (On systems whose native character set is not ASCII, strings
         may use EBCDIC in their internal representation, provided the
         functions ``chr()`` and ``ord()`` implement a mapping between
         ASCII and EBCDIC, and string comparison preserves the ASCII
         order. Or perhaps someone can propose a better rule?)

      Unicode
         The items of a Unicode object are Unicode code units.  A
         Unicode code unit is represented by a Unicode object of one
         item and can hold either a 16-bit or 32-bit value
         representing a Unicode ordinal (the maximum value for the
         ordinal is given in ``sys.maxunicode``, and depends on how
         Python is configured at compile time).  Surrogate pairs may
         be present in the Unicode object, and will be reported as two
         separate items.  The built-in functions ``unichr()`` and
         ``ord()`` convert between code units and nonnegative integers
         representing the Unicode ordinals as defined in the Unicode
         Standard 3.0. Conversion from and to other encodings are
         possible through the Unicode method ``encode()`` and the
         built-in function ``unicode()``.

      Tuples
         The items of a tuple are arbitrary Python objects. Tuples of
         two or more items are formed by comma-separated lists of
         expressions.  A tuple of one item (a 'singleton') can be
         formed by affixing a comma to an expression (an expression by
         itself does not create a tuple, since parentheses must be
         usable for grouping of expressions).  An empty tuple can be
         formed by an empty pair of parentheses.

   Mutable sequences
      Mutable sequences can be changed after they are created.  The
      subscription and slicing notations can be used as the target of
      assignment and ``del`` (delete) statements.

      There are currently two intrinsic mutable sequence types:

      Lists
         The items of a list are arbitrary Python objects.  Lists are
         formed by placing a comma-separated list of expressions in
         square brackets. (Note that there are no special cases needed
         to form lists of length 0 or 1.)

      Byte Arrays
         A bytearray object is a mutable array. They are created by
         the built-in ``bytearray()`` constructor.  Aside from being
         mutable (and hence unhashable), byte arrays otherwise provide
         the same interface and functionality as immutable bytes
         objects.

      The extension module ``array`` provides an additional example of
      a mutable sequence type.

Set types
   These represent unordered, finite sets of unique, immutable
   objects. As such, they cannot be indexed by any subscript. However,
   they can be iterated over, and the built-in function ``len()``
   returns the number of items in a set. Common uses for sets are fast
   membership testing, removing duplicates from a sequence, and
   computing mathematical operations such as intersection, union,
   difference, and symmetric difference.

   For set elements, the same immutability rules apply as for
   dictionary keys. Note that numeric types obey the normal rules for
   numeric comparison: if two numbers compare equal (e.g., ``1`` and
   ``1.0``), only one of them can be contained in a set.

   There are currently two intrinsic set types:

   Sets
      These represent a mutable set. They are created by the built-in
      ``set()`` constructor and can be modified afterwards by several
      methods, such as ``add()``.

   Frozen sets
      These represent an immutable set.  They are created by the
      built-in ``frozenset()`` constructor.  As a frozenset is
      immutable and *hashable*, it can be used again as an element of
      another set, or as a dictionary key.

Mappings
   These represent finite sets of objects indexed by arbitrary index
   sets. The subscript notation ``a[k]`` selects the item indexed by
   ``k`` from the mapping ``a``; this can be used in expressions and
   as the target of assignments or ``del`` statements. The built-in
   function ``len()`` returns the number of items in a mapping.

   There is currently a single intrinsic mapping type:

   Dictionaries
      These represent finite sets of objects indexed by nearly
      arbitrary values.  The only types of values not acceptable as
      keys are values containing lists or dictionaries or other
      mutable types that are compared by value rather than by object
      identity, the reason being that the efficient implementation of
      dictionaries requires a key's hash value to remain constant.
      Numeric types used for keys obey the normal rules for numeric
      comparison: if two numbers compare equal (e.g., ``1`` and
      ``1.0``) then they can be used interchangeably to index the same
      dictionary entry.

      Dictionaries are mutable; they can be created by the ``{...}``
      notation (see section *Dictionary displays*).

      The extension modules ``dbm``, ``gdbm``, and ``bsddb`` provide
      additional examples of mapping types.

Callable types
   These are the types to which the function call operation (see
   section *Calls*) can be applied:

   User-defined functions
      A user-defined function object is created by a function
      definition (see section *Function definitions*).  It should be
      called with an argument list containing the same number of items
      as the function's formal parameter list.

      Special attributes:

      +-------------------------+---------------------------------+-------------+
      | Attribute               | Meaning                         |             |
      +=========================+=================================+=============+
      | ``func_doc``            | The function's documentation    | Writable    |
      |                         | string, or ``None`` if          |             |
      |                         | unavailable                     |             |
      +-------------------------+---------------------------------+-------------+
      | ``__doc__``             | Another way of spelling         | Writable    |
      |                         | ``func_doc``                    |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_name``           | The function's name             | Writable    |
      +-------------------------+---------------------------------+-------------+
      | ``__name__``            | Another way of spelling         | Writable    |
      |                         | ``func_name``                   |             |
      +-------------------------+---------------------------------+-------------+
      | ``__module__``          | The name of the module the      | Writable    |
      |                         | function was defined in, or     |             |
      |                         | ``None`` if unavailable.        |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_defaults``       | A tuple containing default      | Writable    |
      |                         | argument values for those       |             |
      |                         | arguments that have defaults,   |             |
      |                         | or ``None`` if no arguments     |             |
      |                         | have a default value            |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_code``           | The code object representing    | Writable    |
      |                         | the compiled function body.     |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_globals``        | A reference to the dictionary   | Read-only   |
      |                         | that holds the function's       |             |
      |                         | global variables --- the global |             |
      |                         | namespace of the module in      |             |
      |                         | which the function was defined. |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_dict``           | The namespace supporting        | Writable    |
      |                         | arbitrary function attributes.  |             |
      +-------------------------+---------------------------------+-------------+
      | ``func_closure``        | ``None`` or a tuple of cells    | Read-only   |
      |                         | that contain bindings for the   |             |
      |                         | function's free variables.      |             |
      +-------------------------+---------------------------------+-------------+

      Most of the attributes labelled "Writable" check the type of the
      assigned value.

      Changed in version 2.4: ``func_name`` is now writable.

      Function objects also support getting and setting arbitrary
      attributes, which can be used, for example, to attach metadata
      to functions.  Regular attribute dot-notation is used to get and
      set such attributes. *Note that the current implementation only
      supports function attributes on user-defined functions. Function
      attributes on built-in functions may be supported in the
      future.*

      Additional information about a function's definition can be
      retrieved from its code object; see the description of internal
      types below.

   User-defined methods
      A user-defined method object combines a class, a class instance
      (or ``None``) and any callable object (normally a user-defined
      function).

      Special read-only attributes: ``im_self`` is the class instance
      object, ``im_func`` is the function object; ``im_class`` is the
      class of ``im_self`` for bound methods or the class that asked
      for the method for unbound methods; ``__doc__`` is the method's
      documentation (same as ``im_func.__doc__``); ``__name__`` is the
      method name (same as ``im_func.__name__``); ``__module__`` is
      the name of the module the method was defined in, or ``None`` if
      unavailable.

      Changed in version 2.2: ``im_self`` used to refer to the class
      that defined the method.

      Changed in version 2.6: For 3.0 forward-compatibility,
      ``im_func`` is also available as ``__func__``, and ``im_self``
      as ``__self__``.

      Methods also support accessing (but not setting) the arbitrary
      function attributes on the underlying function object.

      User-defined method objects may be created when getting an
      attribute of a class (perhaps via an instance of that class), if
      that attribute is a user-defined function object, an unbound
      user-defined method object, or a class method object. When the
      attribute is a user-defined method object, a new method object
      is only created if the class from which it is being retrieved is
      the same as, or a derived class of, the class stored in the
      original method object; otherwise, the original method object is
      used as it is.

      When a user-defined method object is created by retrieving a
      user-defined function object from a class, its ``im_self``
      attribute is ``None`` and the method object is said to be
      unbound. When one is created by retrieving a user-defined
      function object from a class via one of its instances, its
      ``im_self`` attribute is the instance, and the method object is
      said to be bound. In either case, the new method's ``im_class``
      attribute is the class from which the retrieval takes place, and
      its ``im_func`` attribute is the original function object.

      When a user-defined method object is created by retrieving
      another method object from a class or instance, the behaviour is
      the same as for a function object, except that the ``im_func``
      attribute of the new instance is not the original method object
      but its ``im_func`` attribute.

      When a user-defined method object is created by retrieving a
      class method object from a class or instance, its ``im_self``
      attribute is the class itself (the same as the ``im_class``
      attribute), and its ``im_func`` attribute is the function object
      underlying the class method.

      When an unbound user-defined method object is called, the
      underlying function (``im_func``) is called, with the
      restriction that the first argument must be an instance of the
      proper class (``im_class``) or of a derived class thereof.

      When a bound user-defined method object is called, the
      underlying function (``im_func``) is called, inserting the class
      instance (``im_self``) in front of the argument list.  For
      instance, when ``C`` is a class which contains a definition for
      a function ``f()``, and ``x`` is an instance of ``C``, calling
      ``x.f(1)`` is equivalent to calling ``C.f(x, 1)``.

      When a user-defined method object is derived from a class method
      object, the "class instance" stored in ``im_self`` will actually
      be the class itself, so that calling either ``x.f(1)`` or
      ``C.f(1)`` is equivalent to calling ``f(C,1)`` where ``f`` is
      the underlying function.

      Note that the transformation from function object to (unbound or
      bound) method object happens each time the attribute is
      retrieved from the class or instance. In some cases, a fruitful
      optimization is to assign the attribute to a local variable and
      call that local variable. Also notice that this transformation
      only happens for user-defined functions; other callable objects
      (and all non-callable objects) are retrieved without
      transformation.  It is also important to note that user-defined
      functions which are attributes of a class instance are not
      converted to bound methods; this *only* happens when the
      function is an attribute of the class.

   Generator functions
      A function or method which uses the ``yield`` statement (see
      section *The yield statement*) is called a *generator function*.
      Such a function, when called, always returns an iterator object
      which can be used to execute the body of the function:  calling
      the iterator's ``next()`` method will cause the function to
      execute until it provides a value using the ``yield`` statement.
      When the function executes a ``return`` statement or falls off
      the end, a ``StopIteration`` exception is raised and the
      iterator will have reached the end of the set of values to be
      returned.

   Built-in functions
      A built-in function object is a wrapper around a C function.
      Examples of built-in functions are ``len()`` and ``math.sin()``
      (``math`` is a standard built-in module). The number and type of
      the arguments are determined by the C function. Special read-
      only attributes: ``__doc__`` is the function's documentation
      string, or ``None`` if unavailable; ``__name__`` is the
      function's name; ``__self__`` is set to ``None`` (but see the
      next item); ``__module__`` is the name of the module the
      function was defined in or ``None`` if unavailable.

   Built-in methods
      This is really a different disguise of a built-in function, this
      time containing an object passed to the C function as an
      implicit extra argument.  An example of a built-in method is
      ``alist.append()``, assuming *alist* is a list object. In this
      case, the special read-only attribute ``__self__`` is set to the
      object denoted by *list*.

   Class Types
      Class types, or "new-style classes," are callable.  These
      objects normally act as factories for new instances of
      themselves, but variations are possible for class types that
      override ``__new__()``.  The arguments of the call are passed to
      ``__new__()`` and, in the typical case, to ``__init__()`` to
      initialize the new instance.

   Classic Classes
      Class objects are described below.  When a class object is
      called, a new class instance (also described below) is created
      and returned.  This implies a call to the class's ``__init__()``
      method if it has one.  Any arguments are passed on to the
      ``__init__()`` method.  If there is no ``__init__()`` method,
      the class must be called without arguments.

   Class instances
      Class instances are described below.  Class instances are
      callable only when the class has a ``__call__()`` method;
      ``x(arguments)`` is a shorthand for ``x.__call__(arguments)``.

Modules
   Modules are imported by the ``import`` statement (see section *The
   import statement*). A module object has a namespace implemented by
   a dictionary object (this is the dictionary referenced by the
   func_globals attribute of functions defined in the module).
   Attribute references are translated to lookups in this dictionary,
   e.g., ``m.x`` is equivalent to ``m.__dict__["x"]``. A module object
   does not contain the code object used to initialize the module
   (since it isn't needed once the initialization is done).

   Attribute assignment updates the module's namespace dictionary,
   e.g., ``m.x = 1`` is equivalent to ``m.__dict__["x"] = 1``.

   Special read-only attribute: ``__dict__`` is the module's namespace
   as a dictionary object.

   Predefined (writable) attributes: ``__name__`` is the module's
   name; ``__doc__`` is the module's documentation string, or ``None``
   if unavailable; ``__file__`` is the pathname of the file from which
   the module was loaded, if it was loaded from a file. The
   ``__file__`` attribute is not present for C modules that are
   statically linked into the interpreter; for extension modules
   loaded dynamically from a shared library, it is the pathname of the
   shared library file.

Classes
   Both class types (new-style classes) and class objects (old-
   style/classic classes) are typically created by class definitions
   (see section *Class definitions*).  A class has a namespace
   implemented by a dictionary object. Class attribute references are
   translated to lookups in this dictionary, e.g., ``C.x`` is
   translated to ``C.__dict__["x"]`` (although for new-style classes
   in particular there are a number of hooks which allow for other
   means of locating attributes). When the attribute name is not found
   there, the attribute search continues in the base classes.  For
   old-style classes, the search is depth-first, left-to-right in the
   order of occurrence in the base class list. New-style classes use
   the more complex C3 method resolution order which behaves correctly
   even in the presence of 'diamond' inheritance structures where
   there are multiple inheritance paths leading back to a common
   ancestor. Additional details on the C3 MRO used by new-style
   classes can be found in the documentation accompanying the 2.3
   release at http://www.python.org/download/releases/2.3/mro/.

   When a class attribute reference (for class ``C``, say) would yield
   a user-defined function object or an unbound user-defined method
   object whose associated class is either ``C`` or one of its base
   classes, it is transformed into an unbound user-defined method
   object whose ``im_class`` attribute is ``C``. When it would yield a
   class method object, it is transformed into a bound user-defined
   method object whose ``im_class`` and ``im_self`` attributes are
   both ``C``.  When it would yield a static method object, it is
   transformed into the object wrapped by the static method object.
   See section *Implementing Descriptors* for another way in which
   attributes retrieved from a class may differ from those actually
   contained in its ``__dict__`` (note that only new-style classes
   support descriptors).

   Class attribute assignments update the class's dictionary, never
   the dictionary of a base class.

   A class object can be called (see above) to yield a class instance
   (see below).

   Special attributes: ``__name__`` is the class name; ``__module__``
   is the module name in which the class was defined; ``__dict__`` is
   the dictionary containing the class's namespace; ``__bases__`` is a
   tuple (possibly empty or a singleton) containing the base classes,
   in the order of their occurrence in the base class list;
   ``__doc__`` is the class's documentation string, or None if
   undefined.

Class instances
   A class instance is created by calling a class object (see above).
   A class instance has a namespace implemented as a dictionary which
   is the first place in which attribute references are searched.
   When an attribute is not found there, and the instance's class has
   an attribute by that name, the search continues with the class
   attributes.  If a class attribute is found that is a user-defined
   function object or an unbound user-defined method object whose
   associated class is the class (call it ``C``) of the instance for
   which the attribute reference was initiated or one of its bases, it
   is transformed into a bound user-defined method object whose
   ``im_class`` attribute is ``C`` and whose ``im_self`` attribute is
   the instance. Static method and class method objects are also
   transformed, as if they had been retrieved from class ``C``; see
   above under "Classes". See section *Implementing Descriptors* for
   another way in which attributes of a class retrieved via its
   instances may differ from the objects actually stored in the
   class's ``__dict__``. If no class attribute is found, and the
   object's class has a ``__getattr__()`` method, that is called to
   satisfy the lookup.

   Attribute assignments and deletions update the instance's
   dictionary, never a class's dictionary.  If the class has a
   ``__setattr__()`` or ``__delattr__()`` method, this is called
   instead of updating the instance dictionary directly.

   Class instances can pretend to be numbers, sequences, or mappings
   if they have methods with certain special names.  See section
   *Special method names*.

   Special attributes: ``__dict__`` is the attribute dictionary;
   ``__class__`` is the instance's class.

Files
   A file object represents an open file.  File objects are created by
   the ``open()`` built-in function, and also by ``os.popen()``,
   ``os.fdopen()``, and the ``makefile()`` method of socket objects
   (and perhaps by other functions or methods provided by extension
   modules).  The objects ``sys.stdin``, ``sys.stdout`` and
   ``sys.stderr`` are initialized to file objects corresponding to the
   interpreter's standard input, output and error streams.  See *File
   Objects* for complete documentation of file objects.

Internal types
   A few types used internally by the interpreter are exposed to the
   user. Their definitions may change with future versions of the
   interpreter, but they are mentioned here for completeness.

   Code objects
      Code objects represent *byte-compiled* executable Python code,
      or *bytecode*. The difference between a code object and a
      function object is that the function object contains an explicit
      reference to the function's globals (the module in which it was
      defined), while a code object contains no context; also the
      default argument values are stored in the function object, not
      in the code object (because they represent values calculated at
      run-time).  Unlike function objects, code objects are immutable
      and contain no references (directly or indirectly) to mutable
      objects.

      Special read-only attributes: ``co_name`` gives the function
      name; ``co_argcount`` is the number of positional arguments
      (including arguments with default values); ``co_nlocals`` is the
      number of local variables used by the function (including
      arguments); ``co_varnames`` is a tuple containing the names of
      the local variables (starting with the argument names);
      ``co_cellvars`` is a tuple containing the names of local
      variables that are referenced by nested functions;
      ``co_freevars`` is a tuple containing the names of free
      variables; ``co_code`` is a string representing the sequence of
      bytecode instructions; ``co_consts`` is a tuple containing the
      literals used by the bytecode; ``co_names`` is a tuple
      containing the names used by the bytecode; ``co_filename`` is
      the filename from which the code was compiled;
      ``co_firstlineno`` is the first line number of the function;
      ``co_lnotab`` is a string encoding the mapping from bytecode
      offsets to line numbers (for details see the source code of the
      interpreter); ``co_stacksize`` is the required stack size
      (including local variables); ``co_flags`` is an integer encoding
      a number of flags for the interpreter.

      The following flag bits are defined for ``co_flags``: bit
      ``0x04`` is set if the function uses the ``*arguments`` syntax
      to accept an arbitrary number of positional arguments; bit
      ``0x08`` is set if the function uses the ``**keywords`` syntax
      to accept arbitrary keyword arguments; bit ``0x20`` is set if
      the function is a generator.

      Future feature declarations (``from __future__ import
      division``) also use bits in ``co_flags`` to indicate whether a
      code object was compiled with a particular feature enabled: bit
      ``0x2000`` is set if the function was compiled with future
      division enabled; bits ``0x10`` and ``0x1000`` were used in
      earlier versions of Python.

      Other bits in ``co_flags`` are reserved for internal use.

      If a code object represents a function, the first item in
      ``co_consts`` is the documentation string of the function, or
      ``None`` if undefined.

   Frame objects
      Frame objects represent execution frames.  They may occur in
      traceback objects (see below).

      Special read-only attributes: ``f_back`` is to the previous
      stack frame (towards the caller), or ``None`` if this is the
      bottom stack frame; ``f_code`` is the code object being executed
      in this frame; ``f_locals`` is the dictionary used to look up
      local variables; ``f_globals`` is used for global variables;
      ``f_builtins`` is used for built-in (intrinsic) names;
      ``f_restricted`` is a flag indicating whether the function is
      executing in restricted execution mode; ``f_lasti`` gives the
      precise instruction (this is an index into the bytecode string
      of the code object).

      Special writable attributes: ``f_trace``, if not ``None``, is a
      function called at the start of each source code line (this is
      used by the debugger); ``f_exc_type``, ``f_exc_value``,
      ``f_exc_traceback`` represent the last exception raised in the
      parent frame provided another exception was ever raised in the
      current frame (in all other cases they are None); ``f_lineno``
      is the current line number of the frame --- writing to this from
      within a trace function jumps to the given line (only for the
      bottom-most frame).  A debugger can implement a Jump command
      (aka Set Next Statement) by writing to f_lineno.

   Traceback objects
      Traceback objects represent a stack trace of an exception.  A
      traceback object is created when an exception occurs.  When the
      search for an exception handler unwinds the execution stack, at
      each unwound level a traceback object is inserted in front of
      the current traceback.  When an exception handler is entered,
      the stack trace is made available to the program. (See section
      *The try statement*.) It is accessible as ``sys.exc_traceback``,
      and also as the third item of the tuple returned by
      ``sys.exc_info()``.  The latter is the preferred interface,
      since it works correctly when the program is using multiple
      threads. When the program contains no suitable handler, the
      stack trace is written (nicely formatted) to the standard error
      stream; if the interpreter is interactive, it is also made
      available to the user as ``sys.last_traceback``.

      Special read-only attributes: ``tb_next`` is the next level in
      the stack trace (towards the frame where the exception
      occurred), or ``None`` if there is no next level; ``tb_frame``
      points to the execution frame of the current level;
      ``tb_lineno`` gives the line number where the exception
      occurred; ``tb_lasti`` indicates the precise instruction.  The
      line number and last instruction in the traceback may differ
      from the line number of its frame object if the exception
      occurred in a ``try`` statement with no matching except clause
      or with a finally clause.

   Slice objects
      Slice objects are used to represent slices when *extended slice
      syntax* is used. This is a slice using two colons, or multiple
      slices or ellipses separated by commas, e.g., ``a[i:j:step]``,
      ``a[i:j, k:l]``, or ``a[..., i:j]``.  They are also created by
      the built-in ``slice()`` function.

      Special read-only attributes: ``start`` is the lower bound;
      ``stop`` is the upper bound; ``step`` is the step value; each is
      ``None`` if omitted. These attributes can have any type.

      Slice objects support one method:

      slice.indices(self, length)

         This method takes a single integer argument *length* and
         computes information about the extended slice that the slice
         object would describe if applied to a sequence of *length*
         items.  It returns a tuple of three integers; respectively
         these are the *start* and *stop* indices and the *step* or
         stride length of the slice. Missing or out-of-bounds indices
         are handled in a manner consistent with regular slices.

         New in version 2.3.

   Static method objects
      Static method objects provide a way of defeating the
      transformation of function objects to method objects described
      above. A static method object is a wrapper around any other
      object, usually a user-defined method object. When a static
      method object is retrieved from a class or a class instance, the
      object actually returned is the wrapped object, which is not
      subject to any further transformation. Static method objects are
      not themselves callable, although the objects they wrap usually
      are. Static method objects are created by the built-in
      ``staticmethod()`` constructor.

   Class method objects
      A class method object, like a static method object, is a wrapper
      around another object that alters the way in which that object
      is retrieved from classes and class instances. The behaviour of
      class method objects upon such retrieval is described above,
      under "User-defined methods". Class method objects are created
      by the built-in ``classmethod()`` constructor.


New-style and classic classes
=============================

Classes and instances come in two flavors: old-style (or classic) and
new-style.

Up to Python 2.1, old-style classes were the only flavour available to
the user. The concept of (old-style) class is unrelated to the concept
of type: if *x* is an instance of an old-style class, then
``x.__class__`` designates the class of *x*, but ``type(x)`` is always
``<type 'instance'>``.  This reflects the fact that all old-style
instances, independently of their class, are implemented with a single
built-in type, called ``instance``.

New-style classes were introduced in Python 2.2 to unify classes and
types.  A new-style class is neither more nor less than a user-defined
type.  If *x* is an instance of a new-style class, then ``type(x)`` is
typically the same as ``x.__class__`` (although this is not guaranteed
- a new-style class instance is permitted to override the value
returned for ``x.__class__``).

The major motivation for introducing new-style classes is to provide a
unified object model with a full meta-model.  It also has a number of
practical benefits, like the ability to subclass most built-in types,
or the introduction of "descriptors", which enable computed
properties.

For compatibility reasons, classes are still old-style by default.
New-style classes are created by specifying another new-style class
(i.e. a type) as a parent class, or the "top-level type" ``object`` if
no other parent is needed.  The behaviour of new-style classes differs
from that of old-style classes in a number of important details in
addition to what ``type()`` returns.  Some of these changes are
fundamental to the new object model, like the way special methods are
invoked.  Others are "fixes" that could not be implemented before for
compatibility concerns, like the method resolution order in case of
multiple inheritance.

While this manual aims to provide comprehensive coverage of Python's
class mechanics, it may still be lacking in some areas when it comes
to its coverage of new-style classes. Please see
http://www.python.org/doc/newstyle/ for sources of additional
information.

Old-style classes are removed in Python 3.0, leaving only the
semantics of new-style classes.


Special method names
====================

A class can implement certain operations that are invoked by special
syntax (such as arithmetic operations or subscripting and slicing) by
defining methods with special names. This is Python's approach to
*operator overloading*, allowing classes to define their own behavior
with respect to language operators.  For instance, if a class defines
a method named ``__getitem__()``, and ``x`` is an instance of this
class, then ``x[i]`` is roughly equivalent to ``x.__getitem__(i)`` for
old-style classes and ``type(x).__getitem__(x, i)`` for new-style
classes.  Except where mentioned, attempts to execute an operation
raise an exception when no appropriate method is defined (typically
``AttributeError`` or ``TypeError``).

When implementing a class that emulates any built-in type, it is
important that the emulation only be implemented to the degree that it
makes sense for the object being modelled.  For example, some
sequences may work well with retrieval of individual elements, but
extracting a slice may not make sense.  (One example of this is the
``NodeList`` interface in the W3C's Document Object Model.)


Basic customization
-------------------

object.__new__(cls[, ...])

   Called to create a new instance of class *cls*.  ``__new__()`` is a
   static method (special-cased so you need not declare it as such)
   that takes the class of which an instance was requested as its
   first argument.  The remaining arguments are those passed to the
   object constructor expression (the call to the class).  The return
   value of ``__new__()`` should be the new object instance (usually
   an instance of *cls*).

   Typical implementations create a new instance of the class by
   invoking the superclass's ``__new__()`` method using
   ``super(currentclass, cls).__new__(cls[, ...])`` with appropriate
   arguments and then modifying the newly-created instance as
   necessary before returning it.

   If ``__new__()`` returns an instance of *cls*, then the new
   instance's ``__init__()`` method will be invoked like
   ``__init__(self[, ...])``, where *self* is the new instance and the
   remaining arguments are the same as were passed to ``__new__()``.

   If ``__new__()`` does not return an instance of *cls*, then the new
   instance's ``__init__()`` method will not be invoked.

   ``__new__()`` is intended mainly to allow subclasses of immutable
   types (like int, str, or tuple) to customize instance creation.  It
   is also commonly overridden in custom metaclasses in order to
   customize class creation.

object.__init__(self[, ...])

   Called when the instance is created.  The arguments are those
   passed to the class constructor expression.  If a base class has an
   ``__init__()`` method, the derived class's ``__init__()`` method,
   if any, must explicitly call it to ensure proper initialization of
   the base class part of the instance; for example:
   ``BaseClass.__init__(self, [args...])``.  As a special constraint
   on constructors, no value may be returned; doing so will cause a
   ``TypeError`` to be raised at runtime.

object.__del__(self)

   Called when the instance is about to be destroyed.  This is also
   called a destructor.  If a base class has a ``__del__()`` method,
   the derived class's ``__del__()`` method, if any, must explicitly
   call it to ensure proper deletion of the base class part of the
   instance.  Note that it is possible (though not recommended!) for
   the ``__del__()`` method to postpone destruction of the instance by
   creating a new reference to it.  It may then be called at a later
   time when this new reference is deleted.  It is not guaranteed that
   ``__del__()`` methods are called for objects that still exist when
   the interpreter exits.

   Note: ``del x`` doesn't directly call ``x.__del__()`` --- the former
     decrements the reference count for ``x`` by one, and the latter
     is only called when ``x``'s reference count reaches zero.  Some
     common situations that may prevent the reference count of an
     object from going to zero include: circular references between
     objects (e.g., a doubly-linked list or a tree data structure with
     parent and child pointers); a reference to the object on the
     stack frame of a function that caught an exception (the traceback
     stored in ``sys.exc_traceback`` keeps the stack frame alive); or
     a reference to the object on the stack frame that raised an
     unhandled exception in interactive mode (the traceback stored in
     ``sys.last_traceback`` keeps the stack frame alive).  The first
     situation can only be remedied by explicitly breaking the cycles;
     the latter two situations can be resolved by storing ``None`` in
     ``sys.exc_traceback`` or ``sys.last_traceback``.  Circular
     references which are garbage are detected when the option cycle
     detector is enabled (it's on by default), but can only be cleaned
     up if there are no Python-level ``__del__()`` methods involved.
     Refer to the documentation for the ``gc`` module for more
     information about how ``__del__()`` methods are handled by the
     cycle detector, particularly the description of the ``garbage``
     value.

   Warning: Due to the precarious circumstances under which ``__del__()``
     methods are invoked, exceptions that occur during their execution
     are ignored, and a warning is printed to ``sys.stderr`` instead.
     Also, when ``__del__()`` is invoked in response to a module being
     deleted (e.g., when execution of the program is done), other
     globals referenced by the ``__del__()`` method may already have
     been deleted or in the process of being torn down (e.g. the
     import machinery shutting down).  For this reason, ``__del__()``
     methods should do the absolute minimum needed to maintain
     external invariants.  Starting with version 1.5, Python
     guarantees that globals whose name begins with a single
     underscore are deleted from their module before other globals are
     deleted; if no other references to such globals exist, this may
     help in assuring that imported modules are still available at the
     time when the ``__del__()`` method is called.

object.__repr__(self)

   Called by the ``repr()`` built-in function and by string
   conversions (reverse quotes) to compute the "official" string
   representation of an object.  If at all possible, this should look
   like a valid Python expression that could be used to recreate an
   object with the same value (given an appropriate environment).  If
   this is not possible, a string of the form ``<...some useful
   description...>`` should be returned.  The return value must be a
   string object. If a class defines ``__repr__()`` but not
   ``__str__()``, then ``__repr__()`` is also used when an "informal"
   string representation of instances of that class is required.

   This is typically used for debugging, so it is important that the
   representation is information-rich and unambiguous.

object.__str__(self)

   Called by the ``str()`` built-in function and by the ``print``
   statement to compute the "informal" string representation of an
   object.  This differs from ``__repr__()`` in that it does not have
   to be a valid Python expression: a more convenient or concise
   representation may be used instead. The return value must be a
   string object.

object.__lt__(self, other)
object.__le__(self, other)
object.__eq__(self, other)
object.__ne__(self, other)
object.__gt__(self, other)
object.__ge__(self, other)

   New in version 2.1.

   These are the so-called "rich comparison" methods, and are called
   for comparison operators in preference to ``__cmp__()`` below. The
   correspondence between operator symbols and method names is as
   follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls
   ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` and
   ``x<>y`` call ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and
   ``x>=y`` calls ``x.__ge__(y)``.

   A rich comparison method may return the singleton
   ``NotImplemented`` if it does not implement the operation for a
   given pair of arguments. By convention, ``False`` and ``True`` are
   returned for a successful comparison. However, these methods can
   return any value, so if the comparison operator is used in a
   Boolean context (e.g., in the condition of an ``if`` statement),
   Python will call ``bool()`` on the value to determine if the result
   is true or false.

   There are no implied relationships among the comparison operators.
   The truth of ``x==y`` does not imply that ``x!=y`` is false.
   Accordingly, when defining ``__eq__()``, one should also define
   ``__ne__()`` so that the operators will behave as expected.  See
   the paragraph on ``__hash__()`` for some important notes on
   creating *hashable* objects which support custom comparison
   operations and are usable as dictionary keys.

   There are no swapped-argument versions of these methods (to be used
   when the left argument does not support the operation but the right
   argument does); rather, ``__lt__()`` and ``__gt__()`` are each
   other's reflection, ``__le__()`` and ``__ge__()`` are each other's
   reflection, and ``__eq__()`` and ``__ne__()`` are their own
   reflection.

   Arguments to rich comparison methods are never coerced.

   To automatically generate ordering operations from a single root
   operation, see the Total Ordering recipe in the ASPN cookbook.

object.__cmp__(self, other)

   Called by comparison operations if rich comparison (see above) is
   not defined.  Should return a negative integer if ``self < other``,
   zero if ``self == other``, a positive integer if ``self > other``.
   If no ``__cmp__()``, ``__eq__()`` or ``__ne__()`` operation is
   defined, class instances are compared by object identity
   ("address").  See also the description of ``__hash__()`` for some
   important notes on creating *hashable* objects which support custom
   comparison operations and are usable as dictionary keys. (Note: the
   restriction that exceptions are not propagated by ``__cmp__()`` has
   been removed since Python 1.5.)

object.__rcmp__(self, other)

   Changed in version 2.1: No longer supported.

object.__hash__(self)

   Called by built-in function ``hash()`` and for operations on
   members of hashed collections including ``set``, ``frozenset``, and
   ``dict``.  ``__hash__()`` should return an integer.  The only
   required property is that objects which compare equal have the same
   hash value; it is advised to somehow mix together (e.g. using
   exclusive or) the hash values for the components of the object that
   also play a part in comparison of objects.

   If a class does not define a ``__cmp__()`` or ``__eq__()`` method
   it should not define a ``__hash__()`` operation either; if it
   defines ``__cmp__()`` or ``__eq__()`` but not ``__hash__()``, its
   instances will not be usable in hashed collections.  If a class
   defines mutable objects and implements a ``__cmp__()`` or
   ``__eq__()`` method, it should not implement ``__hash__()``, since
   hashable collection implementations require that a object's hash
   value is immutable (if the object's hash value changes, it will be
   in the wrong hash bucket).

   User-defined classes have ``__cmp__()`` and ``__hash__()`` methods
   by default; with them, all objects compare unequal (except with
   themselves) and ``x.__hash__()`` returns ``id(x)``.

   Classes which inherit a ``__hash__()`` method from a parent class
   but change the meaning of ``__cmp__()`` or ``__eq__()`` such that
   the hash value returned is no longer appropriate (e.g. by switching
   to a value-based concept of equality instead of the default
   identity based equality) can explicitly flag themselves as being
   unhashable by setting ``__hash__ = None`` in the class definition.
   Doing so means that not only will instances of the class raise an
   appropriate ``TypeError`` when a program attempts to retrieve their
   hash value, but they will also be correctly identified as
   unhashable when checking ``isinstance(obj, collections.Hashable)``
   (unlike classes which define their own ``__hash__()`` to explicitly
   raise ``TypeError``).

   Changed in version 2.5: ``__hash__()`` may now also return a long
   integer object; the 32-bit integer is then derived from the hash of
   that object.

   Changed in version 2.6: ``__hash__`` may now be set to ``None`` to
   explicitly flag instances of a class as unhashable.

object.__nonzero__(self)

   Called to implement truth value testing and the built-in operation
   ``bool()``; should return ``False`` or ``True``, or their integer
   equivalents ``0`` or ``1``.  When this method is not defined,
   ``__len__()`` is called, if it is defined, and the object is
   considered true if its result is nonzero. If a class defines
   neither ``__len__()`` nor ``__nonzero__()``, all its instances are
   considered true.

object.__unicode__(self)

   Called to implement ``unicode()`` built-in; should return a Unicode
   object. When this method is not defined, string conversion is
   attempted, and the result of string conversion is converted to
   Unicode using the system default encoding.


Customizing attribute access
----------------------------

The following methods can be defined to customize the meaning of
attribute access (use of, assignment to, or deletion of ``x.name``)
for class instances.

object.__getattr__(self, name)

   Called when an attribute lookup has not found the attribute in the
   usual places (i.e. it is not an instance attribute nor is it found
   in the class tree for ``self``).  ``name`` is the attribute name.
   This method should return the (computed) attribute value or raise
   an ``AttributeError`` exception.

   Note that if the attribute is found through the normal mechanism,
   ``__getattr__()`` is not called.  (This is an intentional asymmetry
   between ``__getattr__()`` and ``__setattr__()``.) This is done both
   for efficiency reasons and because otherwise ``__getattr__()``
   would have no way to access other attributes of the instance.  Note
   that at least for instance variables, you can fake total control by
   not inserting any values in the instance attribute dictionary (but
   instead inserting them in another object).  See the
   ``__getattribute__()`` method below for a way to actually get total
   control in new-style classes.

object.__setattr__(self, name, value)

   Called when an attribute assignment is attempted.  This is called
   instead of the normal mechanism (i.e. store the value in the
   instance dictionary).  *name* is the attribute name, *value* is the
   value to be assigned to it.

   If ``__setattr__()`` wants to assign to an instance attribute, it
   should not simply execute ``self.name = value`` --- this would
   cause a recursive call to itself.  Instead, it should insert the
   value in the dictionary of instance attributes, e.g.,
   ``self.__dict__[name] = value``.  For new-style classes, rather
   than accessing the instance dictionary, it should call the base
   class method with the same name, for example,
   ``object.__setattr__(self, name, value)``.

object.__delattr__(self, name)

   Like ``__setattr__()`` but for attribute deletion instead of
   assignment.  This should only be implemented if ``del obj.name`` is
   meaningful for the object.


More attribute access for new-style classes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following methods only apply to new-style classes.

object.__getattribute__(self, name)

   Called unconditionally to implement attribute accesses for
   instances of the class. If the class also defines
   ``__getattr__()``, the latter will not be called unless
   ``__getattribute__()`` either calls it explicitly or raises an
   ``AttributeError``. This method should return the (computed)
   attribute value or raise an ``AttributeError`` exception. In order
   to avoid infinite recursion in this method, its implementation
   should always call the base class method with the same name to
   access any attributes it needs, for example,
   ``object.__getattribute__(self, name)``.

   Note: This method may still be bypassed when looking up special methods
     as the result of implicit invocation via language syntax or
     built-in functions. See *Special method lookup for new-style
     classes*.


Implementing Descriptors
~~~~~~~~~~~~~~~~~~~~~~~~

The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in the
class dictionary of another new-style class, known as the *owner*
class. In the examples below, "the attribute" refers to the attribute
whose name is the key of the property in the owner class'
``__dict__``.  Descriptors can only be implemented as new-style
classes themselves.

object.__get__(self, instance, owner)

   Called to get the attribute of the owner class (class attribute
   access) or of an instance of that class (instance attribute
   access). *owner* is always the owner class, while *instance* is the
   instance that the attribute was accessed through, or ``None`` when
   the attribute is accessed through the *owner*.  This method should
   return the (computed) attribute value or raise an
   ``AttributeError`` exception.

object.__set__(self, instance, value)

   Called to set the attribute on an instance *instance* of the owner
   class to a new value, *value*.

object.__delete__(self, instance)

   Called to delete the attribute on an instance *instance* of the
   owner class.


Invoking Descriptors
~~~~~~~~~~~~~~~~~~~~

In general, a descriptor is an object attribute with "binding
behavior", one whose attribute access has been overridden by methods
in the descriptor protocol:  ``__get__()``, ``__set__()``, and
``__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.

However, 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 and
how they were called.  Note that descriptors are only invoked for new
style objects or classes (ones that subclass ``object()`` or
``type()``).

The starting point for descriptor invocation is a binding, ``a.x``.
How the arguments are assembled depends on ``a``:

Direct Call
   The simplest and least common call is when user code directly
   invokes a descriptor method:    ``x.__get__(a)``.

Instance Binding
   If binding to a new-style object instance, ``a.x`` is transformed
   into the call: ``type(a).__dict__['x'].__get__(a, type(a))``.

Class Binding
   If binding to a new-style class, ``A.x`` is transformed into the
   call: ``A.__dict__['x'].__get__(None, A)``.

Super Binding
   If ``a`` is an instance of ``super``, then the binding ``super(B,
   obj).m()`` searches ``obj.__class__.__mro__`` for the base class
   ``A`` immediately preceding ``B`` and then invokes the descriptor
   with the call: ``A.__dict__['m'].__get__(obj, A)``.

For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined.  Normally, data
descriptors define both ``__get__()`` and ``__set__()``, while non-
data descriptors have just the ``__get__()`` method.  Data descriptors
always override a redefinition in an instance dictionary.  In
contrast, non-data descriptors can be overridden by instances. [2]

Python methods (including ``staticmethod()`` and ``classmethod()``)
are implemented as non-data descriptors.  Accordingly, instances can
redefine and override methods.  This allows individual instances to
acquire behaviors that differ from other instances of the same class.

The ``property()`` function is implemented as a data descriptor.
Accordingly, instances cannot override the behavior of a property.


__slots__
~~~~~~~~~

By default, instances of both old and new-style classes have a
dictionary for attribute storage.  This wastes space for objects
having very few instance variables.  The space consumption can become
acute when creating large numbers of instances.

The default can be overridden by defining *__slots__* in a new-style
class definition.  The *__slots__* declaration takes a sequence of
instance variables and reserves just enough space in each instance to
hold a value for each variable.  Space is saved because *__dict__* is
not created for each instance.

__slots__

   This class variable can be assigned a string, iterable, or sequence
   of strings with variable names used by instances.  If defined in a
   new-style class, *__slots__* reserves space for the declared
   variables and prevents the automatic creation of *__dict__* and
   *__weakref__* for each instance.

   New in version 2.2.

Notes on using *__slots__*

* When inheriting from a class without *__slots__*, the *__dict__*
  attribute of that class will always be accessible, so a *__slots__*
  definition in the subclass is meaningless.

* Without a *__dict__* variable, instances cannot be assigned new
  variables not listed in the *__slots__* definition.  Attempts to
  assign to an unlisted variable name raises ``AttributeError``. If
  dynamic assignment of new variables is desired, then add
  ``'__dict__'`` to the sequence of strings in the *__slots__*
  declaration.

  Changed in version 2.3: Previously, adding ``'__dict__'`` to the
  *__slots__* declaration would not enable the assignment of new
  attributes not specifically listed in the sequence of instance
  variable names.

* Without a *__weakref__* variable for each instance, classes defining
  *__slots__* do not support weak references to its instances. If weak
  reference support is needed, then add ``'__weakref__'`` to the
  sequence of strings in the *__slots__* declaration.

  Changed in version 2.3: Previously, adding ``'__weakref__'`` to the
  *__slots__* declaration would not enable support for weak
  references.

* *__slots__* are implemented at the class level by creating
  descriptors (*Implementing Descriptors*) for each variable name.  As
  a result, class attributes cannot be used to set default values for
  instance variables defined by *__slots__*; otherwise, the class
  attribute would overwrite the descriptor assignment.

* The action of a *__slots__* declaration is limited to the class
  where it is defined.  As a result, subclasses will have a *__dict__*
  unless they also define *__slots__* (which must only contain names
  of any *additional* slots).

* If a class defines a slot also defined in a base class, the instance
  variable defined by the base class slot is inaccessible (except by
  retrieving its descriptor directly from the base class). This
  renders the meaning of the program undefined.  In the future, a
  check may be added to prevent this.

* Nonempty *__slots__* does not work for classes derived from
  "variable-length" built-in types such as ``long``, ``str`` and
  ``tuple``.

* Any non-string iterable may be assigned to *__slots__*. Mappings may
  also be used; however, in the future, special meaning may be
  assigned to the values corresponding to each key.

* *__class__* assignment works only if both classes have the same
  *__slots__*.

  Changed in version 2.6: Previously, *__class__* assignment raised an
  error if either new or old class had *__slots__*.


Customizing class creation
--------------------------

By default, new-style classes are constructed using ``type()``. A
class definition is read into a separate namespace and the value of
class name is bound to the result of ``type(name, bases, dict)``.

When the class definition is read, if *__metaclass__* is defined then
the callable assigned to it will be called instead of ``type()``. This
allows classes or functions to be written which monitor or alter the
class creation process:

* Modifying the class dictionary prior to the class being created.

* Returning an instance of another class -- essentially performing the
  role of a factory function.

These steps will have to be performed in the metaclass's ``__new__()``
method -- ``type.__new__()`` can then be called from this method to
create a class with different properties.  This example adds a new
element to the class dictionary before creating the class:

   class metacls(type):
       def __new__(mcs, name, bases, dict):
           dict['foo'] = 'metacls was here'
           return type.__new__(mcs, name, bases, dict)

You can of course also override other class methods (or add new
methods); for example defining a custom ``__call__()`` method in the
metaclass allows custom behavior when the class is called, e.g. not
always creating a new instance.

__metaclass__

   This variable can be any callable accepting arguments for ``name``,
   ``bases``, and ``dict``.  Upon class creation, the callable is used
   instead of the built-in ``type()``.

   New in version 2.2.

The appropriate metaclass is determined by the following precedence
rules:

* If ``dict['__metaclass__']`` exists, it is used.

* Otherwise, if there is at least one base class, its metaclass is
  used (this looks for a *__class__* attribute first and if not found,
  uses its type).

* Otherwise, if a global variable named __metaclass__ exists, it is
  used.

* Otherwise, the old-style, classic metaclass (types.ClassType) is
  used.

The potential uses for metaclasses are boundless. Some ideas that have
been explored including logging, interface checking, automatic
delegation, automatic property creation, proxies, frameworks, and
automatic resource locking/synchronization.


Emulating callable objects
--------------------------

object.__call__(self[, args...])

   Called when the instance is "called" as a function; if this method
   is defined, ``x(arg1, arg2, ...)`` is a shorthand for
   ``x.__call__(arg1, arg2, ...)``.


Emulating container types
-------------------------

The following methods can be defined to implement container objects.
Containers usually are sequences (such as lists or tuples) or mappings
(like dictionaries), but can represent other containers as well.  The
first set of methods is used either to emulate a sequence or to
emulate a mapping; the difference is that for a sequence, the
allowable keys should be the integers *k* for which ``0 <= k < N``
where *N* is the length of the sequence, or slice objects, which
define a range of items. (For backwards compatibility, the method
``__getslice__()`` (see below) can also be defined to handle simple,
but not extended slices.) It is also recommended that mappings provide
the methods ``keys()``, ``values()``, ``items()``, ``has_key()``,
``get()``, ``clear()``, ``setdefault()``, ``iterkeys()``,
``itervalues()``, ``iteritems()``, ``pop()``, ``popitem()``,
``copy()``, and ``update()`` behaving similar to those for Python's
standard dictionary objects.  The ``UserDict`` module provides a
``DictMixin`` class to help create those methods from a base set of
``__getitem__()``, ``__setitem__()``, ``__delitem__()``, and
``keys()``. Mutable sequences should provide methods ``append()``,
``count()``, ``index()``, ``extend()``, ``insert()``, ``pop()``,
``remove()``, ``reverse()`` and ``sort()``, like Python standard list
objects.  Finally, sequence types should implement addition (meaning
concatenation) and multiplication (meaning repetition) by defining the
methods ``__add__()``, ``__radd__()``, ``__iadd__()``, ``__mul__()``,
``__rmul__()`` and ``__imul__()`` described below; they should not
define ``__coerce__()`` or other numerical operators.  It is
recommended that both mappings and sequences implement the
``__contains__()`` method to allow efficient use of the ``in``
operator; for mappings, ``in`` should be equivalent of ``has_key()``;
for sequences, it should search through the values.  It is further
recommended that both mappings and sequences implement the
``__iter__()`` method to allow efficient iteration through the
container; for mappings, ``__iter__()`` should be the same as
``iterkeys()``; for sequences, it should iterate through the values.

object.__len__(self)

   Called to implement the built-in function ``len()``.  Should return
   the length of the object, an integer ``>=`` 0.  Also, an object
   that doesn't define a ``__nonzero__()`` method and whose
   ``__len__()`` method returns zero is considered to be false in a
   Boolean context.

object.__getitem__(self, key)

   Called to implement evaluation of ``self[key]``. For sequence
   types, the accepted keys should be integers and slice objects.
   Note that the special interpretation of negative indexes (if the
   class wishes to emulate a sequence type) is up to the
   ``__getitem__()`` method. If *key* is of an inappropriate type,
   ``TypeError`` may be raised; if of a value outside the set of
   indexes for the sequence (after any special interpretation of
   negative values), ``IndexError`` should be raised. For mapping
   types, if *key* is missing (not in the container), ``KeyError``
   should be raised.

   Note: ``for`` loops expect that an ``IndexError`` will be raised for
     illegal indexes to allow proper detection of the end of the
     sequence.

object.__setitem__(self, key, value)

   Called to implement assignment to ``self[key]``.  Same note as for
   ``__getitem__()``.  This should only be implemented for mappings if
   the objects support changes to the values for keys, or if new keys
   can be added, or for sequences if elements can be replaced.  The
   same exceptions should be raised for improper *key* values as for
   the ``__getitem__()`` method.

object.__delitem__(self, key)

   Called to implement deletion of ``self[key]``.  Same note as for
   ``__getitem__()``.  This should only be implemented for mappings if
   the objects support removal of keys, or for sequences if elements
   can be removed from the sequence.  The same exceptions should be
   raised for improper *key* values as for the ``__getitem__()``
   method.

object.__iter__(self)

   This method is called when an iterator is required for a container.
   This method should return a new iterator object that can iterate
   over all the objects in the container.  For mappings, it should
   iterate over the keys of the container, and should also be made
   available as the method ``iterkeys()``.

   Iterator objects also need to implement this method; they are
   required to return themselves.  For more information on iterator
   objects, see *Iterator Types*.

object.__reversed__(self)

   Called (if present) by the ``reversed()`` built-in to implement
   reverse iteration.  It should return a new iterator object that
   iterates over all the objects in the container in reverse order.

   If the ``__reversed__()`` method is not provided, the
   ``reversed()`` built-in will fall back to using the sequence
   protocol (``__len__()`` and ``__getitem__()``).  Objects that
   support the sequence protocol should only provide
   ``__reversed__()`` if they can provide an implementation that is
   more efficient than the one provided by ``reversed()``.

   New in version 2.6.

The membership test operators (``in`` and ``not in``) are normally
implemented as an iteration through a sequence.  However, container
objects can supply the following special method with a more efficient
implementation, which also does not require the object be a sequence.

object.__contains__(self, item)

   Called to implement membership test operators.  Should return true
   if *item* is in *self*, false otherwise.  For mapping objects, this
   should consider the keys of the mapping rather than the values or
   the key-item pairs.

   For objects that don't define ``__contains__()``, the membership
   test first tries iteration via ``__iter__()``, then the old
   sequence iteration protocol via ``__getitem__()``, see *this
   section in the language reference*.


Additional methods for emulation of sequence types
--------------------------------------------------

The following optional methods can be defined to further emulate
sequence objects.  Immutable sequences methods should at most only
define ``__getslice__()``; mutable sequences might define all three
methods.

object.__getslice__(self, i, j)

   Deprecated since version 2.0: Support slice objects as parameters
   to the ``__getitem__()`` method. (However, built-in types in
   CPython currently still implement ``__getslice__()``.  Therefore,
   you have to override it in derived classes when implementing
   slicing.)

   Called to implement evaluation of ``self[i:j]``. The returned
   object should be of the same type as *self*.  Note that missing *i*
   or *j* in the slice expression are replaced by zero or
   ``sys.maxint``, respectively.  If negative indexes are used in the
   slice, the length of the sequence is added to that index. If the
   instance does not implement the ``__len__()`` method, an
   ``AttributeError`` is raised. No guarantee is made that indexes
   adjusted this way are not still negative.  Indexes which are
   greater than the length of the sequence are not modified. If no
   ``__getslice__()`` is found, a slice object is created instead, and
   passed to ``__getitem__()`` instead.

object.__setslice__(self, i, j, sequence)

   Called to implement assignment to ``self[i:j]``. Same notes for *i*
   and *j* as for ``__getslice__()``.

   This method is deprecated. If no ``__setslice__()`` is found, or
   for extended slicing of the form ``self[i:j:k]``, a slice object is
   created, and passed to ``__setitem__()``, instead of
   ``__setslice__()`` being called.

object.__delslice__(self, i, j)

   Called to implement deletion of ``self[i:j]``. Same notes for *i*
   and *j* as for ``__getslice__()``. This method is deprecated. If no
   ``__delslice__()`` is found, or for extended slicing of the form
   ``self[i:j:k]``, a slice object is created, and passed to
   ``__delitem__()``, instead of ``__delslice__()`` being called.

Notice that these methods are only invoked when a single slice with a
single colon is used, and the slice method is available.  For slice
operations involving extended slice notation, or in absence of the
slice methods, ``__getitem__()``, ``__setitem__()`` or
``__delitem__()`` is called with a slice object as argument.

The following example demonstrate how to make your program or module
compatible with earlier versions of Python (assuming that methods
``__getitem__()``, ``__setitem__()`` and ``__delitem__()`` support
slice objects as arguments):

   class MyClass:
       ...
       def __getitem__(self, index):
           ...
       def __setitem__(self, index, value):
           ...
       def __delitem__(self, index):
           ...

       if sys.version_info < (2, 0):
           # They won't be defined if version is at least 2.0 final

           def __getslice__(self, i, j):
               return self[max(0, i):max(0, j):]
           def __setslice__(self, i, j, seq):
               self[max(0, i):max(0, j):] = seq
           def __delslice__(self, i, j):
               del self[max(0, i):max(0, j):]
       ...

Note the calls to ``max()``; these are necessary because of the
handling of negative indices before the ``__*slice__()`` methods are
called.  When negative indexes are used, the ``__*item__()`` methods
receive them as provided, but the ``__*slice__()`` methods get a
"cooked" form of the index values.  For each negative index value, the
length of the sequence is added to the index before calling the method
(which may still result in a negative index); this is the customary
handling of negative indexes by the built-in sequence types, and the
``__*item__()`` methods are expected to do this as well.  However,
since they should already be doing that, negative indexes cannot be
passed in; they must be constrained to the bounds of the sequence
before being passed to the ``__*item__()`` methods. Calling ``max(0,
i)`` conveniently returns the proper value.


Emulating numeric types
-----------------------

The following methods can be defined to emulate numeric objects.
Methods corresponding to operations that are not supported by the
particular kind of number implemented (e.g., bitwise operations for
non-integral numbers) should be left undefined.

object.__add__(self, other)
object.__sub__(self, other)
object.__mul__(self, other)
object.__floordiv__(self, other)
object.__mod__(self, other)
object.__divmod__(self, other)
object.__pow__(self, other[, modulo])
object.__lshift__(self, other)
object.__rshift__(self, other)
object.__and__(self, other)
object.__xor__(self, other)
object.__or__(self, other)

   These methods are called to implement the binary arithmetic
   operations (``+``, ``-``, ``*``, ``//``, ``%``, ``divmod()``,
   ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``, ``|``).  For
   instance, to evaluate the expression ``x + y``, where *x* is an
   instance of a class that has an ``__add__()`` method,
   ``x.__add__(y)`` is called.  The ``__divmod__()`` method should be
   the equivalent to using ``__floordiv__()`` and ``__mod__()``; it
   should not be related to ``__truediv__()`` (described below).  Note
   that ``__pow__()`` should be defined to accept an optional third
   argument if the ternary version of the built-in ``pow()`` function
   is to be supported.

   If one of those methods does not support the operation with the
   supplied arguments, it should return ``NotImplemented``.

object.__div__(self, other)
object.__truediv__(self, other)

   The division operator (``/``) is implemented by these methods.  The
   ``__truediv__()`` method is used when ``__future__.division`` is in
   effect, otherwise ``__div__()`` is used.  If only one of these two
   methods is defined, the object will not support division in the
   alternate context; ``TypeError`` will be raised instead.

object.__radd__(self, other)
object.__rsub__(self, other)
object.__rmul__(self, other)
object.__rdiv__(self, other)
object.__rtruediv__(self, other)
object.__rfloordiv__(self, other)
object.__rmod__(self, other)
object.__rdivmod__(self, other)
object.__rpow__(self, other)
object.__rlshift__(self, other)
object.__rrshift__(self, other)
object.__rand__(self, other)
object.__rxor__(self, other)
object.__ror__(self, other)

   These methods are called to implement the binary arithmetic
   operations (``+``, ``-``, ``*``, ``/``, ``%``, ``divmod()``,
   ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``, ``|``) with
   reflected (swapped) operands.  These functions are only called if
   the left operand does not support the corresponding operation and
   the operands are of different types. [3] For instance, to evaluate
   the expression ``x - y``, where *y* is an instance of a class that
   has an ``__rsub__()`` method, ``y.__rsub__(x)`` is called if
   ``x.__sub__(y)`` returns *NotImplemented*.

   Note that ternary ``pow()`` will not try calling ``__rpow__()``
   (the coercion rules would become too complicated).

   Note: If the right operand's type is a subclass of the left operand's
     type and that subclass provides the reflected method for the
     operation, this method will be called before the left operand's
     non-reflected method.  This behavior allows subclasses to
     override their ancestors' operations.

object.__iadd__(self, other)
object.__isub__(self, other)
object.__imul__(self, other)
object.__idiv__(self, other)
object.__itruediv__(self, other)
object.__ifloordiv__(self, other)
object.__imod__(self, other)
object.__ipow__(self, other[, modulo])
object.__ilshift__(self, other)
object.__irshift__(self, other)
object.__iand__(self, other)
object.__ixor__(self, other)
object.__ior__(self, other)

   These methods are called to implement the augmented arithmetic
   assignments (``+=``, ``-=``, ``*=``, ``/=``, ``//=``, ``%=``,
   ``**=``, ``<<=``, ``>>=``, ``&=``, ``^=``, ``|=``).  These methods
   should attempt to do the operation in-place (modifying *self*) and
   return the result (which could be, but does not have to be,
   *self*).  If a specific method is not defined, the augmented
   assignment falls back to the normal methods.  For instance, to
   execute the statement ``x += y``, where *x* is an instance of a
   class that has an ``__iadd__()`` method, ``x.__iadd__(y)`` is
   called.  If *x* is an instance of a class that does not define a
   ``__iadd__()`` method, ``x.__add__(y)`` and ``y.__radd__(x)`` are
   considered, as with the evaluation of ``x + y``.

object.__neg__(self)
object.__pos__(self)
object.__abs__(self)
object.__invert__(self)

   Called to implement the unary arithmetic operations (``-``, ``+``,
   ``abs()`` and ``~``).

object.__complex__(self)
object.__int__(self)
object.__long__(self)
object.__float__(self)

   Called to implement the built-in functions ``complex()``,
   ``int()``, ``long()``, and ``float()``.  Should return a value of
   the appropriate type.

object.__oct__(self)
object.__hex__(self)

   Called to implement the built-in functions ``oct()`` and ``hex()``.
   Should return a string value.

object.__index__(self)

   Called to implement ``operator.index()``.  Also called whenever
   Python needs an integer object (such as in slicing).  Must return
   an integer (int or long).

   New in version 2.5.

object.__coerce__(self, other)

   Called to implement "mixed-mode" numeric arithmetic.  Should either
   return a 2-tuple containing *self* and *other* converted to a
   common numeric type, or ``None`` if conversion is impossible.  When
   the common type would be the type of ``other``, it is sufficient to
   return ``None``, since the interpreter will also ask the other
   object to attempt a coercion (but sometimes, if the implementation
   of the other type cannot be changed, it is useful to do the
   conversion to the other type here).  A return value of
   ``NotImplemented`` is equivalent to returning ``None``.


Coercion rules
--------------

This section used to document the rules for coercion.  As the language
has evolved, the coercion rules have become hard to document
precisely; documenting what one version of one particular
implementation does is undesirable.  Instead, here are some informal
guidelines regarding coercion.  In Python 3.0, coercion will not be
supported.

* If the left operand of a % operator is a string or Unicode object,
  no coercion takes place and the string formatting operation is
  invoked instead.

* It is no longer recommended to define a coercion operation. Mixed-
  mode operations on types that don't define coercion pass the
  original arguments to the operation.

* New-style classes (those derived from ``object``) never invoke the
  ``__coerce__()`` method in response to a binary operator; the only
  time ``__coerce__()`` is invoked is when the built-in function
  ``coerce()`` is called.

* For most intents and purposes, an operator that returns
  ``NotImplemented`` is treated the same as one that is not
  implemented at all.

* Below, ``__op__()`` and ``__rop__()`` are used to signify the
  generic method names corresponding to an operator; ``__iop__()`` is
  used for the corresponding in-place operator.  For example, for the
  operator '``+``', ``__add__()`` and ``__radd__()`` are used for the
  left and right variant of the binary operator, and ``__iadd__()``
  for the in-place variant.

* For objects *x* and *y*, first ``x.__op__(y)`` is tried.  If this is
  not implemented or returns ``NotImplemented``, ``y.__rop__(x)`` is
  tried.  If this is also not implemented or returns
  ``NotImplemented``, a ``TypeError`` exception is raised.  But see
  the following exception:

* Exception to the previous item: if the left operand is an instance
  of a built-in type or a new-style class, and the right operand is an
  instance of a proper subclass of that type or class and overrides
  the base's ``__rop__()`` method, the right operand's ``__rop__()``
  method is tried *before* the left operand's ``__op__()`` method.

  This is done so that a subclass can completely override binary
  operators. Otherwise, the left operand's ``__op__()`` method would
  always accept the right operand: when an instance of a given class
  is expected, an instance of a subclass of that class is always
  acceptable.

* When either operand type defines a coercion, this coercion is called
  before that type's ``__op__()`` or ``__rop__()`` method is called,
  but no sooner.  If the coercion returns an object of a different
  type for the operand whose coercion is invoked, part of the process
  is redone using the new object.

* When an in-place operator (like '``+=``') is used, if the left
  operand implements ``__iop__()``, it is invoked without any
  coercion.  When the operation falls back to ``__op__()`` and/or
  ``__rop__()``, the normal coercion rules apply.

* In ``x + y``, if *x* is a sequence that implements sequence
  concatenation, sequence concatenation is invoked.

* In ``x * y``, if one operator is a sequence that implements sequence
  repetition, and the other is an integer (``int`` or ``long``),
  sequence repetition is invoked.

* Rich comparisons (implemented by methods ``__eq__()`` and so on)
  never use coercion.  Three-way comparison (implemented by
  ``__cmp__()``) does use coercion under the same conditions as other
  binary operations use it.

* In the current implementation, the built-in numeric types ``int``,
  ``long`` and ``float`` do not use coercion; the type ``complex``
  however does use coercion for binary operators and rich comparisons,
  despite the above rules.  The difference can become apparent when
  subclassing these types.  Over time, the type ``complex`` may be
  fixed to avoid coercion. All these types implement a
  ``__coerce__()`` method, for use by the built-in ``coerce()``
  function.


With Statement Context Managers
-------------------------------

New in version 2.5.

A *context manager* is an object that defines the runtime context to
be established when executing a ``with`` statement. The context
manager handles the entry into, and the exit from, the desired runtime
context for the execution of the block of code.  Context managers are
normally invoked using the ``with`` statement (described in section
*The with statement*), but can also be used by directly invoking their
methods.

Typical uses of context managers include saving and restoring various
kinds of global state, locking and unlocking resources, closing opened
files, etc.

For more information on context managers, see *Context Manager Types*.

object.__enter__(self)

   Enter the runtime context related to this object. The ``with``
   statement will bind this method's return value to the target(s)
   specified in the ``as`` clause of the statement, if any.

object.__exit__(self, exc_type, exc_value, traceback)

   Exit the runtime context related to this object. The parameters
   describe the exception that caused the context to be exited. If the
   context was exited without an exception, all three arguments will
   be ``None``.

   If an exception is supplied, and the method wishes to suppress the
   exception (i.e., prevent it from being propagated), it should
   return a true value. Otherwise, the exception will be processed
   normally upon exit from this method.

   Note that ``__exit__()`` methods should not reraise the passed-in
   exception; this is the caller's responsibility.

See also:

   **PEP 0343** - The "with" statement
      The specification, background, and examples for the Python
      ``with`` statement.


Special method lookup for old-style classes
-------------------------------------------

For old-style classes, special methods are always looked up in exactly
the same way as any other method or attribute. This is the case
regardless of whether the method is being looked up explicitly as in
``x.__getitem__(i)`` or implicitly as in ``x[i]``.

This behaviour means that special methods may exhibit different
behaviour for different instances of a single old-style class if the
appropriate special attributes are set differently:

   >>> class C:
   ...     pass
   ...
   >>> c1 = C()
   >>> c2 = C()
   >>> c1.__len__ = lambda: 5
   >>> c2.__len__ = lambda: 9
   >>> len(c1)
   5
   >>> len(c2)
   9


Special method lookup for new-style classes
-------------------------------------------

For new-style classes, implicit invocations of special methods are
only guaranteed to work correctly if defined on an object's type, not
in the object's instance dictionary.  That behaviour is the reason why
the following code raises an exception (unlike the equivalent example
with old-style classes):

   >>> class C(object):
   ...     pass
   ...
   >>> c = C()
   >>> c.__len__ = lambda: 5
   >>> len(c)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: object of type 'C' has no len()

The rationale behind this behaviour lies with a number of special
methods such as ``__hash__()`` and ``__repr__()`` that are implemented
by all objects, including type objects. If the implicit lookup of
these methods used the conventional lookup process, they would fail
when invoked on the type object itself:

   >>> 1 .__hash__() == hash(1)
   True
   >>> int.__hash__() == hash(int)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: descriptor '__hash__' of 'int' object needs an argument

Incorrectly attempting to invoke an unbound method of a class in this
way is sometimes referred to as 'metaclass confusion', and is avoided
by bypassing the instance when looking up special methods:

   >>> type(1).__hash__(1) == hash(1)
   True
   >>> type(int).__hash__(int) == hash(int)
   True

In addition to bypassing any instance attributes in the interest of
correctness, implicit special method lookup generally also bypasses
the ``__getattribute__()`` method even of the object's metaclass:

   >>> class Meta(type):
   ...    def __getattribute__(*args):
   ...       print "Metaclass getattribute invoked"
   ...       return type.__getattribute__(*args)
   ...
   >>> class C(object):
   ...     __metaclass__ = Meta
   ...     def __len__(self):
   ...         return 10
   ...     def __getattribute__(*args):
   ...         print "Class getattribute invoked"
   ...         return object.__getattribute__(*args)
   ...
   >>> c = C()
   >>> c.__len__()                 # Explicit lookup via instance
   Class getattribute invoked
   10
   >>> type(c).__len__(c)          # Explicit lookup via type
   Metaclass getattribute invoked
   10
   >>> len(c)                      # Implicit lookup
   10

Bypassing the ``__getattribute__()`` machinery in this fashion
provides significant scope for speed optimisations within the
interpreter, at the cost of some flexibility in the handling of
special methods (the special method *must* be set on the class object
itself in order to be consistently invoked by the interpreter).

-[ Footnotes ]-

[1] It *is* possible in some cases to change an object's type, under
    certain controlled conditions. It generally isn't a good idea
    though, since it can lead to some very strange behaviour if it is
    handled incorrectly.

[2] A descriptor can define any combination of ``__get__()``,
    ``__set__()`` and ``__delete__()``.  If it does not define
    ``__get__()``, then accessing the attribute even on an instance
    will return the descriptor object itself.  If the descriptor
    defines ``__set__()`` and/or ``__delete__()``, it is a data
    descriptor; if it defines neither, it is a non-data descriptor.

[3] For operands of the same type, it is assumed that if the non-
    reflected method (such as ``__add__()``) fails the operation is
    not supported, which is why the reflected method is not called.
