3. Data model
*************


3.1. 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. Do not depend
on immediate finalization of objects when they become unreachable (ex:
always close files).

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".)


3.2. 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 are
      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:

      字串 (String)
         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:

      List（串列）
         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:

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

      Most of the attributes labelled 「Writable」 check the type of
      the assigned value.

      2.4 版更變: "func_name" is now writable.

      2.6 版更變: The double-underscore attributes "__closure__",
      "__code__", "__defaults__", and "__globals__" were introduced as
      aliases for the corresponding "func_*" attributes for forwards
      compatibility with Python 3.

      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.

      2.2 版更變: "im_self" used to refer to the class that defined
      the method.

      2.6 版更變: For Python 3 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, 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 *alist*.

   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.

   **CPython implementation detail:** Because of the way CPython
   clears module dictionaries, the module dictionary will be cleared
   when the module falls out of scope even if the dictionary still has
   live references.  To avoid this, copy the dictionary or keep the
   module around while using its dictionary directly.

   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
   https://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_self" attribute is "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.

         2.3 版新加入.

   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.


3.3. New-style and classic classes
==================================

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

Up to Python 2.1 the concept of "class" was unrelated to the concept
of "type", and old-style classes were the only flavor available.  For
an old-style class, the statement "x.__class__" provides the class of
*x*, but "type(x)" is always "<type 'instance'>".  This reflects the
fact that all old-style instances, independent of their class, are
implemented with a single built-in type, called "instance".

New-style classes were introduced in Python 2.2 to unify the concepts
of "class" and "type".  A new-style class is simply a user-defined
type, no more, no less.  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
https://www.python.org/doc/newstyle/ for sources of additional
information.

Old-style classes are removed in Python 3, leaving only new-style
classes.


3.4. 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.)


3.4.1. 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 after the instance has been created (by "__new__()"), but
   before it is returned to the caller.  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...])".

   Because "__new__()" and "__init__()" work together in constructing
   objects ("__new__()" to create it, and "__init__()" to customise
   it), no non-"None" value may be returned by "__init__()"; 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.

   備註: "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.

   警告: 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.

   See also the "-R" command-line option.

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)

   2.1 版新加入.

   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 "functools.total_ordering()".

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)

   2.1 版更變: 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 mix together the hash values of the components of the
   object that also play a part in comparison of objects by packing
   them into a tuple and hashing the tuple. Example:

      def __hash__(self):
          return hash((self.name, self.nick, self.color))

   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 an 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 a result derived from
   "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").

   2.5 版更變: "__hash__()" may now also return a long integer object;
   the 32-bit integer is then derived from the hash of that object.

   2.6 版更變: "__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.


3.4.2. 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.


3.4.2.1. 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)".

   備註: 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.


3.4.2.2. Implementing Descriptors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following methods only apply when an instance of the class
containing the method (a so-called *descriptor* class) appears in an
*owner* class (the descriptor must be in either the owner’s class
dictionary or in the class dictionary for one of its parents).  In the
examples below, 「the attribute」 refers to the attribute whose name
is the key of the property in the owner class』 "__dict__".

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.


3.4.2.3. 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, obj.__class__)".

For instance bindings, the precedence of descriptor invocation depends
on the which descriptor methods are defined.  A descriptor can define
any combination of "__get__()", "__set__()" and "__delete__()".  If it
does not define "__get__()", then accessing the attribute will return
the descriptor object itself unless there is a value in the object’s
instance dictionary.  If the descriptor defines "__set__()" and/or
"__delete__()", it is a data descriptor; if it defines neither, it is
a non-data descriptor.  Normally, data descriptors define both
"__get__()" and "__set__()", while non-data descriptors have just the
"__get__()" method.  Data descriptors with "__set__()" and "__get__()"
defined always override a redefinition in an instance dictionary.  In
contrast, non-data descriptors can be overridden by instances.

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.


3.4.2.4. __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.

   2.2 版新加入.

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.

  2.3 版更變: 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.

  2.3 版更變: 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__*.

  2.6 版更變: Previously, *__class__* assignment raised an error if
  either new or old class had *__slots__*.


3.4.3. 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()".

   2.2 版新加入.

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.


3.4.4. Customizing instance and subclass checks
-----------------------------------------------

2.6 版新加入.

The following methods are used to override the default behavior of the
"isinstance()" and "issubclass()" built-in functions.

In particular, the metaclass "abc.ABCMeta" implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as 「
virtual base classes」 to any class or type (including built-in
types), including other ABCs.

class.__instancecheck__(self, instance)

   Return true if *instance* should be considered a (direct or
   indirect) instance of *class*. If defined, called to implement
   "isinstance(instance, class)".

class.__subclasscheck__(self, subclass)

   Return true if *subclass* should be considered a (direct or
   indirect) subclass of *class*.  If defined, called to implement
   "issubclass(subclass, class)".

Note that these methods are looked up on the type (metaclass) of a
class.  They cannot be defined as class methods in the actual class.
This is consistent with the lookup of special methods that are called
on instances, only in this case the instance is itself a class.

也參考:

  **PEP 3119** - Introducing Abstract Base Classes
     Includes the specification for customizing "isinstance()" and
     "issubclass()" behavior through "__instancecheck__()" and
     "__subclasscheck__()", with motivation for this functionality in
     the context of adding Abstract Base Classes (see the "abc"
     module) to the language.


3.4.5. 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, ...)".


3.4.6. 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.

   **CPython implementation detail:** In CPython, the length is
   required to be at most "sys.maxsize". If the length is larger than
   "sys.maxsize" some features (such as "len()") may raise
   "OverflowError".  To prevent raising "OverflowError" by truth value
   testing, an object must define a "__nonzero__()" method.

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.

   備註: "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.__missing__(self, key)

   Called by "dict"."__getitem__()" to implement "self[key]" for dict
   subclasses when key is not in the dictionary.

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()".

   2.6 版新加入.

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.


3.4.7. 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)

   2.0 版後已棄用: 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.maxsize",
   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.


3.4.8. 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. [2] 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).

   備註: 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).

   2.5 版新加入.

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".


3.4.9. 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, 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 operand 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", "float", and "complex" do not use coercion. All these types
  implement a "__coerce__()" method, for use by the built-in
  "coerce()" function.

  2.7 版更變: The complex type no longer makes implicit calls to the
  "__coerce__()" method for mixed-type binary arithmetic operations.


3.4.10. With Statement Context Managers
---------------------------------------

2.5 版新加入.

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.

也參考:

  **PEP 343** - The 「with」 statement
     The specification, background, and examples for the Python "with"
     statement.


3.4.11. 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


3.4.12. 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).

-[ 註解 ]-

[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] 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.
