Bumping to 3.0rc2.

3 files changed, 7 insertions, 7 deletions

diff --git a/Lib/distutils/__init__.py b/Lib/distutils/__init__.py
index 2bc0ab0..5e4972f 100644
--- a/Lib/distutils/__init__.py
+++ b/Lib/distutils/__init__.py
@@ -20,5 +20,5 @@ __revision__ = "$Id$"
 #
 
 #--start constants--
-__version__ = "3.0rc1"
+__version__ = "3.0rc2"
 #--end constants--
diff --git a/Lib/idlelib/idlever.py b/Lib/idlelib/idlever.py
index 754ac96..fdeb65d 100644
--- a/Lib/idlelib/idlever.py
+++ b/Lib/idlelib/idlever.py
@@ -1 +1 @@
-IDLE_VERSION = "3.0rc1"
+IDLE_VERSION = "3.0rc2"
diff --git a/Lib/pydoc_topics.py b/Lib/pydoc_topics.py
index 1fd446e..e2fa26f 100644
--- a/Lib/pydoc_topics.py
+++ b/Lib/pydoc_topics.py
@@ -1,9 +1,9 @@
-# Autogenerated by Sphinx on Thu Oct  2 15:58:57 2008
+# Autogenerated by Sphinx on Wed Nov  5 22:25:20 2008
 topics = {'assert': '\nThe ``assert`` statement\n************************\n\nAssert statements are a convenient way to insert debugging assertions\ninto a program:\n\n   assert_stmt ::= "assert" expression ["," expression]\n\nThe simple form, ``assert expression``, is equivalent to\n\n   if __debug__:\n      if not expression: raise AssertionError\n\nThe extended form, ``assert expression1, expression2``, is equivalent\nto\n\n   if __debug__:\n      if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that ``__debug__`` and ``AssertionError``\nrefer to the built-in variables with those names.  In the current\nimplementation, the built-in variable ``__debug__`` is ``True`` under\nnormal circumstances, ``False`` when optimization is requested\n(command line option -O).  The current code generator emits no code\nfor an assert statement when optimization is requested at compile\ntime.  Note that it is unnecessary to include the source code for the\nexpression that failed in the error message; it will be displayed as\npart of the stack trace.\n\nAssignments to ``__debug__`` are illegal.  The value for the built-in\nvariable is determined when the interpreter starts.\n',
  'assignment': '\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n   assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n   target_list     ::= target ("," target)* [","]\n   target          ::= identifier\n              | "(" target_list ")"\n              | "[" target_list "]"\n              | attributeref\n              | subscription\n              | slicing\n              | "*" target\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable.  The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section *The standard type\nhierarchy*).\n\nAssignment of an object to a target list, optionally enclosed in\nparentheses or square brackets, is recursively defined as follows.\n\n* If the target list is a single target: The object is assigned to\n  that target.\n\n* If the target list is a comma-separated list of targets:\n\n  * If the target list contains one target prefixed with an asterisk,\n    called a "starred" target: The object must be a sequence with at\n    least as many items as there are targets in the target list, minus\n    one.  The first items of the sequence are assigned, from left to\n    right, to the targets before the starred target.  The final items\n    of the sequence are assigned to the targets after the starred\n    target.  A list of the remaining items in the sequence is then\n    assigned to the starred target (the list can be empty).\n\n  * Else: The object must be a sequence with the same number of items\n    as there are targets in the target list, and the items are\n    assigned, from left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n  * If the name does not occur in a ``global`` or ``nonlocal``\n    statement in the current code block: the name is bound to the\n    object in the current local namespace.\n\n  * Otherwise: the name is bound to the object in the global namespace\n    or the outer namespace determined by ``nonlocal``, respectively.\n\n  The name is rebound if it was already bound.  This may cause the\n  reference count for the object previously bound to the name to reach\n  zero, causing the object to be deallocated and its destructor (if it\n  has one) to be called.\n\n  The name is rebound if it was already bound.  This may cause the\n  reference count for the object previously bound to the name to reach\n  zero, causing the object to be deallocated and its destructor (if it\n  has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in square\n  brackets: The object must be a sequence with the same number of\n  items as there are targets in the target list, and its items are\n  assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n  the reference is evaluated.  It should yield an object with\n  assignable attributes; if this is not the case, ``TypeError`` is\n  raised.  That object is then asked to assign the assigned object to\n  the given attribute; if it cannot perform the assignment, it raises\n  an exception (usually but not necessarily ``AttributeError``).\n\n* If the target is a subscription: The primary expression in the\n  reference is evaluated.  It should yield either a mutable sequence\n  object (such as a list) or a mapping object (such as a dictionary).\n  Next, the subscript expression is evaluated.\n\n  If the primary is a mutable sequence object (such as a list), the\n  subscript must yield an integer.  If it is negative, the sequence\'s\n  length is added to it.  The resulting value must be a nonnegative\n  integer less than the sequence\'s length, and the sequence is asked\n  to assign the assigned object to its item with that index.  If the\n  index is out of range, ``IndexError`` is raised (assignment to a\n  subscripted sequence cannot add new items to a list).\n\n  If the primary is a mapping object (such as a dictionary), the\n  subscript must have a type compatible with the mapping\'s key type,\n  and the mapping is then asked to create a key/datum pair which maps\n  the subscript to the assigned object.  This can either replace an\n  existing key/value pair with the same key value, or insert a new\n  key/value pair (if no key with the same value existed).\n\n  For user-defined objects, the ``__setitem__()`` method is called\n  with appropriate arguments.\n\n* If the target is a slicing: The primary expression in the reference\n  is evaluated.  It should yield a mutable sequence object (such as a\n  list).  The assigned object should be a sequence object of the same\n  type.  Next, the lower and upper bound expressions are evaluated,\n  insofar they are present; defaults are zero and the sequence\'s\n  length.  The bounds should evaluate to integers. If either bound is\n  negative, the sequence\'s length is added to it.  The resulting\n  bounds are clipped to lie between zero and the sequence\'s length,\n  inclusive.  Finally, the sequence object is asked to replace the\n  slice with the items of the assigned sequence.  The length of the\n  slice may be different from the length of the assigned sequence,\n  thus changing the length of the target sequence, if the object\n  allows it.\n\n(In the current implementation, the syntax for targets is taken to be\nthe same as for expressions, and invalid syntax is rejected during the\ncode generation phase, causing less detailed error messages.)\n\nWARNING: Although the definition of assignment implies that overlaps\nbetween the left-hand side and the right-hand side are \'safe\' (for\nexample ``a, b = b, a`` swaps two variables), overlaps *within* the\ncollection of assigned-to variables are not safe!  For instance, the\nfollowing program prints ``[0, 2]``:\n\n   x = [0, 1]\n   i = 0\n   i, x[i] = 1, 2\n   print(x)\n\nSee also:\n\n   **PEP 3132** - Extended Iterable Unpacking\n      The specification for the ``*target`` feature.\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n   augmented_assignment_stmt ::= target augop (expression_list | yield_expression)\n   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n             | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like ``x += 1`` can be rewritten as\n``x = x + 1`` to achieve a similar, but not exactly equal effect. In\nthe augmented version, ``x`` is only evaluated once. Also, when\npossible, the actual operation is performed *in-place*, meaning that\nrather than creating a new object and assigning that to the target,\nthe old object is modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the initial value is\nretrieved with a ``getattr()`` and the result is assigned with a\n``setattr()``.  Notice that the two methods do not necessarily refer\nto the same variable.  When ``getattr()`` refers to a class variable,\n``setattr()`` still writes to an instance variable. For example:\n\n   class A:\n       x = 3    # class variable\n   a = A()\n   a.x += 1     # writes a.x as 4 leaving A.x as 3\n',
  'atom-identifiers': '\nIdentifiers (Names)\n*******************\n\nAn identifier occurring as an atom is a name.  See section\n*Identifiers and keywords* for lexical definition and section *Naming\nand binding* for documentation of naming and binding.\n\nWhen the name is bound to an object, evaluation of the atom yields\nthat object. When a name is not bound, an attempt to evaluate it\nraises a ``NameError`` exception.\n\n**Private name mangling:** When an identifier that textually occurs in\na class definition begins with two or more underscore characters and\ndoes not end in two or more underscores, it is considered a *private\nname* of that class. Private names are transformed to a longer form\nbefore code is generated for them.  The transformation inserts the\nclass name in front of the name, with leading underscores removed, and\na single underscore inserted in front of the class name.  For example,\nthe identifier ``__spam`` occurring in a class named ``Ham`` will be\ntransformed to ``_Ham__spam``.  This transformation is independent of\nthe syntactical context in which the identifier is used.  If the\ntransformed name is extremely long (longer than 255 characters),\nimplementation defined truncation may happen.  If the class name\nconsists only of underscores, no transformation is done.\n',
  'atom-literals': "\nLiterals\n********\n\nPython supports string and bytes literals and various numeric\nliterals:\n\n   literal ::= stringliteral | bytesliteral\n               | integer | floatnumber | imagnumber\n\nEvaluation of a literal yields an object of the given type (string,\nbytes, integer, floating point number, complex number) with the given\nvalue.  The value may be approximated in the case of floating point\nand imaginary (complex) literals.  See section *Literals* for details.\n\nWith the exception of bytes literals, these all correspond to\nimmutable data types, and hence the object's identity is less\nimportant than its value. Multiple evaluations of literals with the\nsame value (either the same occurrence in the program text or a\ndifferent occurrence) may obtain the same object or a different object\nwith the same value.\n",
- 'attribute-access': '\nCustomizing attribute access\n****************************\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of ``x.name``)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n   Called when an attribute lookup has not found the attribute in the\n   usual places (i.e. it is not an instance attribute nor is it found\n   in the class tree for ``self``).  ``name`` is the attribute name.\n   This method should return the (computed) attribute value or raise\n   an ``AttributeError`` exception.\n\n   Note that if the attribute is found through the normal mechanism,\n   ``__getattr__()`` is not called.  (This is an intentional asymmetry\n   between ``__getattr__()`` and ``__setattr__()``.) This is done both\n   for efficiency reasons and because otherwise ``__getattr__()``\n   would have no way to access other attributes of the instance.  Note\n   that at least for instance variables, you can fake total control by\n   not inserting any values in the instance attribute dictionary (but\n   instead inserting them in another object).  See the\n   ``__getattribute__()`` method below for a way to actually get total\n   control over attribute access.\n\nobject.__getattribute__(self, name)\n\n   Called unconditionally to implement attribute accesses for\n   instances of the class. If the class also defines\n   ``__getattr__()``, the latter will not be called unless\n   ``__getattribute__()`` either calls it explicitly or raises an\n   ``AttributeError``. This method should return the (computed)\n   attribute value or raise an ``AttributeError`` exception. In order\n   to avoid infinite recursion in this method, its implementation\n   should always call the base class method with the same name to\n   access any attributes it needs, for example,\n   ``object.__getattribute__(self, name)``.\n\n   Note: This method may still be bypassed when looking up special methods\n     as the result of implicit invocation via language syntax or\n     builtin functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n   Called when an attribute assignment is attempted.  This is called\n   instead of the normal mechanism (i.e. store the value in the\n   instance dictionary). *name* is the attribute name, *value* is the\n   value to be assigned to it.\n\n   If ``__setattr__()`` wants to assign to an instance attribute, it\n   should call the base class method with the same name, for example,\n   ``object.__setattr__(self, name, value)``.\n\nobject.__delattr__(self, name)\n\n   Like ``__setattr__()`` but for attribute deletion instead of\n   assignment.  This should only be implemented if ``del obj.name`` is\n   meaningful for the object.\n\nobject.__dir__(self)\n\n   Called when ``dir()`` is called on the object.  A list must be\n   returned.\n\n\nImplementing Descriptors\n========================\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in the\nclass dictionary of another class, known as the *owner* class.  In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' ``__dict__``.\n\nobject.__get__(self, instance, owner)\n\n   Called to get the attribute of the owner class (class attribute\n   access) or of an instance of that class (instance attribute\n   access). *owner* is always the owner class, while *instance* is the\n   instance that the attribute was accessed through, or ``None`` when\n   the attribute is accessed through the *owner*.  This method should\n   return the (computed) attribute value or raise an\n   ``AttributeError`` exception.\n\nobject.__set__(self, instance, value)\n\n   Called to set the attribute on an instance *instance* of the owner\n   class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n   Called to delete the attribute on an instance *instance* of the\n   owner class.\n\n\nInvoking Descriptors\n====================\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol:  ``__get__()``, ``__set__()``, and\n``__delete__()``. If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, ``a.x`` has a\nlookup chain starting with ``a.__dict__[\'x\']``, then\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(a)`` excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead.  Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, ``a.x``.\nHow the arguments are assembled depends on ``a``:\n\nDirect Call\n   The simplest and least common call is when user code directly\n   invokes a descriptor method:    ``x.__get__(a)``.\n\nInstance Binding\n   If binding to an object instance, ``a.x`` is transformed into the\n   call: ``type(a).__dict__[\'x\'].__get__(a, type(a))``.\n\nClass Binding\n   If binding to a class, ``A.x`` is transformed into the call:\n   ``A.__dict__[\'x\'].__get__(None, A)``.\n\nSuper Binding\n   If ``a`` is an instance of ``super``, then the binding ``super(B,\n   obj).m()`` searches ``obj.__class__.__mro__`` for the base class\n   ``A`` immediately preceding ``B`` and then invokes the descriptor\n   with the call: ``A.__dict__[\'m\'].__get__(obj, A)``.\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  Normally, data\ndescriptors define both ``__get__()`` and ``__set__()``, while non-\ndata descriptors have just the ``__get__()`` method.  Data descriptors\nalways override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances. [2]\n\nPython methods (including ``staticmethod()`` and ``classmethod()``)\nare implemented as non-data descriptors.  Accordingly, instances can\nredefine and override methods.  This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe ``property()`` function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n=========\n\nBy default, instances of classes have a dictionary for attribute\nstorage.  This wastes space for objects having very few instance\nvariables.  The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable.  Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n   This class variable can be assigned a string, iterable, or sequence\n   of strings with variable names used by instances.  If defined in a\n   class, *__slots__* reserves space for the declared variables and\n   prevents the automatic creation of *__dict__* and *__weakref__* for\n   each instance.\n\n\nNotes on using *__slots__*\n--------------------------\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n  attribute of that class will always be accessible, so a *__slots__*\n  definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n  variables not listed in the *__slots__* definition.  Attempts to\n  assign to an unlisted variable name raises ``AttributeError``. If\n  dynamic assignment of new variables is desired, then add\n  ``\'__dict__\'`` to the sequence of strings in the *__slots__*\n  declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n  *__slots__* do not support weak references to its instances. If weak\n  reference support is needed, then add ``\'__weakref__\'`` to the\n  sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n  descriptors (*Implementing Descriptors*) for each variable name.  As\n  a result, class attributes cannot be used to set default values for\n  instance variables defined by *__slots__*; otherwise, the class\n  attribute would overwrite the descriptor assignment.\n\n* If a class defines a slot also defined in a base class, the instance\n  variable defined by the base class slot is inaccessible (except by\n  retrieving its descriptor directly from the base class). This\n  renders the meaning of the program undefined.  In the future, a\n  check may be added to prevent this.\n\n* The action of a *__slots__* declaration is limited to the class\n  where it is defined.  As a result, subclasses will have a *__dict__*\n  unless they also define *__slots__*.\n\n* *__slots__* do not work for classes derived from "variable-length"\n  built-in types such as ``int``, ``str`` and ``tuple``.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n  also be used; however, in the future, special meaning may be\n  assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n  *__slots__*.\n',
+ 'attribute-access': '\nCustomizing attribute access\n****************************\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of ``x.name``)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n   Called when an attribute lookup has not found the attribute in the\n   usual places (i.e. it is not an instance attribute nor is it found\n   in the class tree for ``self``).  ``name`` is the attribute name.\n   This method should return the (computed) attribute value or raise\n   an ``AttributeError`` exception.\n\n   Note that if the attribute is found through the normal mechanism,\n   ``__getattr__()`` is not called.  (This is an intentional asymmetry\n   between ``__getattr__()`` and ``__setattr__()``.) This is done both\n   for efficiency reasons and because otherwise ``__getattr__()``\n   would have no way to access other attributes of the instance.  Note\n   that at least for instance variables, you can fake total control by\n   not inserting any values in the instance attribute dictionary (but\n   instead inserting them in another object).  See the\n   ``__getattribute__()`` method below for a way to actually get total\n   control over attribute access.\n\nobject.__getattribute__(self, name)\n\n   Called unconditionally to implement attribute accesses for\n   instances of the class. If the class also defines\n   ``__getattr__()``, the latter will not be called unless\n   ``__getattribute__()`` either calls it explicitly or raises an\n   ``AttributeError``. This method should return the (computed)\n   attribute value or raise an ``AttributeError`` exception. In order\n   to avoid infinite recursion in this method, its implementation\n   should always call the base class method with the same name to\n   access any attributes it needs, for example,\n   ``object.__getattribute__(self, name)``.\n\n   Note: This method may still be bypassed when looking up special methods\n     as the result of implicit invocation via language syntax or\n     builtin functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n   Called when an attribute assignment is attempted.  This is called\n   instead of the normal mechanism (i.e. store the value in the\n   instance dictionary). *name* is the attribute name, *value* is the\n   value to be assigned to it.\n\n   If ``__setattr__()`` wants to assign to an instance attribute, it\n   should call the base class method with the same name, for example,\n   ``object.__setattr__(self, name, value)``.\n\nobject.__delattr__(self, name)\n\n   Like ``__setattr__()`` but for attribute deletion instead of\n   assignment.  This should only be implemented if ``del obj.name`` is\n   meaningful for the object.\n\nobject.__dir__(self)\n\n   Called when ``dir()`` is called on the object.  A list must be\n   returned.\n\n\nImplementing Descriptors\n========================\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in the\nclass dictionary of another class, known as the *owner* class.  In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' ``__dict__``.\n\nobject.__get__(self, instance, owner)\n\n   Called to get the attribute of the owner class (class attribute\n   access) or of an instance of that class (instance attribute\n   access). *owner* is always the owner class, while *instance* is the\n   instance that the attribute was accessed through, or ``None`` when\n   the attribute is accessed through the *owner*.  This method should\n   return the (computed) attribute value or raise an\n   ``AttributeError`` exception.\n\nobject.__set__(self, instance, value)\n\n   Called to set the attribute on an instance *instance* of the owner\n   class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n   Called to delete the attribute on an instance *instance* of the\n   owner class.\n\n\nInvoking Descriptors\n====================\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol:  ``__get__()``, ``__set__()``, and\n``__delete__()``. If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, ``a.x`` has a\nlookup chain starting with ``a.__dict__[\'x\']``, then\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(a)`` excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead.  Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, ``a.x``.\nHow the arguments are assembled depends on ``a``:\n\nDirect Call\n   The simplest and least common call is when user code directly\n   invokes a descriptor method:    ``x.__get__(a)``.\n\nInstance Binding\n   If binding to an object instance, ``a.x`` is transformed into the\n   call: ``type(a).__dict__[\'x\'].__get__(a, type(a))``.\n\nClass Binding\n   If binding to a class, ``A.x`` is transformed into the call:\n   ``A.__dict__[\'x\'].__get__(None, A)``.\n\nSuper Binding\n   If ``a`` is an instance of ``super``, then the binding ``super(B,\n   obj).m()`` searches ``obj.__class__.__mro__`` for the base class\n   ``A`` immediately preceding ``B`` and then invokes the descriptor\n   with the call: ``A.__dict__[\'m\'].__get__(obj, A)``.\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  Normally, data\ndescriptors define both ``__get__()`` and ``__set__()``, while non-\ndata descriptors have just the ``__get__()`` method.  Data descriptors\nalways override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances. [2]\n\nPython methods (including ``staticmethod()`` and ``classmethod()``)\nare implemented as non-data descriptors.  Accordingly, instances can\nredefine and override methods.  This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe ``property()`` function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n=========\n\nBy default, instances of classes have a dictionary for attribute\nstorage.  This wastes space for objects having very few instance\nvariables.  The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable.  Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n   This class variable can be assigned a string, iterable, or sequence\n   of strings with variable names used by instances.  If defined in a\n   class, *__slots__* reserves space for the declared variables and\n   prevents the automatic creation of *__dict__* and *__weakref__* for\n   each instance.\n\n\nNotes on using *__slots__*\n--------------------------\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n  attribute of that class will always be accessible, so a *__slots__*\n  definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n  variables not listed in the *__slots__* definition.  Attempts to\n  assign to an unlisted variable name raises ``AttributeError``. If\n  dynamic assignment of new variables is desired, then add\n  ``\'__dict__\'`` to the sequence of strings in the *__slots__*\n  declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n  *__slots__* do not support weak references to its instances. If weak\n  reference support is needed, then add ``\'__weakref__\'`` to the\n  sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n  descriptors (*Implementing Descriptors*) for each variable name.  As\n  a result, class attributes cannot be used to set default values for\n  instance variables defined by *__slots__*; otherwise, the class\n  attribute would overwrite the descriptor assignment.\n\n* If a class defines a slot also defined in a base class, the instance\n  variable defined by the base class slot is inaccessible (except by\n  retrieving its descriptor directly from the base class). This\n  renders the meaning of the program undefined.  In the future, a\n  check may be added to prevent this.\n\n* The action of a *__slots__* declaration is limited to the class\n  where it is defined.  As a result, subclasses will have a *__dict__*\n  unless they also define *__slots__*.\n\n* Nonempty *__slots__* does not work for classes derived from\n  "variable-length" built-in types such as ``int``, ``str`` and\n  ``tuple``.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n  also be used; however, in the future, special meaning may be\n  assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n  *__slots__*.\n',
  'attribute-references': '\nAttribute references\n********************\n\nAn attribute reference is a primary followed by a period and a name:\n\n   attributeref ::= primary "." identifier\n\nThe primary must evaluate to an object of a type that supports\nattribute references, which most objects do.  This object is then\nasked to produce the attribute whose name is the identifier (which can\nbe customized by overriding the ``__getattr__()`` method).  If this\nattribute is not available, the exception ``AttributeError`` is\nraised.  Otherwise, the type and value of the object produced is\ndetermined by the object.  Multiple evaluations of the same attribute\nreference may yield different objects.\n',
  'augassign': '\nAugmented assignment statements\n*******************************\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n   augmented_assignment_stmt ::= target augop (expression_list | yield_expression)\n   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n             | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions for the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like ``x += 1`` can be rewritten as\n``x = x + 1`` to achieve a similar, but not exactly equal effect. In\nthe augmented version, ``x`` is only evaluated once. Also, when\npossible, the actual operation is performed *in-place*, meaning that\nrather than creating a new object and assigning that to the target,\nthe old object is modified instead.\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the initial value is\nretrieved with a ``getattr()`` and the result is assigned with a\n``setattr()``.  Notice that the two methods do not necessarily refer\nto the same variable.  When ``getattr()`` refers to a class variable,\n``setattr()`` still writes to an instance variable. For example:\n\n   class A:\n       x = 3    # class variable\n   a = A()\n   a.x += 1     # writes a.x as 4 leaving A.x as 3\n',
  'binary': '\nBinary arithmetic operations\n****************************\n\nThe binary arithmetic operations have the conventional priority\nlevels.  Note that some of these operations also apply to certain non-\nnumeric types.  Apart from the power operator, there are only two\nlevels, one for multiplicative operators and one for additive\noperators:\n\n   m_expr ::= u_expr | m_expr "*" u_expr | m_expr "//" u_expr | m_expr "/" u_expr\n              | m_expr "%" u_expr\n   a_expr ::= m_expr | a_expr "+" m_expr | a_expr "-" m_expr\n\nThe ``*`` (multiplication) operator yields the product of its\narguments.  The arguments must either both be numbers, or one argument\nmust be an integer and the other must be a sequence. In the former\ncase, the numbers are converted to a common type and then multiplied\ntogether.  In the latter case, sequence repetition is performed; a\nnegative repetition factor yields an empty sequence.\n\nThe ``/`` (division) and ``//`` (floor division) operators yield the\nquotient of their arguments.  The numeric arguments are first\nconverted to a common type. Integer division yields a float, while\nfloor division of integers results in an integer; the result is that\nof mathematical division with the \'floor\' function applied to the\nresult.  Division by zero raises the ``ZeroDivisionError`` exception.\n\nThe ``%`` (modulo) operator yields the remainder from the division of\nthe first argument by the second.  The numeric arguments are first\nconverted to a common type.  A zero right argument raises the\n``ZeroDivisionError`` exception.  The arguments may be floating point\nnumbers, e.g., ``3.14%0.7`` equals ``0.34`` (since ``3.14`` equals\n``4*0.7 + 0.34``.)  The modulo operator always yields a result with\nthe same sign as its second operand (or zero); the absolute value of\nthe result is strictly smaller than the absolute value of the second\noperand [1].\n\nThe floor division and modulo operators are connected by the following\nidentity: ``x == (x//y)*y + (x%y)``.  Floor division and modulo are\nalso connected with the built-in function ``divmod()``: ``divmod(x, y)\n== (x//y, x%y)``. [2].\n\nIn addition to performing the modulo operation on numbers, the ``%``\noperator is also overloaded by string objects to perform old-style\nstring formatting (also known as interpolation).  The syntax for\nstring formatting is described in the Python Library Reference,\nsection *Old String Formatting Operations*.\n\nThe floor division operator, the modulo operator, and the ``divmod()``\nfunction are not defined for complex numbers.  Instead, convert to a\nfloating point number using the ``abs()`` function if appropriate.\n\nThe ``+`` (addition) operator yields the sum of its arguments.  The\narguments must either both be numbers or both sequences of the same\ntype.  In the former case, the numbers are converted to a common type\nand then added together.  In the latter case, the sequences are\nconcatenated.\n\nThe ``-`` (subtraction) operator yields the difference of its\narguments.  The numeric arguments are first converted to a common\ntype.\n',
@@ -23,7 +23,7 @@ topics = {'assert': '\nThe ``assert`` statement\n************************\n\nAss
  'context-managers': '\nWith Statement Context Managers\n*******************************\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a ``with`` statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code.  Context managers are\nnormally invoked using the ``with`` statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n   Enter the runtime context related to this object. The ``with``\n   statement will bind this method\'s return value to the target(s)\n   specified in the ``as`` clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n   Exit the runtime context related to this object. The parameters\n   describe the exception that caused the context to be exited. If the\n   context was exited without an exception, all three arguments will\n   be ``None``.\n\n   If an exception is supplied, and the method wishes to suppress the\n   exception (i.e., prevent it from being propagated), it should\n   return a true value. Otherwise, the exception will be processed\n   normally upon exit from this method.\n\n   Note that ``__exit__()`` methods should not reraise the passed-in\n   exception; this is the caller\'s responsibility.\n\nSee also:\n\n   **PEP 0343** - The "with" statement\n      The specification, background, and examples for the Python\n      ``with`` statement.\n',
  'continue': '\nThe ``continue`` statement\n**************************\n\n   continue_stmt ::= "continue"\n\n``continue`` may only occur syntactically nested in a ``for`` or\n``while`` loop, but not nested in a function or class definition or\n``finally`` clause within that loop.  It continues with the next cycle\nof the nearest enclosing loop.\n\nWhen ``continue`` passes control out of a ``try`` statement with a\n``finally`` clause, that ``finally`` clause is executed before really\nstarting the next loop cycle.\n',
  'conversions': '\nArithmetic conversions\n**********************\n\nWhen a description of an arithmetic operator below uses the phrase\n"the numeric arguments are converted to a common type," this means\nthat the operator implementation for built-in types works that way:\n\n* If either argument is a complex number, the other is converted to\n  complex;\n\n* otherwise, if either argument is a floating point number, the other\n  is converted to floating point;\n\n* otherwise, both must be integers and no conversion is necessary.\n\nSome additional rules apply for certain operators (e.g., a string left\nargument to the \'%\' operator).  Extensions must define their own\nconversion behavior.\n',
- 'customization': '\nBasic customization\n*******************\n\nobject.__new__(cls[, ...])\n\n   Called to create a new instance of class *cls*.  ``__new__()`` is a\n   static method (special-cased so you need not declare it as such)\n   that takes the class of which an instance was requested as its\n   first argument.  The remaining arguments are those passed to the\n   object constructor expression (the call to the class).  The return\n   value of ``__new__()`` should be the new object instance (usually\n   an instance of *cls*).\n\n   Typical implementations create a new instance of the class by\n   invoking the superclass\'s ``__new__()`` method using\n   ``super(currentclass, cls).__new__(cls[, ...])`` with appropriate\n   arguments and then modifying the newly-created instance as\n   necessary before returning it.\n\n   If ``__new__()`` returns an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will be invoked like\n   ``__init__(self[, ...])``, where *self* is the new instance and the\n   remaining arguments are the same as were passed to ``__new__()``.\n\n   If ``__new__()`` does not return an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will not be invoked.\n\n   ``__new__()`` is intended mainly to allow subclasses of immutable\n   types (like int, str, or tuple) to customize instance creation.  It\n   is also commonly overridden in custom metaclasses in order to\n   customize class creation.\n\nobject.__init__(self[, ...])\n\n   Called when the instance is created.  The arguments are those\n   passed to the class constructor expression.  If a base class has an\n   ``__init__()`` method, the derived class\'s ``__init__()`` method,\n   if any, must explicitly call it to ensure proper initialization of\n   the base class part of the instance; for example:\n   ``BaseClass.__init__(self, [args...])``.  As a special constraint\n   on constructors, no value may be returned; doing so will cause a\n   ``TypeError`` to be raised at runtime.\n\nobject.__del__(self)\n\n   Called when the instance is about to be destroyed.  This is also\n   called a destructor.  If a base class has a ``__del__()`` method,\n   the derived class\'s ``__del__()`` method, if any, must explicitly\n   call it to ensure proper deletion of the base class part of the\n   instance.  Note that it is possible (though not recommended!) for\n   the ``__del__()`` method to postpone destruction of the instance by\n   creating a new reference to it.  It may then be called at a later\n   time when this new reference is deleted.  It is not guaranteed that\n   ``__del__()`` methods are called for objects that still exist when\n   the interpreter exits.\n\n   Note: ``del x`` doesn\'t directly call ``x.__del__()`` --- the former\n     decrements the reference count for ``x`` by one, and the latter\n     is only called when ``x``\'s reference count reaches zero.  Some\n     common situations that may prevent the reference count of an\n     object from going to zero include: circular references between\n     objects (e.g., a doubly-linked list or a tree data structure with\n     parent and child pointers); a reference to the object on the\n     stack frame of a function that caught an exception (the traceback\n     stored in ``sys.exc_info()[2]`` keeps the stack frame alive); or\n     a reference to the object on the stack frame that raised an\n     unhandled exception in interactive mode (the traceback stored in\n     ``sys.last_traceback`` keeps the stack frame alive).  The first\n     situation can only be remedied by explicitly breaking the cycles;\n     the latter two situations can be resolved by storing ``None`` in\n     ``sys.last_traceback``. Circular references which are garbage are\n     detected when the option cycle detector is enabled (it\'s on by\n     default), but can only be cleaned up if there are no Python-\n     level ``__del__()`` methods involved. Refer to the documentation\n     for the ``gc`` module for more information about how\n     ``__del__()`` methods are handled by the cycle detector,\n     particularly the description of the ``garbage`` value.\n\n   Warning: Due to the precarious circumstances under which ``__del__()``\n     methods are invoked, exceptions that occur during their execution\n     are ignored, and a warning is printed to ``sys.stderr`` instead.\n     Also, when ``__del__()`` is invoked in response to a module being\n     deleted (e.g., when execution of the program is done), other\n     globals referenced by the ``__del__()`` method may already have\n     been deleted.  For this reason, ``__del__()`` methods should do\n     the absolute minimum needed to maintain external invariants.\n     Starting with version 1.5, Python guarantees that globals whose\n     name begins with a single underscore are deleted from their\n     module before other globals are deleted; if no other references\n     to such globals exist, this may help in assuring that imported\n     modules are still available at the time when the ``__del__()``\n     method is called.\n\nobject.__repr__(self)\n\n   Called by the ``repr()`` built-in function and by string\n   conversions (reverse quotes) to compute the "official" string\n   representation of an object.  If at all possible, this should look\n   like a valid Python expression that could be used to recreate an\n   object with the same value (given an appropriate environment).  If\n   this is not possible, a string of the form ``<...some useful\n   description...>`` should be returned.  The return value must be a\n   string object. If a class defines ``__repr__()`` but not\n   ``__str__()``, then ``__repr__()`` is also used when an "informal"\n   string representation of instances of that class is required.\n\n   This is typically used for debugging, so it is important that the\n   representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n   Called by the ``str()`` built-in function and by the ``print()``\n   function to compute the "informal" string representation of an\n   object.  This differs from ``__repr__()`` in that it does not have\n   to be a valid Python expression: a more convenient or concise\n   representation may be used instead. The return value must be a\n   string object.\n\nobject.__format__(self, format_spec)\n\n   Called by the ``format()`` built-in function (and by extension, the\n   ``format()`` method of class ``str``) to produce a "formatted"\n   string representation of an object. The ``format_spec`` argument is\n   a string that contains a description of the formatting options\n   desired. The interpretation of the ``format_spec`` argument is up\n   to the type implementing ``__format__()``, however most classes\n   will either delegate formatting to one of the built-in types, or\n   use a similar formatting option syntax.\n\n   See *Format Specification Mini-Language* for a description of the\n   standard formatting syntax.\n\n   The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n   These are the so-called "rich comparison" methods. The\n   correspondence between operator symbols and method names is as\n   follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls\n   ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` calls\n   ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and ``x>=y`` calls\n   ``x.__ge__(y)``.\n\n   A rich comparison method may return the singleton\n   ``NotImplemented`` if it does not implement the operation for a\n   given pair of arguments. By convention, ``False`` and ``True`` are\n   returned for a successful comparison. However, these methods can\n   return any value, so if the comparison operator is used in a\n   Boolean context (e.g., in the condition of an ``if`` statement),\n   Python will call ``bool()`` on the value to determine if the result\n   is true or false.\n\n   There are no implied relationships among the comparison operators.\n   The truth of ``x==y`` does not imply that ``x!=y`` is false.\n   Accordingly, when defining ``__eq__()``, one should also define\n   ``__ne__()`` so that the operators will behave as expected.  See\n   the paragraph on ``__hash__()`` for some important notes on\n   creating *hashable* objects which support custom comparison\n   operations and are usable as dictionary keys.\n\n   There are no swapped-argument versions of these methods (to be used\n   when the left argument does not support the operation but the right\n   argument does); rather, ``__lt__()`` and ``__gt__()`` are each\n   other\'s reflection, ``__le__()`` and ``__ge__()`` are each other\'s\n   reflection, and ``__eq__()`` and ``__ne__()`` are their own\n   reflection.\n\n   Arguments to rich comparison methods are never coerced.\n\nobject.__hash__(self)\n\n   Called for the key object for dictionary operations, and by the\n   built-in function ``hash()``.  Should return an integer usable as a\n   hash value for dictionary operations.  The only required property\n   is that objects which compare equal have the same hash value; it is\n   advised to somehow mix together (e.g., using exclusive or) the hash\n   values for the components of the object that also play a part in\n   comparison of objects.\n\n   If a class does not define an ``__eq__()`` method it should not\n   define a ``__hash__()`` operation either; if it defines\n   ``__eq__()`` but not ``__hash__()``, its instances will not be\n   usable as dictionary keys.  If a class defines mutable objects and\n   implements an ``__eq__()`` method, it should not implement\n   ``__hash__()``, since the dictionary implementation requires that a\n   key\'s hash value is immutable (if the object\'s hash value changes,\n   it will be in the wrong hash bucket).\n\n   User-defined classes have ``__eq__()`` and ``__hash__()`` methods\n   by default; with them, all objects compare unequal (except with\n   themselves) and ``x.__hash__()`` returns ``id(x)``.\n\n   Classes which inherit a ``__hash__()`` method from a parent class\n   but change the meaning of ``__eq__()`` such that the hash value\n   returned is no longer appropriate (e.g. by switching to a value-\n   based concept of equality instead of the default identity based\n   equality) can explicitly flag themselves as being unhashable by\n   setting ``__hash__ = None`` in the class definition. Doing so means\n   that not only will instances of the class raise an appropriate\n   ``TypeError`` when a program attempts to retrieve their hash value,\n   but they will also be correctly identified as unhashable when\n   checking ``isinstance(obj, collections.Hashable)`` (unlike classes\n   which define their own ``__hash__()`` to explicitly raise\n   ``TypeError``).\n\n   If a class that overrrides ``__eq__()`` needs to retain the\n   implementation of ``__hash__()`` from a parent class, the\n   interpreter must be told this explicitly by setting ``__hash__ =\n   <ParentClass>.__hash__``. Otherwise the inheritance of\n   ``__hash__()`` will be blocked, just as if ``__hash__`` had been\n   explicitly set to ``None``.\n\nobject.__bool__(self)\n\n   Called to implement truth value testing, and the built-in operation\n   ``bool()``; should return ``False`` or ``True``. When this method\n   is not defined, ``__len__()`` is called, if it is defined (see\n   below) and ``True`` is returned when the length is not zero.  If a\n   class defines neither ``__len__()`` nor ``__bool__()``, all its\n   instances are considered true.\n',
+ 'customization': '\nBasic customization\n*******************\n\nobject.__new__(cls[, ...])\n\n   Called to create a new instance of class *cls*.  ``__new__()`` is a\n   static method (special-cased so you need not declare it as such)\n   that takes the class of which an instance was requested as its\n   first argument.  The remaining arguments are those passed to the\n   object constructor expression (the call to the class).  The return\n   value of ``__new__()`` should be the new object instance (usually\n   an instance of *cls*).\n\n   Typical implementations create a new instance of the class by\n   invoking the superclass\'s ``__new__()`` method using\n   ``super(currentclass, cls).__new__(cls[, ...])`` with appropriate\n   arguments and then modifying the newly-created instance as\n   necessary before returning it.\n\n   If ``__new__()`` returns an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will be invoked like\n   ``__init__(self[, ...])``, where *self* is the new instance and the\n   remaining arguments are the same as were passed to ``__new__()``.\n\n   If ``__new__()`` does not return an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will not be invoked.\n\n   ``__new__()`` is intended mainly to allow subclasses of immutable\n   types (like int, str, or tuple) to customize instance creation.  It\n   is also commonly overridden in custom metaclasses in order to\n   customize class creation.\n\nobject.__init__(self[, ...])\n\n   Called when the instance is created.  The arguments are those\n   passed to the class constructor expression.  If a base class has an\n   ``__init__()`` method, the derived class\'s ``__init__()`` method,\n   if any, must explicitly call it to ensure proper initialization of\n   the base class part of the instance; for example:\n   ``BaseClass.__init__(self, [args...])``.  As a special constraint\n   on constructors, no value may be returned; doing so will cause a\n   ``TypeError`` to be raised at runtime.\n\nobject.__del__(self)\n\n   Called when the instance is about to be destroyed.  This is also\n   called a destructor.  If a base class has a ``__del__()`` method,\n   the derived class\'s ``__del__()`` method, if any, must explicitly\n   call it to ensure proper deletion of the base class part of the\n   instance.  Note that it is possible (though not recommended!) for\n   the ``__del__()`` method to postpone destruction of the instance by\n   creating a new reference to it.  It may then be called at a later\n   time when this new reference is deleted.  It is not guaranteed that\n   ``__del__()`` methods are called for objects that still exist when\n   the interpreter exits.\n\n   Note: ``del x`` doesn\'t directly call ``x.__del__()`` --- the former\n     decrements the reference count for ``x`` by one, and the latter\n     is only called when ``x``\'s reference count reaches zero.  Some\n     common situations that may prevent the reference count of an\n     object from going to zero include: circular references between\n     objects (e.g., a doubly-linked list or a tree data structure with\n     parent and child pointers); a reference to the object on the\n     stack frame of a function that caught an exception (the traceback\n     stored in ``sys.exc_info()[2]`` keeps the stack frame alive); or\n     a reference to the object on the stack frame that raised an\n     unhandled exception in interactive mode (the traceback stored in\n     ``sys.last_traceback`` keeps the stack frame alive).  The first\n     situation can only be remedied by explicitly breaking the cycles;\n     the latter two situations can be resolved by storing ``None`` in\n     ``sys.last_traceback``. Circular references which are garbage are\n     detected when the option cycle detector is enabled (it\'s on by\n     default), but can only be cleaned up if there are no Python-\n     level ``__del__()`` methods involved. Refer to the documentation\n     for the ``gc`` module for more information about how\n     ``__del__()`` methods are handled by the cycle detector,\n     particularly the description of the ``garbage`` value.\n\n   Warning: Due to the precarious circumstances under which ``__del__()``\n     methods are invoked, exceptions that occur during their execution\n     are ignored, and a warning is printed to ``sys.stderr`` instead.\n     Also, when ``__del__()`` is invoked in response to a module being\n     deleted (e.g., when execution of the program is done), other\n     globals referenced by the ``__del__()`` method may already have\n     been deleted.  For this reason, ``__del__()`` methods should do\n     the absolute minimum needed to maintain external invariants.\n     Starting with version 1.5, Python guarantees that globals whose\n     name begins with a single underscore are deleted from their\n     module before other globals are deleted; if no other references\n     to such globals exist, this may help in assuring that imported\n     modules are still available at the time when the ``__del__()``\n     method is called.\n\nobject.__repr__(self)\n\n   Called by the ``repr()`` built-in function to compute the\n   "official" string representation of an object.  If at all possible,\n   this should look like a valid Python expression that could be used\n   to recreate an object with the same value (given an appropriate\n   environment).  If this is not possible, a string of the form\n   ``<...some useful description...>`` should be returned. The return\n   value must be a string object. If a class defines ``__repr__()``\n   but not ``__str__()``, then ``__repr__()`` is also used when an\n   "informal" string representation of instances of that class is\n   required.\n\n   This is typically used for debugging, so it is important that the\n   representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n   Called by the ``str()`` built-in function and by the ``print()``\n   function to compute the "informal" string representation of an\n   object.  This differs from ``__repr__()`` in that it does not have\n   to be a valid Python expression: a more convenient or concise\n   representation may be used instead. The return value must be a\n   string object.\n\nobject.__format__(self, format_spec)\n\n   Called by the ``format()`` built-in function (and by extension, the\n   ``format()`` method of class ``str``) to produce a "formatted"\n   string representation of an object. The ``format_spec`` argument is\n   a string that contains a description of the formatting options\n   desired. The interpretation of the ``format_spec`` argument is up\n   to the type implementing ``__format__()``, however most classes\n   will either delegate formatting to one of the built-in types, or\n   use a similar formatting option syntax.\n\n   See *Format Specification Mini-Language* for a description of the\n   standard formatting syntax.\n\n   The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n   These are the so-called "rich comparison" methods. The\n   correspondence between operator symbols and method names is as\n   follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls\n   ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` calls\n   ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and ``x>=y`` calls\n   ``x.__ge__(y)``.\n\n   A rich comparison method may return the singleton\n   ``NotImplemented`` if it does not implement the operation for a\n   given pair of arguments. By convention, ``False`` and ``True`` are\n   returned for a successful comparison. However, these methods can\n   return any value, so if the comparison operator is used in a\n   Boolean context (e.g., in the condition of an ``if`` statement),\n   Python will call ``bool()`` on the value to determine if the result\n   is true or false.\n\n   There are no implied relationships among the comparison operators.\n   The truth of ``x==y`` does not imply that ``x!=y`` is false.\n   Accordingly, when defining ``__eq__()``, one should also define\n   ``__ne__()`` so that the operators will behave as expected.  See\n   the paragraph on ``__hash__()`` for some important notes on\n   creating *hashable* objects which support custom comparison\n   operations and are usable as dictionary keys.\n\n   There are no swapped-argument versions of these methods (to be used\n   when the left argument does not support the operation but the right\n   argument does); rather, ``__lt__()`` and ``__gt__()`` are each\n   other\'s reflection, ``__le__()`` and ``__ge__()`` are each other\'s\n   reflection, and ``__eq__()`` and ``__ne__()`` are their own\n   reflection.\n\n   Arguments to rich comparison methods are never coerced.\n\nobject.__hash__(self)\n\n   Called for the key object for dictionary operations, and by the\n   built-in function ``hash()``.  Should return an integer usable as a\n   hash value for dictionary operations.  The only required property\n   is that objects which compare equal have the same hash value; it is\n   advised to somehow mix together (e.g., using exclusive or) the hash\n   values for the components of the object that also play a part in\n   comparison of objects.\n\n   If a class does not define an ``__eq__()`` method it should not\n   define a ``__hash__()`` operation either; if it defines\n   ``__eq__()`` but not ``__hash__()``, its instances will not be\n   usable as dictionary keys.  If a class defines mutable objects and\n   implements an ``__eq__()`` method, it should not implement\n   ``__hash__()``, since the dictionary implementation requires that a\n   key\'s hash value is immutable (if the object\'s hash value changes,\n   it will be in the wrong hash bucket).\n\n   User-defined classes have ``__eq__()`` and ``__hash__()`` methods\n   by default; with them, all objects compare unequal (except with\n   themselves) and ``x.__hash__()`` returns ``id(x)``.\n\n   Classes which inherit a ``__hash__()`` method from a parent class\n   but change the meaning of ``__eq__()`` such that the hash value\n   returned is no longer appropriate (e.g. by switching to a value-\n   based concept of equality instead of the default identity based\n   equality) can explicitly flag themselves as being unhashable by\n   setting ``__hash__ = None`` in the class definition. Doing so means\n   that not only will instances of the class raise an appropriate\n   ``TypeError`` when a program attempts to retrieve their hash value,\n   but they will also be correctly identified as unhashable when\n   checking ``isinstance(obj, collections.Hashable)`` (unlike classes\n   which define their own ``__hash__()`` to explicitly raise\n   ``TypeError``).\n\n   If a class that overrrides ``__eq__()`` needs to retain the\n   implementation of ``__hash__()`` from a parent class, the\n   interpreter must be told this explicitly by setting ``__hash__ =\n   <ParentClass>.__hash__``. Otherwise the inheritance of\n   ``__hash__()`` will be blocked, just as if ``__hash__`` had been\n   explicitly set to ``None``.\n\nobject.__bool__(self)\n\n   Called to implement truth value testing, and the built-in operation\n   ``bool()``; should return ``False`` or ``True``. When this method\n   is not defined, ``__len__()`` is called, if it is defined (see\n   below) and ``True`` is returned when the length is not zero.  If a\n   class defines neither ``__len__()`` nor ``__bool__()``, all its\n   instances are considered true.\n',
  'debugger': '\n``pdb`` --- The Python Debugger\n*******************************\n\nThe module ``pdb`` defines an interactive source code debugger for\nPython programs.  It supports setting (conditional) breakpoints and\nsingle stepping at the source line level, inspection of stack frames,\nsource code listing, and evaluation of arbitrary Python code in the\ncontext of any stack frame.  It also supports post-mortem debugging\nand can be called under program control.\n\nThe debugger is extensible --- it is actually defined as the class\n``Pdb``. This is currently undocumented but easily understood by\nreading the source.  The extension interface uses the modules ``bdb``\n(undocumented) and ``cmd``.\n\nThe debugger\'s prompt is ``(Pdb)``. Typical usage to run a program\nunder control of the debugger is:\n\n   >>> import pdb\n   >>> import mymodule\n   >>> pdb.run(\'mymodule.test()\')\n   > <string>(0)?()\n   (Pdb) continue\n   > <string>(1)?()\n   (Pdb) continue\n   NameError: \'spam\'\n   > <string>(1)?()\n   (Pdb)\n\n``pdb.py`` can also be invoked as a script to debug other scripts.\nFor example:\n\n   python -m pdb myscript.py\n\nWhen invoked as a script, pdb will automatically enter post-mortem\ndebugging if the program being debugged exits abnormally. After post-\nmortem debugging (or after normal exit of the program), pdb will\nrestart the program. Automatic restarting preserves pdb\'s state (such\nas breakpoints) and in most cases is more useful than quitting the\ndebugger upon program\'s exit.\n\nTypical usage to inspect a crashed program is:\n\n   >>> import pdb\n   >>> import mymodule\n   >>> mymodule.test()\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in ?\n     File "./mymodule.py", line 4, in test\n       test2()\n     File "./mymodule.py", line 3, in test2\n       print(spam)\n   NameError: spam\n   >>> pdb.pm()\n   > ./mymodule.py(3)test2()\n   -> print(spam)\n   (Pdb)\n\nThe module defines the following functions; each enters the debugger\nin a slightly different way:\n\npdb.run(statement[, globals[, locals]])\n\n   Execute the *statement* (given as a string) under debugger control.\n   The debugger prompt appears before any code is executed; you can\n   set breakpoints and type ``continue``, or you can step through the\n   statement using ``step`` or ``next`` (all these commands are\n   explained below).  The optional *globals* and *locals* arguments\n   specify the environment in which the code is executed; by default\n   the dictionary of the module ``__main__`` is used.  (See the\n   explanation of the built-in ``exec()`` or ``eval()`` functions.)\n\npdb.runeval(expression[, globals[, locals]])\n\n   Evaluate the *expression* (given as a string) under debugger\n   control.  When ``runeval()`` returns, it returns the value of the\n   expression.  Otherwise this function is similar to ``run()``.\n\npdb.runcall(function[, argument, ...])\n\n   Call the *function* (a function or method object, not a string)\n   with the given arguments.  When ``runcall()`` returns, it returns\n   whatever the function call returned.  The debugger prompt appears\n   as soon as the function is entered.\n\npdb.set_trace()\n\n   Enter the debugger at the calling stack frame.  This is useful to\n   hard-code a breakpoint at a given point in a program, even if the\n   code is not otherwise being debugged (e.g. when an assertion\n   fails).\n\npdb.post_mortem([traceback])\n\n   Enter post-mortem debugging of the given *traceback* object.  If no\n   *traceback* is given, it uses the one of the exception that is\n   currently being handled (an exception must be being handled if the\n   default is to be used).\n\npdb.pm()\n\n   Enter post-mortem debugging of the traceback found in\n   ``sys.last_traceback``.\n',
  'del': '\nThe ``del`` statement\n*********************\n\n   del_stmt ::= "del" target_list\n\nDeletion is recursively defined very similar to the way assignment is\ndefined. Rather that spelling it out in full details, here are some\nhints.\n\nDeletion of a target list recursively deletes each target, from left\nto right.\n\nDeletion of a name removes the binding of that name from the local or\nglobal namespace, depending on whether the name occurs in a ``global``\nstatement in the same code block.  If the name is unbound, a\n``NameError`` exception will be raised.\n\nIt is illegal to delete a name from the local namespace if it occurs\nas a free variable in a nested block.\n\nDeletion of attribute references, subscriptions and slicings is passed\nto the primary object involved; deletion of a slicing is in general\nequivalent to assignment of an empty slice of the right type (but even\nthis is determined by the sliced object).\n',
  'dict': '\nDictionary displays\n*******************\n\nA dictionary display is a possibly empty series of key/datum pairs\nenclosed in curly braces:\n\n   dict_display       ::= "{" [key_datum_list | dict_comprehension] "}"\n   key_datum_list     ::= key_datum ("," key_datum)* [","]\n   key_datum          ::= expression ":" expression\n   dict_comprehension ::= expression ":" expression comp_for\n\nA dictionary display yields a new dictionary object.\n\nIf a comma-separated sequence of key/datum pairs is given, they are\nevaluated from left to right to define the entries of the dictionary:\neach key object is used as a key into the dictionary to store the\ncorresponding datum.  This means that you can specify the same key\nmultiple times in the key/datum list, and the final dictionary\'s value\nfor that key will be the last one given.\n\nA dict comprehension, in contrast to list and set comprehensions,\nneeds two expressions separated with a colon followed by the usual\n"for" and "if" clauses. When the comprehension is run, the resulting\nkey and value elements are inserted in the new dictionary in the order\nthey are produced.\n\nRestrictions on the types of the key values are listed earlier in\nsection *The standard type hierarchy*.  (To summarize, the key type\nshould be *hashable*, which excludes all mutable objects.)  Clashes\nbetween duplicate keys are not detected; the last datum (textually\nrightmost in the display) stored for a given key value prevails.\n',
@@ -41,7 +41,7 @@ topics = {'assert': '\nThe ``assert`` statement\n************************\n\nAss
  'identifiers': '\nIdentifiers and keywords\n************************\n\nIdentifiers (also referred to as *names*) are described by the\nfollowing lexical definitions.\n\nThe syntax of identifiers in Python is based on the Unicode standard\nannex UAX-31, with elaboration and changes as defined below; see also\n**PEP 3131** for further details.\n\nWithin the ASCII range (U+0001..U+007F), the valid characters for\nidentifiers are the same as in Python 2.x: the uppercase and lowercase\nletters ``A`` through ``Z``, the underscore ``_`` and, except for the\nfirst character, the digits ``0`` through ``9``.\n\nPython 3.0 introduces additional characters from outside the ASCII\nrange (see **PEP 3131**).  For these characters, the classification\nuses the version of the Unicode Character Database as included in the\n``unicodedata`` module.\n\nIdentifiers are unlimited in length.  Case is significant.\n\n   identifier  ::= id_start id_continue*\n   id_start    ::= <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>\n   id_continue ::= <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>\n\nThe Unicode category codes mentioned above stand for:\n\n* *Lu* - uppercase letters\n\n* *Ll* - lowercase letters\n\n* *Lt* - titlecase letters\n\n* *Lm* - modifier letters\n\n* *Lo* - other letters\n\n* *Nl* - letter numbers\n\n* *Mn* - nonspacing marks\n\n* *Mc* - spacing combining marks\n\n* *Nd* - decimal numbers\n\n* *Pc* - connector punctuations\n\nAll identifiers are converted into the normal form NFC while parsing;\ncomparison of identifiers is based on NFC.\n\nA non-normative HTML file listing all valid identifier characters for\nUnicode 4.1 can be found at http://www.dcl.hpi.uni-\npotsdam.de/home/loewis/table-3131.html.\n\n\nKeywords\n========\n\nThe following identifiers are used as reserved words, or *keywords* of\nthe language, and cannot be used as ordinary identifiers.  They must\nbe spelled exactly as written here:\n\n   False      class      finally    is         return\n   None       continue   for        lambda     try\n   True       def        from       nonlocal   while\n   and        del        global     not        with\n   as         elif       if         or         yield\n   assert     else       import     pass\n   break      except     in         raise\n\n\nReserved classes of identifiers\n===============================\n\nCertain classes of identifiers (besides keywords) have special\nmeanings.  These classes are identified by the patterns of leading and\ntrailing underscore characters:\n\n``_*``\n   Not imported by ``from module import *``.  The special identifier\n   ``_`` is used in the interactive interpreter to store the result of\n   the last evaluation; it is stored in the ``builtins`` module.  When\n   not in interactive mode, ``_`` has no special meaning and is not\n   defined. See section *The import statement*.\n\n   Note: The name ``_`` is often used in conjunction with\n     internationalization; refer to the documentation for the\n     ``gettext`` module for more information on this convention.\n\n``__*__``\n   System-defined names.  These names are defined by the interpreter\n   and its implementation (including the standard library);\n   applications should not expect to define additional names using\n   this convention.  The set of names of this class defined by Python\n   may be extended in future versions. See section *Special method\n   names*.\n\n``__*``\n   Class-private names.  Names in this category, when used within the\n   context of a class definition, are re-written to use a mangled form\n   to help avoid name clashes between "private" attributes of base and\n   derived classes. See section *Identifiers (Names)*.\n',
  'if': '\nThe ``if`` statement\n********************\n\nThe ``if`` statement is used for conditional execution:\n\n   if_stmt ::= "if" expression ":" suite\n               ( "elif" expression ":" suite )*\n               ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the ``if`` statement is executed or evaluated).\nIf all expressions are false, the suite of the ``else`` clause, if\npresent, is executed.\n',
  'imaginary': '\nImaginary literals\n******************\n\nImaginary literals are described by the following lexical definitions:\n\n   imagnumber ::= (floatnumber | intpart) ("j" | "J")\n\nAn imaginary literal yields a complex number with a real part of 0.0.\nComplex numbers are represented as a pair of floating point numbers\nand have the same restrictions on their range.  To create a complex\nnumber with a nonzero real part, add a floating point number to it,\ne.g., ``(3+4j)``.  Some examples of imaginary literals:\n\n   3.14j   10.j    10j     .001j   1e100j  3.14e-10j\n',
- 'import': '\nThe ``import`` statement\n************************\n\n   import_stmt     ::= "import" module ["as" name] ( "," module ["as" name] )*\n                   | "from" relative_module "import" identifier ["as" name]\n                   ( "," identifier ["as" name] )*\n                   | "from" relative_module "import" "(" identifier ["as" name]\n                   ( "," identifier ["as" name] )* [","] ")"\n                   | "from" module "import" "*"\n   module          ::= (identifier ".")* identifier\n   relative_module ::= "."* module | "."+\n   name            ::= identifier\n\nImport statements are executed in two steps: (1) find a module, and\ninitialize it if necessary; (2) define a name or names in the local\nnamespace (of the scope where the ``import`` statement occurs).  The\nfirst form (without ``from``) repeats these steps for each identifier\nin the list.  The form with ``from`` performs step (1) once, and then\nperforms step (2) repeatedly.\n\nIn this context, to "initialize" a built-in or extension module means\nto call an initialization function that the module must provide for\nthe purpose (in the reference implementation, the function\'s name is\nobtained by prepending string "init" to the module\'s name); to\n"initialize" a Python-coded module means to execute the module\'s body.\n\nThe system maintains a table of modules that have been or are being\ninitialized, indexed by module name.  This table is accessible as\n``sys.modules``.  When a module name is found in this table, step (1)\nis finished.  If not, a search for a module definition is started.\nWhen a module is found, it is loaded.  Details of the module searching\nand loading process are implementation and platform specific.  It\ngenerally involves searching for a "built-in" module with the given\nname and then searching a list of locations given as ``sys.path``.\n\nIf a built-in module is found, its built-in initialization code is\nexecuted and step (1) is finished.  If no matching file is found,\n``ImportError`` is raised. If a file is found, it is parsed, yielding\nan executable code block.  If a syntax error occurs, ``SyntaxError``\nis raised.  Otherwise, an empty module of the given name is created\nand inserted in the module table, and then the code block is executed\nin the context of this module.  Exceptions during this execution\nterminate step (1).\n\nWhen step (1) finishes without raising an exception, step (2) can\nbegin.\n\nThe first form of ``import`` statement binds the module name in the\nlocal namespace to the module object, and then goes on to import the\nnext identifier, if any.  If the module name is followed by ``as``,\nthe name following ``as`` is used as the local name for the module.\n\nThe ``from`` form does not bind the module name: it goes through the\nlist of identifiers, looks each one of them up in the module found in\nstep (1), and binds the name in the local namespace to the object thus\nfound.  As with the first form of ``import``, an alternate local name\ncan be supplied by specifying "``as`` localname".  If a name is not\nfound, ``ImportError`` is raised.  If the list of identifiers is\nreplaced by a star (``\'*\'``), all public names defined in the module\nare bound in the local namespace of the ``import`` statement..\n\nThe *public names* defined by a module are determined by checking the\nmodule\'s namespace for a variable named ``__all__``; if defined, it\nmust be a sequence of strings which are names defined or imported by\nthat module.  The names given in ``__all__`` are all considered public\nand are required to exist.  If ``__all__`` is not defined, the set of\npublic names includes all names found in the module\'s namespace which\ndo not begin with an underscore character (``\'_\'``). ``__all__``\nshould contain the entire public API. It is intended to avoid\naccidentally exporting items that are not part of the API (such as\nlibrary modules which were imported and used within the module).\n\nThe ``from`` form with ``*`` may only occur in a module scope.  If the\nwild card form of import --- ``import *`` --- is used in a function\nand the function contains or is a nested block with free variables,\nthe compiler will raise a ``SyntaxError``.\n\n**Hierarchical module names:** when the module names contains one or\nmore dots, the module search path is carried out differently.  The\nsequence of identifiers up to the last dot is used to find a\n"package"; the final identifier is then searched inside the package.\nA package is generally a subdirectory of a directory on ``sys.path``\nthat has a file ``__init__.py``.\n\nThe built-in function ``__import__()`` is provided to support\napplications that determine which modules need to be loaded\ndynamically; refer to *Built-in Functions* for additional information.\n\n\nFuture statements\n=================\n\nA *future statement* is a directive to the compiler that a particular\nmodule should be compiled using syntax or semantics that will be\navailable in a specified future release of Python.  The future\nstatement is intended to ease migration to future versions of Python\nthat introduce incompatible changes to the language.  It allows use of\nthe new features on a per-module basis before the release in which the\nfeature becomes standard.\n\n   future_statement ::= "from" "__future__" "import" feature ["as" name]\n                        ("," feature ["as" name])*\n                        | "from" "__future__" "import" "(" feature ["as" name]\n                        ("," feature ["as" name])* [","] ")"\n   feature          ::= identifier\n   name             ::= identifier\n\nA future statement must appear near the top of the module.  The only\nlines that can appear before a future statement are:\n\n* the module docstring (if any),\n\n* comments,\n\n* blank lines, and\n\n* other future statements.\n\nThe features recognized by Python 3.0 are ``absolute_import``,\n``division``, ``generators``, ``nested_scopes`` and\n``with_statement``.  They are all redundant because they are always\nenabled, and only kept for backwards compatibility.\n\nA future statement is recognized and treated specially at compile\ntime: Changes to the semantics of core constructs are often\nimplemented by generating different code.  It may even be the case\nthat a new feature introduces new incompatible syntax (such as a new\nreserved word), in which case the compiler may need to parse the\nmodule differently.  Such decisions cannot be pushed off until\nruntime.\n\nFor any given release, the compiler knows which feature names have\nbeen defined, and raises a compile-time error if a future statement\ncontains a feature not known to it.\n\nThe direct runtime semantics are the same as for any import statement:\nthere is a standard module ``__future__``, described later, and it\nwill be imported in the usual way at the time the future statement is\nexecuted.\n\nThe interesting runtime semantics depend on the specific feature\nenabled by the future statement.\n\nNote that there is nothing special about the statement:\n\n   import __future__ [as name]\n\nThat is not a future statement; it\'s an ordinary import statement with\nno special semantics or syntax restrictions.\n\nCode compiled by calls to the builtin functions ``exec()`` and\n``compile()`` that occur in a module ``M`` containing a future\nstatement will, by default, use the new syntax or semantics associated\nwith the future statement.  This can be controlled by optional\narguments to ``compile()`` --- see the documentation of that function\nfor details.\n\nA future statement typed at an interactive interpreter prompt will\ntake effect for the rest of the interpreter session.  If an\ninterpreter is started with the *-i* option, is passed a script name\nto execute, and the script includes a future statement, it will be in\neffect in the interactive session started after the script is\nexecuted.\n',
+ 'import': '\nThe ``import`` statement\n************************\n\n   import_stmt     ::= "import" module ["as" name] ( "," module ["as" name] )*\n                   | "from" relative_module "import" identifier ["as" name]\n                   ( "," identifier ["as" name] )*\n                   | "from" relative_module "import" "(" identifier ["as" name]\n                   ( "," identifier ["as" name] )* [","] ")"\n                   | "from" module "import" "*"\n   module          ::= (identifier ".")* identifier\n   relative_module ::= "."* module | "."+\n   name            ::= identifier\n\nImport statements are executed in two steps: (1) find a module, and\ninitialize it if necessary; (2) define a name or names in the local\nnamespace (of the scope where the ``import`` statement occurs).  The\nfirst form (without ``from``) repeats these steps for each identifier\nin the list.  The form with ``from`` performs step (1) once, and then\nperforms step (2) repeatedly.\n\nIn this context, to "initialize" a built-in or extension module means\nto call an initialization function that the module must provide for\nthe purpose (in the reference implementation, the function\'s name is\nobtained by prepending string "init" to the module\'s name); to\n"initialize" a Python-coded module means to execute the module\'s body.\n\nThe system maintains a table of modules that have been or are being\ninitialized, indexed by module name.  This table is accessible as\n``sys.modules``.  When a module name is found in this table, step (1)\nis finished.  If not, a search for a module definition is started.\nWhen a module is found, it is loaded.  Details of the module searching\nand loading process are implementation and platform specific.  It\ngenerally involves searching for a "built-in" module with the given\nname and then searching a list of locations given as ``sys.path``.\n\nIf a built-in module is found, its built-in initialization code is\nexecuted and step (1) is finished.  If no matching file is found,\n``ImportError`` is raised. If a file is found, it is parsed, yielding\nan executable code block.  If a syntax error occurs, ``SyntaxError``\nis raised.  Otherwise, an empty module of the given name is created\nand inserted in the module table, and then the code block is executed\nin the context of this module.  Exceptions during this execution\nterminate step (1).\n\nWhen step (1) finishes without raising an exception, step (2) can\nbegin.\n\nThe first form of ``import`` statement binds the module name in the\nlocal namespace to the module object, and then goes on to import the\nnext identifier, if any.  If the module name is followed by ``as``,\nthe name following ``as`` is used as the local name for the module.\n\nThe ``from`` form does not bind the module name: it goes through the\nlist of identifiers, looks each one of them up in the module found in\nstep (1), and binds the name in the local namespace to the object thus\nfound.  As with the first form of ``import``, an alternate local name\ncan be supplied by specifying "``as`` localname".  If a name is not\nfound, ``ImportError`` is raised.  If the list of identifiers is\nreplaced by a star (``\'*\'``), all public names defined in the module\nare bound in the local namespace of the ``import`` statement..\n\nThe *public names* defined by a module are determined by checking the\nmodule\'s namespace for a variable named ``__all__``; if defined, it\nmust be a sequence of strings which are names defined or imported by\nthat module.  The names given in ``__all__`` are all considered public\nand are required to exist.  If ``__all__`` is not defined, the set of\npublic names includes all names found in the module\'s namespace which\ndo not begin with an underscore character (``\'_\'``). ``__all__``\nshould contain the entire public API. It is intended to avoid\naccidentally exporting items that are not part of the API (such as\nlibrary modules which were imported and used within the module).\n\nThe ``from`` form with ``*`` may only occur in a module scope.  If the\nwild card form of import --- ``import *`` --- is used in a function\nand the function contains or is a nested block with free variables,\nthe compiler will raise a ``SyntaxError``.\n\n**Hierarchical module names:** when the module names contains one or\nmore dots, the module search path is carried out differently.  The\nsequence of identifiers up to the last dot is used to find a\n"package"; the final identifier is then searched inside the package.\nA package is generally a subdirectory of a directory on ``sys.path``\nthat has a file ``__init__.py``.\n\nThe built-in function ``__import__()`` is provided to support\napplications that determine which modules need to be loaded\ndynamically; refer to *Built-in Functions* for additional information.\n\n\nFuture statements\n=================\n\nA *future statement* is a directive to the compiler that a particular\nmodule should be compiled using syntax or semantics that will be\navailable in a specified future release of Python.  The future\nstatement is intended to ease migration to future versions of Python\nthat introduce incompatible changes to the language.  It allows use of\nthe new features on a per-module basis before the release in which the\nfeature becomes standard.\n\n   future_statement ::= "from" "__future__" "import" feature ["as" name]\n                        ("," feature ["as" name])*\n                        | "from" "__future__" "import" "(" feature ["as" name]\n                        ("," feature ["as" name])* [","] ")"\n   feature          ::= identifier\n   name             ::= identifier\n\nA future statement must appear near the top of the module.  The only\nlines that can appear before a future statement are:\n\n* the module docstring (if any),\n\n* comments,\n\n* blank lines, and\n\n* other future statements.\n\nThe features recognized by Python 3.0 are ``absolute_import``,\n``division``, ``generators``, ``unicode_literals``,\n``print_function``, ``nested_scopes`` and ``with_statement``.  They\nare all redundant because they are always enabled, and only kept for\nbackwards compatibility.\n\nA future statement is recognized and treated specially at compile\ntime: Changes to the semantics of core constructs are often\nimplemented by generating different code.  It may even be the case\nthat a new feature introduces new incompatible syntax (such as a new\nreserved word), in which case the compiler may need to parse the\nmodule differently.  Such decisions cannot be pushed off until\nruntime.\n\nFor any given release, the compiler knows which feature names have\nbeen defined, and raises a compile-time error if a future statement\ncontains a feature not known to it.\n\nThe direct runtime semantics are the same as for any import statement:\nthere is a standard module ``__future__``, described later, and it\nwill be imported in the usual way at the time the future statement is\nexecuted.\n\nThe interesting runtime semantics depend on the specific feature\nenabled by the future statement.\n\nNote that there is nothing special about the statement:\n\n   import __future__ [as name]\n\nThat is not a future statement; it\'s an ordinary import statement with\nno special semantics or syntax restrictions.\n\nCode compiled by calls to the builtin functions ``exec()`` and\n``compile()`` that occur in a module ``M`` containing a future\nstatement will, by default, use the new syntax or semantics associated\nwith the future statement.  This can be controlled by optional\narguments to ``compile()`` --- see the documentation of that function\nfor details.\n\nA future statement typed at an interactive interpreter prompt will\ntake effect for the rest of the interpreter session.  If an\ninterpreter is started with the *-i* option, is passed a script name\nto execute, and the script includes a future statement, it will be in\neffect in the interactive session started after the script is\nexecuted.\n',
  'in': '\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation.  Also unlike C, expressions like ``a < b < c`` have the\ninterpretation that is conventional in mathematics:\n\n   comparison    ::= or_expr ( comp_operator or_expr )*\n   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n                     | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: ``True`` or ``False``.\n\nComparisons can be chained arbitrarily, e.g., ``x < y <= z`` is\nequivalent to ``x < y and y <= z``, except that ``y`` is evaluated\nonly once (but in both cases ``z`` is not evaluated at all when ``x <\ny`` is found to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then ``a op1 b op2 c ... y\nopN z`` is equivalent to ``a op1 b and b op2 c and ... y opN z``,\nexcept that each expression is evaluated at most once.\n\nNote that ``a op1 b op2 c`` doesn\'t imply any kind of comparison\nbetween *a* and *c*, so that, e.g., ``x < y > z`` is perfectly legal\n(though perhaps not pretty).\n\nThe operators ``<``, ``>``, ``==``, ``>=``, ``<=``, and ``!=`` compare\nthe values of two objects.  The objects need not have the same type.\nIf both are numbers, they are converted to a common type.  Otherwise,\nthe ``==`` and ``!=`` operators *always* consider objects of different\ntypes to be unequal, while the ``<``, ``>``, ``>=`` and ``<=``\noperators raise a ``TypeError`` when comparing objects of different\ntypes that do not implement these operators for the given pair of\ntypes.  You can control comparison behavior of objects of non-builtin\ntypes by defining rich comparison methods like ``__gt__()``, described\nin section *Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric equivalents\n  (the result of the built-in function ``ord()``) of their characters.\n  [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison of\n  corresponding elements.  This means that to compare equal, each\n  element must compare equal and the two sequences must be of the same\n  type and have the same length.\n\n  If not equal, the sequences are ordered the same as their first\n  differing elements.  For example, ``cmp([1,2,x], [1,2,y])`` returns\n  the same as ``cmp(x,y)``.  If the corresponding element does not\n  exist, the shorter sequence is ordered first (for example, ``[1,2] <\n  [1,2,3]``).\n\n* Mappings (dictionaries) compare equal if and only if their sorted\n  ``(key, value)`` lists compare equal. [4] Outcomes other than\n  equality are resolved consistently, but are not otherwise defined.\n  [5]\n\n* Most other objects of builtin types compare unequal unless they are\n  the same object; the choice whether one object is considered smaller\n  or larger than another one is made arbitrarily but consistently\n  within one execution of a program.\n\nThe operators ``in`` and ``not in`` test for membership.  ``x in s``\nevaluates to true if *x* is a member of *s*, and false otherwise.  ``x\nnot in s`` returns the negation of ``x in s``.  All built-in sequences\nand set types support this as well as dictionary, for which ``in``\ntests whether a the dictionary has a given key.\n\nFor the list and tuple types, ``x in y`` is true if and only if there\nexists an index *i* such that ``x == y[i]`` is true.\n\nFor the string and bytes types, ``x in y`` is true if and only if *x*\nis a substring of *y*.  An equivalent test is ``y.find(x) != -1``.\nEmpty strings are always considered to be a substring of any other\nstring, so ``"" in "abc"`` will return ``True``.\n\nFor user-defined classes which define the ``__contains__()`` method,\n``x in y`` is true if and only if ``y.__contains__(x)`` is true.\n\nFor user-defined classes which do not define ``__contains__()`` and do\ndefine ``__getitem__()``, ``x in y`` is true if and only if there is a\nnon-negative integer index *i* such that ``x == y[i]``, and all lower\ninteger indices do not raise ``IndexError`` exception.  (If any other\nexception is raised, it is as if ``in`` raised that exception).\n\nThe operator ``not in`` is defined to have the inverse true value of\n``in``.\n\nThe operators ``is`` and ``is not`` test for object identity: ``x is\ny`` is true if and only if *x* and *y* are the same object.  ``x is\nnot y`` yields the inverse truth value. [6]\n',
  'integers': '\nInteger literals\n****************\n\nInteger literals are described by the following lexical definitions:\n\n   integer        ::= decimalinteger | octinteger | hexinteger | bininteger\n   decimalinteger ::= nonzerodigit digit* | "0"+\n   nonzerodigit   ::= "1"..."9"\n   digit          ::= "0"..."9"\n   octinteger     ::= "0" ("o" | "O") octdigit+\n   hexinteger     ::= "0" ("x" | "X") hexdigit+\n   bininteger     ::= "0" ("b" | "B") bindigit+\n   octdigit       ::= "0"..."7"\n   hexdigit       ::= digit | "a"..."f" | "A"..."F"\n   bindigit       ::= "0" | "1"\n\nThere is no limit for the length of integer literals apart from what\ncan be stored in available memory.\n\nNote that leading zeros in a non-zero decimal number are not allowed.\nThis is for disambiguation with C-style octal literals, which Python\nused before version 3.0.\n\nSome examples of integer literals:\n\n   7     2147483647                        0o177    0b100110111\n   3     79228162514264337593543950336     0o377    0x100000000\n         79228162514264337593543950336              0xdeadbeef\n',
  'lambda': '\nExpression lists\n****************\n\n   expression_list ::= expression ( "," expression )* [","]\n\nAn expression list containing at least one comma yields a tuple.  The\nlength of the tuple is the number of expressions in the list.  The\nexpressions are evaluated from left to right.\n\nThe trailing comma is required only to create a single tuple (a.k.a. a\n*singleton*); it is optional in all other cases.  A single expression\nwithout a trailing comma doesn\'t create a tuple, but rather yields the\nvalue of that expression. (To create an empty tuple, use an empty pair\nof parentheses: ``()``.)\n',
@@ -59,7 +59,7 @@ topics = {'assert': '\nThe ``assert`` statement\n************************\n\nAss
  'shifting': '\nShifting operations\n*******************\n\nThe shifting operations have lower priority than the arithmetic\noperations:\n\n   shift_expr ::= a_expr | shift_expr ( "<<" | ">>" ) a_expr\n\nThese operators accept integers as arguments.  They shift the first\nargument to the left or right by the number of bits given by the\nsecond argument.\n\nA right shift by *n* bits is defined as division by ``pow(2,n)``.  A\nleft shift by *n* bits is defined as multiplication with ``pow(2,n)``.\n',
  'slicings': '\nSlicings\n********\n\nA slicing selects a range of items in a sequence object (e.g., a\nstring, tuple or list).  Slicings may be used as expressions or as\ntargets in assignment or ``del`` statements.  The syntax for a\nslicing:\n\n   slicing      ::= primary "[" slice_list "]"\n   slice_list   ::= slice_item ("," slice_item)* [","]\n   slice_item   ::= expression | proper_slice\n   proper_slice ::= [lower_bound] ":" [upper_bound] [ ":" [stride] ]\n   lower_bound  ::= expression\n   upper_bound  ::= expression\n   stride       ::= expression\n\nThere is ambiguity in the formal syntax here: anything that looks like\nan expression list also looks like a slice list, so any subscription\ncan be interpreted as a slicing.  Rather than further complicating the\nsyntax, this is disambiguated by defining that in this case the\ninterpretation as a subscription takes priority over the\ninterpretation as a slicing (this is the case if the slice list\ncontains no proper slice).\n\nThe semantics for a slicing are as follows.  The primary must evaluate\nto a mapping object, and it is indexed (using the same\n``__getitem__()`` method as normal subscription) with a key that is\nconstructed from the slice list, as follows.  If the slice list\ncontains at least one comma, the key is a tuple containing the\nconversion of the slice items; otherwise, the conversion of the lone\nslice item is the key.  The conversion of a slice item that is an\nexpression is that expression.  The conversion of a proper slice is a\nslice object (see section *The standard type hierarchy*) whose\n``start``, ``stop`` and ``step`` attributes are the values of the\nexpressions given as lower bound, upper bound and stride,\nrespectively, substituting ``None`` for missing expressions.\n',
  'specialattrs': "\nSpecial Attributes\n******************\n\nThe implementation adds a few special read-only attributes to several\nobject types, where they are relevant.  Some of these are not reported\nby the ``dir()`` built-in function.\n\nobject.__dict__\n\n   A dictionary or other mapping object used to store an object's\n   (writable) attributes.\n\ninstance.__class__\n\n   The class to which a class instance belongs.\n\nclass.__bases__\n\n   The tuple of base classes of a class object.  If there are no base\n   classes, this will be an empty tuple.\n\nclass.__name__\n\n   The name of the class or type.\n\n-[ Footnotes ]-\n\n[1] Additional information on these special methods may be found in\n    the Python Reference Manual (*Basic customization*).\n\n[2] As a consequence, the list ``[1, 2]`` is considered equal to\n    ``[1.0, 2.0]``, and similarly for tuples.\n\n[3] They must have since the parser can't tell the type of the\n    operands.\n\n[4] To format only a tuple you should therefore provide a singleton\n    tuple whose only element is the tuple to be formatted.\n\n[5] These numbers are fairly arbitrary.  They are intended to avoid\n    printing endless strings of meaningless digits without hampering\n    correct use and without having to know the exact precision of\n    floating point values on a particular machine.\n\n[6] The advantage of leaving the newline on is that returning an empty\n    string is then an unambiguous EOF indication.  It is also possible\n    (in cases where it might matter, for example, if you want to make\n    an exact copy of a file while scanning its lines) to tell whether\n    the last line of a file ended in a newline or not (yes this\n    happens!).\n",
- 'specialnames': '\nSpecial method names\n********************\n\nA class can implement certain operations that are invoked by special\nsyntax (such as arithmetic operations or subscripting and slicing) by\ndefining methods with special names. This is Python\'s approach to\n*operator overloading*, allowing classes to define their own behavior\nwith respect to language operators.  For instance, if a class defines\na method named ``__getitem__()``, and ``x`` is an instance of this\nclass, then ``x[i]`` is roughly equivalent to ``type(x).__getitem__(x,\ni)``.  Except where mentioned, attempts to execute an operation raise\nan exception when no appropriate method is defined (typically\n``AttributeError`` or ``TypeError``).\n\nWhen implementing a class that emulates any built-in type, it is\nimportant that the emulation only be implemented to the degree that it\nmakes sense for the object being modelled.  For example, some\nsequences may work well with retrieval of individual elements, but\nextracting a slice may not make sense.  (One example of this is the\n``NodeList`` interface in the W3C\'s Document Object Model.)\n\n\nBasic customization\n===================\n\nobject.__new__(cls[, ...])\n\n   Called to create a new instance of class *cls*.  ``__new__()`` is a\n   static method (special-cased so you need not declare it as such)\n   that takes the class of which an instance was requested as its\n   first argument.  The remaining arguments are those passed to the\n   object constructor expression (the call to the class).  The return\n   value of ``__new__()`` should be the new object instance (usually\n   an instance of *cls*).\n\n   Typical implementations create a new instance of the class by\n   invoking the superclass\'s ``__new__()`` method using\n   ``super(currentclass, cls).__new__(cls[, ...])`` with appropriate\n   arguments and then modifying the newly-created instance as\n   necessary before returning it.\n\n   If ``__new__()`` returns an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will be invoked like\n   ``__init__(self[, ...])``, where *self* is the new instance and the\n   remaining arguments are the same as were passed to ``__new__()``.\n\n   If ``__new__()`` does not return an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will not be invoked.\n\n   ``__new__()`` is intended mainly to allow subclasses of immutable\n   types (like int, str, or tuple) to customize instance creation.  It\n   is also commonly overridden in custom metaclasses in order to\n   customize class creation.\n\nobject.__init__(self[, ...])\n\n   Called when the instance is created.  The arguments are those\n   passed to the class constructor expression.  If a base class has an\n   ``__init__()`` method, the derived class\'s ``__init__()`` method,\n   if any, must explicitly call it to ensure proper initialization of\n   the base class part of the instance; for example:\n   ``BaseClass.__init__(self, [args...])``.  As a special constraint\n   on constructors, no value may be returned; doing so will cause a\n   ``TypeError`` to be raised at runtime.\n\nobject.__del__(self)\n\n   Called when the instance is about to be destroyed.  This is also\n   called a destructor.  If a base class has a ``__del__()`` method,\n   the derived class\'s ``__del__()`` method, if any, must explicitly\n   call it to ensure proper deletion of the base class part of the\n   instance.  Note that it is possible (though not recommended!) for\n   the ``__del__()`` method to postpone destruction of the instance by\n   creating a new reference to it.  It may then be called at a later\n   time when this new reference is deleted.  It is not guaranteed that\n   ``__del__()`` methods are called for objects that still exist when\n   the interpreter exits.\n\n   Note: ``del x`` doesn\'t directly call ``x.__del__()`` --- the former\n     decrements the reference count for ``x`` by one, and the latter\n     is only called when ``x``\'s reference count reaches zero.  Some\n     common situations that may prevent the reference count of an\n     object from going to zero include: circular references between\n     objects (e.g., a doubly-linked list or a tree data structure with\n     parent and child pointers); a reference to the object on the\n     stack frame of a function that caught an exception (the traceback\n     stored in ``sys.exc_info()[2]`` keeps the stack frame alive); or\n     a reference to the object on the stack frame that raised an\n     unhandled exception in interactive mode (the traceback stored in\n     ``sys.last_traceback`` keeps the stack frame alive).  The first\n     situation can only be remedied by explicitly breaking the cycles;\n     the latter two situations can be resolved by storing ``None`` in\n     ``sys.last_traceback``. Circular references which are garbage are\n     detected when the option cycle detector is enabled (it\'s on by\n     default), but can only be cleaned up if there are no Python-\n     level ``__del__()`` methods involved. Refer to the documentation\n     for the ``gc`` module for more information about how\n     ``__del__()`` methods are handled by the cycle detector,\n     particularly the description of the ``garbage`` value.\n\n   Warning: Due to the precarious circumstances under which ``__del__()``\n     methods are invoked, exceptions that occur during their execution\n     are ignored, and a warning is printed to ``sys.stderr`` instead.\n     Also, when ``__del__()`` is invoked in response to a module being\n     deleted (e.g., when execution of the program is done), other\n     globals referenced by the ``__del__()`` method may already have\n     been deleted.  For this reason, ``__del__()`` methods should do\n     the absolute minimum needed to maintain external invariants.\n     Starting with version 1.5, Python guarantees that globals whose\n     name begins with a single underscore are deleted from their\n     module before other globals are deleted; if no other references\n     to such globals exist, this may help in assuring that imported\n     modules are still available at the time when the ``__del__()``\n     method is called.\n\nobject.__repr__(self)\n\n   Called by the ``repr()`` built-in function and by string\n   conversions (reverse quotes) to compute the "official" string\n   representation of an object.  If at all possible, this should look\n   like a valid Python expression that could be used to recreate an\n   object with the same value (given an appropriate environment).  If\n   this is not possible, a string of the form ``<...some useful\n   description...>`` should be returned.  The return value must be a\n   string object. If a class defines ``__repr__()`` but not\n   ``__str__()``, then ``__repr__()`` is also used when an "informal"\n   string representation of instances of that class is required.\n\n   This is typically used for debugging, so it is important that the\n   representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n   Called by the ``str()`` built-in function and by the ``print()``\n   function to compute the "informal" string representation of an\n   object.  This differs from ``__repr__()`` in that it does not have\n   to be a valid Python expression: a more convenient or concise\n   representation may be used instead. The return value must be a\n   string object.\n\nobject.__format__(self, format_spec)\n\n   Called by the ``format()`` built-in function (and by extension, the\n   ``format()`` method of class ``str``) to produce a "formatted"\n   string representation of an object. The ``format_spec`` argument is\n   a string that contains a description of the formatting options\n   desired. The interpretation of the ``format_spec`` argument is up\n   to the type implementing ``__format__()``, however most classes\n   will either delegate formatting to one of the built-in types, or\n   use a similar formatting option syntax.\n\n   See *Format Specification Mini-Language* for a description of the\n   standard formatting syntax.\n\n   The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n   These are the so-called "rich comparison" methods. The\n   correspondence between operator symbols and method names is as\n   follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls\n   ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` calls\n   ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and ``x>=y`` calls\n   ``x.__ge__(y)``.\n\n   A rich comparison method may return the singleton\n   ``NotImplemented`` if it does not implement the operation for a\n   given pair of arguments. By convention, ``False`` and ``True`` are\n   returned for a successful comparison. However, these methods can\n   return any value, so if the comparison operator is used in a\n   Boolean context (e.g., in the condition of an ``if`` statement),\n   Python will call ``bool()`` on the value to determine if the result\n   is true or false.\n\n   There are no implied relationships among the comparison operators.\n   The truth of ``x==y`` does not imply that ``x!=y`` is false.\n   Accordingly, when defining ``__eq__()``, one should also define\n   ``__ne__()`` so that the operators will behave as expected.  See\n   the paragraph on ``__hash__()`` for some important notes on\n   creating *hashable* objects which support custom comparison\n   operations and are usable as dictionary keys.\n\n   There are no swapped-argument versions of these methods (to be used\n   when the left argument does not support the operation but the right\n   argument does); rather, ``__lt__()`` and ``__gt__()`` are each\n   other\'s reflection, ``__le__()`` and ``__ge__()`` are each other\'s\n   reflection, and ``__eq__()`` and ``__ne__()`` are their own\n   reflection.\n\n   Arguments to rich comparison methods are never coerced.\n\nobject.__hash__(self)\n\n   Called for the key object for dictionary operations, and by the\n   built-in function ``hash()``.  Should return an integer usable as a\n   hash value for dictionary operations.  The only required property\n   is that objects which compare equal have the same hash value; it is\n   advised to somehow mix together (e.g., using exclusive or) the hash\n   values for the components of the object that also play a part in\n   comparison of objects.\n\n   If a class does not define an ``__eq__()`` method it should not\n   define a ``__hash__()`` operation either; if it defines\n   ``__eq__()`` but not ``__hash__()``, its instances will not be\n   usable as dictionary keys.  If a class defines mutable objects and\n   implements an ``__eq__()`` method, it should not implement\n   ``__hash__()``, since the dictionary implementation requires that a\n   key\'s hash value is immutable (if the object\'s hash value changes,\n   it will be in the wrong hash bucket).\n\n   User-defined classes have ``__eq__()`` and ``__hash__()`` methods\n   by default; with them, all objects compare unequal (except with\n   themselves) and ``x.__hash__()`` returns ``id(x)``.\n\n   Classes which inherit a ``__hash__()`` method from a parent class\n   but change the meaning of ``__eq__()`` such that the hash value\n   returned is no longer appropriate (e.g. by switching to a value-\n   based concept of equality instead of the default identity based\n   equality) can explicitly flag themselves as being unhashable by\n   setting ``__hash__ = None`` in the class definition. Doing so means\n   that not only will instances of the class raise an appropriate\n   ``TypeError`` when a program attempts to retrieve their hash value,\n   but they will also be correctly identified as unhashable when\n   checking ``isinstance(obj, collections.Hashable)`` (unlike classes\n   which define their own ``__hash__()`` to explicitly raise\n   ``TypeError``).\n\n   If a class that overrrides ``__eq__()`` needs to retain the\n   implementation of ``__hash__()`` from a parent class, the\n   interpreter must be told this explicitly by setting ``__hash__ =\n   <ParentClass>.__hash__``. Otherwise the inheritance of\n   ``__hash__()`` will be blocked, just as if ``__hash__`` had been\n   explicitly set to ``None``.\n\nobject.__bool__(self)\n\n   Called to implement truth value testing, and the built-in operation\n   ``bool()``; should return ``False`` or ``True``. When this method\n   is not defined, ``__len__()`` is called, if it is defined (see\n   below) and ``True`` is returned when the length is not zero.  If a\n   class defines neither ``__len__()`` nor ``__bool__()``, all its\n   instances are considered true.\n\n\nCustomizing attribute access\n============================\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of ``x.name``)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n   Called when an attribute lookup has not found the attribute in the\n   usual places (i.e. it is not an instance attribute nor is it found\n   in the class tree for ``self``).  ``name`` is the attribute name.\n   This method should return the (computed) attribute value or raise\n   an ``AttributeError`` exception.\n\n   Note that if the attribute is found through the normal mechanism,\n   ``__getattr__()`` is not called.  (This is an intentional asymmetry\n   between ``__getattr__()`` and ``__setattr__()``.) This is done both\n   for efficiency reasons and because otherwise ``__getattr__()``\n   would have no way to access other attributes of the instance.  Note\n   that at least for instance variables, you can fake total control by\n   not inserting any values in the instance attribute dictionary (but\n   instead inserting them in another object).  See the\n   ``__getattribute__()`` method below for a way to actually get total\n   control over attribute access.\n\nobject.__getattribute__(self, name)\n\n   Called unconditionally to implement attribute accesses for\n   instances of the class. If the class also defines\n   ``__getattr__()``, the latter will not be called unless\n   ``__getattribute__()`` either calls it explicitly or raises an\n   ``AttributeError``. This method should return the (computed)\n   attribute value or raise an ``AttributeError`` exception. In order\n   to avoid infinite recursion in this method, its implementation\n   should always call the base class method with the same name to\n   access any attributes it needs, for example,\n   ``object.__getattribute__(self, name)``.\n\n   Note: This method may still be bypassed when looking up special methods\n     as the result of implicit invocation via language syntax or\n     builtin functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n   Called when an attribute assignment is attempted.  This is called\n   instead of the normal mechanism (i.e. store the value in the\n   instance dictionary). *name* is the attribute name, *value* is the\n   value to be assigned to it.\n\n   If ``__setattr__()`` wants to assign to an instance attribute, it\n   should call the base class method with the same name, for example,\n   ``object.__setattr__(self, name, value)``.\n\nobject.__delattr__(self, name)\n\n   Like ``__setattr__()`` but for attribute deletion instead of\n   assignment.  This should only be implemented if ``del obj.name`` is\n   meaningful for the object.\n\nobject.__dir__(self)\n\n   Called when ``dir()`` is called on the object.  A list must be\n   returned.\n\n\nImplementing Descriptors\n------------------------\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in the\nclass dictionary of another class, known as the *owner* class.  In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' ``__dict__``.\n\nobject.__get__(self, instance, owner)\n\n   Called to get the attribute of the owner class (class attribute\n   access) or of an instance of that class (instance attribute\n   access). *owner* is always the owner class, while *instance* is the\n   instance that the attribute was accessed through, or ``None`` when\n   the attribute is accessed through the *owner*.  This method should\n   return the (computed) attribute value or raise an\n   ``AttributeError`` exception.\n\nobject.__set__(self, instance, value)\n\n   Called to set the attribute on an instance *instance* of the owner\n   class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n   Called to delete the attribute on an instance *instance* of the\n   owner class.\n\n\nInvoking Descriptors\n--------------------\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol:  ``__get__()``, ``__set__()``, and\n``__delete__()``. If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, ``a.x`` has a\nlookup chain starting with ``a.__dict__[\'x\']``, then\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(a)`` excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead.  Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, ``a.x``.\nHow the arguments are assembled depends on ``a``:\n\nDirect Call\n   The simplest and least common call is when user code directly\n   invokes a descriptor method:    ``x.__get__(a)``.\n\nInstance Binding\n   If binding to an object instance, ``a.x`` is transformed into the\n   call: ``type(a).__dict__[\'x\'].__get__(a, type(a))``.\n\nClass Binding\n   If binding to a class, ``A.x`` is transformed into the call:\n   ``A.__dict__[\'x\'].__get__(None, A)``.\n\nSuper Binding\n   If ``a`` is an instance of ``super``, then the binding ``super(B,\n   obj).m()`` searches ``obj.__class__.__mro__`` for the base class\n   ``A`` immediately preceding ``B`` and then invokes the descriptor\n   with the call: ``A.__dict__[\'m\'].__get__(obj, A)``.\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  Normally, data\ndescriptors define both ``__get__()`` and ``__set__()``, while non-\ndata descriptors have just the ``__get__()`` method.  Data descriptors\nalways override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances. [2]\n\nPython methods (including ``staticmethod()`` and ``classmethod()``)\nare implemented as non-data descriptors.  Accordingly, instances can\nredefine and override methods.  This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe ``property()`` function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n---------\n\nBy default, instances of classes have a dictionary for attribute\nstorage.  This wastes space for objects having very few instance\nvariables.  The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable.  Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n   This class variable can be assigned a string, iterable, or sequence\n   of strings with variable names used by instances.  If defined in a\n   class, *__slots__* reserves space for the declared variables and\n   prevents the automatic creation of *__dict__* and *__weakref__* for\n   each instance.\n\n\nNotes on using *__slots__*\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n  attribute of that class will always be accessible, so a *__slots__*\n  definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n  variables not listed in the *__slots__* definition.  Attempts to\n  assign to an unlisted variable name raises ``AttributeError``. If\n  dynamic assignment of new variables is desired, then add\n  ``\'__dict__\'`` to the sequence of strings in the *__slots__*\n  declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n  *__slots__* do not support weak references to its instances. If weak\n  reference support is needed, then add ``\'__weakref__\'`` to the\n  sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n  descriptors (*Implementing Descriptors*) for each variable name.  As\n  a result, class attributes cannot be used to set default values for\n  instance variables defined by *__slots__*; otherwise, the class\n  attribute would overwrite the descriptor assignment.\n\n* If a class defines a slot also defined in a base class, the instance\n  variable defined by the base class slot is inaccessible (except by\n  retrieving its descriptor directly from the base class). This\n  renders the meaning of the program undefined.  In the future, a\n  check may be added to prevent this.\n\n* The action of a *__slots__* declaration is limited to the class\n  where it is defined.  As a result, subclasses will have a *__dict__*\n  unless they also define *__slots__*.\n\n* *__slots__* do not work for classes derived from "variable-length"\n  built-in types such as ``int``, ``str`` and ``tuple``.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n  also be used; however, in the future, special meaning may be\n  assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n  *__slots__*.\n\n\nCustomizing class creation\n==========================\n\nBy default, classes are constructed using ``type()``. A class\ndefinition is read into a separate namespace and the value of class\nname is bound to the result of ``type(name, bases, dict)``.\n\nWhen the class definition is read, if *__metaclass__* is defined then\nthe callable assigned to it will be called instead of ``type()``. This\nallows classes or functions to be written which monitor or alter the\nclass creation process:\n\n* Modifying the class dictionary prior to the class being created.\n\n* Returning an instance of another class -- essentially performing the\n  role of a factory function.\n\nThese steps will have to be performed in the metaclass\'s ``__new__()``\nmethod -- ``type.__new__()`` can then be called from this method to\ncreate a class with different properties.  This example adds a new\nelement to the class dictionary before creating the class:\n\n   class metacls(type):\n       def __new__(mcs, name, bases, dict):\n           dict[\'foo\'] = \'metacls was here\'\n           return type.__new__(mcs, name, bases, dict)\n\nYou can of course also override other class methods (or add new\nmethods); for example defining a custom ``__call__()`` method in the\nmetaclass allows custom behavior when the class is called, e.g. not\nalways creating a new instance.\n\n__metaclass__\n\n   This variable can be any callable accepting arguments for ``name``,\n   ``bases``, and ``dict``.  Upon class creation, the callable is used\n   instead of the built-in ``type()``.\n\nThe appropriate metaclass is determined by the following precedence\nrules:\n\n* If ``dict[\'__metaclass__\']`` exists, it is used.\n\n* Otherwise, if there is at least one base class, its metaclass is\n  used (this looks for a *__class__* attribute first and if not found,\n  uses its type).\n\n* Otherwise, if a global variable named __metaclass__ exists, it is\n  used.\n\n* Otherwise, the default metaclass (``type``) is used.\n\nThe potential uses for metaclasses are boundless. Some ideas that have\nbeen explored including logging, interface checking, automatic\ndelegation, automatic property creation, proxies, frameworks, and\nautomatic resource locking/synchronization.\n\n\nEmulating callable objects\n==========================\n\nobject.__call__(self[, args...])\n\n   Called when the instance is "called" as a function; if this method\n   is defined, ``x(arg1, arg2, ...)`` is a shorthand for\n   ``x.__call__(arg1, arg2, ...)``.\n\n\nEmulating container types\n=========================\n\nThe following methods can be defined to implement container objects.\nContainers usually are sequences (such as lists or tuples) or mappings\n(like dictionaries), but can represent other containers as well.  The\nfirst set of methods is used either to emulate a sequence or to\nemulate a mapping; the difference is that for a sequence, the\nallowable keys should be the integers *k* for which ``0 <= k < N``\nwhere *N* is the length of the sequence, or slice objects, which\ndefine a range of items.  It is also recommended that mappings provide\nthe methods ``keys()``, ``values()``, ``items()``, ``get()``,\n``clear()``, ``setdefault()``, ``pop()``, ``popitem()``, ``copy()``,\nand ``update()`` behaving similar to those for Python\'s standard\ndictionary objects.  The ``collections`` module provides a\n``MutableMapping`` abstract base class to help create those methods\nfrom a base set of ``__getitem__()``, ``__setitem__()``,\n``__delitem__()``, and ``keys()``. Mutable sequences should provide\nmethods ``append()``, ``count()``, ``index()``, ``extend()``,\n``insert()``, ``pop()``, ``remove()``, ``reverse()`` and ``sort()``,\nlike Python standard list objects.  Finally, sequence types should\nimplement addition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods ``__add__()``, ``__radd__()``,\n``__iadd__()``, ``__mul__()``, ``__rmul__()`` and ``__imul__()``\ndescribed below; they should not define other numerical operators.  It\nis recommended that both mappings and sequences implement the\n``__contains__()`` method to allow efficient use of the ``in``\noperator; for mappings, ``in`` should search the mapping\'s keys; for\nsequences, it should search through the values.  It is further\nrecommended that both mappings and sequences implement the\n``__iter__()`` method to allow efficient iteration through the\ncontainer; for mappings, ``__iter__()`` should be the same as\n``keys()``; for sequences, it should iterate through the values.\n\nobject.__len__(self)\n\n   Called to implement the built-in function ``len()``.  Should return\n   the length of the object, an integer ``>=`` 0.  Also, an object\n   that doesn\'t define a ``__bool__()`` method and whose ``__len__()``\n   method returns zero is considered to be false in a Boolean context.\n\nNote: Slicing is done exclusively with the following three methods.  A\n  call like\n\n     a[1:2] = b\n\n  is translated to\n\n     a[slice(1, 2, None)] = b\n\n  and so forth.  Missing slice items are always filled in with\n  ``None``.\n\nobject.__getitem__(self, key)\n\n   Called to implement evaluation of ``self[key]``. For sequence\n   types, the accepted keys should be integers and slice objects.\n   Note that the special interpretation of negative indexes (if the\n   class wishes to emulate a sequence type) is up to the\n   ``__getitem__()`` method. If *key* is of an inappropriate type,\n   ``TypeError`` may be raised; if of a value outside the set of\n   indexes for the sequence (after any special interpretation of\n   negative values), ``IndexError`` should be raised. For mapping\n   types, if *key* is missing (not in the container), ``KeyError``\n   should be raised.\n\n   Note: ``for`` loops expect that an ``IndexError`` will be raised for\n     illegal indexes to allow proper detection of the end of the\n     sequence.\n\nobject.__setitem__(self, key, value)\n\n   Called to implement assignment to ``self[key]``.  Same note as for\n   ``__getitem__()``.  This should only be implemented for mappings if\n   the objects support changes to the values for keys, or if new keys\n   can be added, or for sequences if elements can be replaced.  The\n   same exceptions should be raised for improper *key* values as for\n   the ``__getitem__()`` method.\n\nobject.__delitem__(self, key)\n\n   Called to implement deletion of ``self[key]``.  Same note as for\n   ``__getitem__()``.  This should only be implemented for mappings if\n   the objects support removal of keys, or for sequences if elements\n   can be removed from the sequence.  The same exceptions should be\n   raised for improper *key* values as for the ``__getitem__()``\n   method.\n\nobject.__iter__(self)\n\n   This method is called when an iterator is required for a container.\n   This method should return a new iterator object that can iterate\n   over all the objects in the container.  For mappings, it should\n   iterate over the keys of the container, and should also be made\n   available as the method ``keys()``.\n\n   Iterator objects also need to implement this method; they are\n   required to return themselves.  For more information on iterator\n   objects, see *Iterator Types*.\n\nobject.__reversed__(self)\n\n   Called (if present) by the ``reversed()`` builtin to implement\n   reverse iteration.  It should return a new iterator object that\n   iterates over all the objects in the container in reverse order.\n\n   If the ``__reversed__()`` method is not provided, the\n   ``reversed()`` builtin will fall back to using the sequence\n   protocol (``__len__()`` and ``__getitem__()``).  Objects should\n   normally only provide ``__reversed__()`` if they do not support the\n   sequence protocol and an efficient implementation of reverse\n   iteration is possible.\n\nThe membership test operators (``in`` and ``not in``) are normally\nimplemented as an iteration through a sequence.  However, container\nobjects can supply the following special method with a more efficient\nimplementation, which also does not require the object be a sequence.\n\nobject.__contains__(self, item)\n\n   Called to implement membership test operators.  Should return true\n   if *item* is in *self*, false otherwise.  For mapping objects, this\n   should consider the keys of the mapping rather than the values or\n   the key-item pairs.\n\n\nEmulating numeric types\n=======================\n\nThe following methods can be defined to emulate numeric objects.\nMethods corresponding to operations that are not supported by the\nparticular kind of number implemented (e.g., bitwise operations for\nnon-integral numbers) should be left undefined.\n\nobject.__add__(self, other)\nobject.__sub__(self, other)\nobject.__mul__(self, other)\nobject.__truediv__(self, other)\nobject.__floordiv__(self, other)\nobject.__mod__(self, other)\nobject.__divmod__(self, other)\nobject.__pow__(self, other[, modulo])\nobject.__lshift__(self, other)\nobject.__rshift__(self, other)\nobject.__and__(self, other)\nobject.__xor__(self, other)\nobject.__or__(self, other)\n\n   These methods are called to implement the binary arithmetic\n   operations (``+``, ``-``, ``*``, ``/``, ``//``, ``%``,\n   ``divmod()``, ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``,\n   ``|``).  For instance, to evaluate the expression ``x + y``, where\n   *x* is an instance of a class that has an ``__add__()`` method,\n   ``x.__add__(y)`` is called.  The ``__divmod__()`` method should be\n   the equivalent to using ``__floordiv__()`` and ``__mod__()``; it\n   should not be related to ``__truediv__()``.  Note that\n   ``__pow__()`` should be defined to accept an optional third\n   argument if the ternary version of the built-in ``pow()`` function\n   is to be supported.\n\n   If one of those methods does not support the operation with the\n   supplied arguments, it should return ``NotImplemented``.\n\nobject.__radd__(self, other)\nobject.__rsub__(self, other)\nobject.__rmul__(self, other)\nobject.__rtruediv__(self, other)\nobject.__rfloordiv__(self, other)\nobject.__rmod__(self, other)\nobject.__rdivmod__(self, other)\nobject.__rpow__(self, other)\nobject.__rlshift__(self, other)\nobject.__rrshift__(self, other)\nobject.__rand__(self, other)\nobject.__rxor__(self, other)\nobject.__ror__(self, other)\n\n   These methods are called to implement the binary arithmetic\n   operations (``+``, ``-``, ``*``, ``/``, ``//``, ``%``,\n   ``divmod()``, ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``,\n   ``|``) with reflected (swapped) operands. These functions are only\n   called if the left operand does not support the corresponding\n   operation and the operands are of different types. [3]  For\n   instance, to evaluate the expression ``x - y``, where *y* is an\n   instance of a class that has an ``__rsub__()`` method,\n   ``y.__rsub__(x)`` is called if ``x.__sub__(y)`` returns\n   *NotImplemented*.\n\n   Note that ternary ``pow()`` will not try calling ``__rpow__()``\n   (the coercion rules would become too complicated).\n\n   Note: If the right operand\'s type is a subclass of the left operand\'s\n     type and that subclass provides the reflected method for the\n     operation, this method will be called before the left operand\'s\n     non-reflected method.  This behavior allows subclasses to\n     override their ancestors\' operations.\n\nobject.__iadd__(self, other)\nobject.__isub__(self, other)\nobject.__imul__(self, other)\nobject.__itruediv__(self, other)\nobject.__ifloordiv__(self, other)\nobject.__imod__(self, other)\nobject.__ipow__(self, other[, modulo])\nobject.__ilshift__(self, other)\nobject.__irshift__(self, other)\nobject.__iand__(self, other)\nobject.__ixor__(self, other)\nobject.__ior__(self, other)\n\n   These methods are called to implement the augmented arithmetic\n   operations (``+=``, ``-=``, ``*=``, ``/=``, ``//=``, ``%=``,\n   ``**=``, ``<<=``, ``>>=``, ``&=``, ``^=``, ``|=``).  These methods\n   should attempt to do the operation in-place (modifying *self*) and\n   return the result (which could be, but does not have to be,\n   *self*).  If a specific method is not defined, the augmented\n   operation falls back to the normal methods.  For instance, to\n   evaluate the expression ``x += y``, where *x* is an instance of a\n   class that has an ``__iadd__()`` method, ``x.__iadd__(y)`` is\n   called.  If *x* is an instance of a class that does not define a\n   ``__iadd__()`` method, ``x.__add__(y)`` and ``y.__radd__(x)`` are\n   considered, as with the evaluation of ``x + y``.\n\nobject.__neg__(self)\nobject.__pos__(self)\nobject.__abs__(self)\nobject.__invert__(self)\n\n   Called to implement the unary arithmetic operations (``-``, ``+``,\n   ``abs()`` and ``~``).\n\nobject.__complex__(self)\nobject.__int__(self)\nobject.__float__(self)\nobject.__round__(self[, n])\n\n   Called to implement the built-in functions ``complex()``,\n   ``int()``, ``float()`` and ``round()``.  Should return a value of\n   the appropriate type.\n\nobject.__index__(self)\n\n   Called to implement ``operator.index()``.  Also called whenever\n   Python needs an integer object (such as in slicing, or in the\n   built-in ``bin()``, ``hex()`` and ``oct()`` functions). Must return\n   an integer.\n\n\nWith Statement Context Managers\n===============================\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a ``with`` statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code.  Context managers are\nnormally invoked using the ``with`` statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n   Enter the runtime context related to this object. The ``with``\n   statement will bind this method\'s return value to the target(s)\n   specified in the ``as`` clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n   Exit the runtime context related to this object. The parameters\n   describe the exception that caused the context to be exited. If the\n   context was exited without an exception, all three arguments will\n   be ``None``.\n\n   If an exception is supplied, and the method wishes to suppress the\n   exception (i.e., prevent it from being propagated), it should\n   return a true value. Otherwise, the exception will be processed\n   normally upon exit from this method.\n\n   Note that ``__exit__()`` methods should not reraise the passed-in\n   exception; this is the caller\'s responsibility.\n\nSee also:\n\n   **PEP 0343** - The "with" statement\n      The specification, background, and examples for the Python\n      ``with`` statement.\n\n\nSpecial method lookup\n=====================\n\nFor custom classes, implicit invocations of special methods are only\nguaranteed to work correctly if defined on an object\'s type, not in\nthe object\'s instance dictionary.  That behaviour is the reason why\nthe following code raises an exception:\n\n   >>> class C(object):\n   ...     pass\n   ...\n   >>> c = C()\n   >>> c.__len__ = lambda: 5\n   >>> len(c)\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   TypeError: object of type \'C\' has no len()\n\nThe rationale behind this behaviour lies with a number of special\nmethods such as ``__hash__()`` and ``__repr__()`` that are implemented\nby all objects, including type objects. If the implicit lookup of\nthese methods used the conventional lookup process, they would fail\nwhen invoked on the type object itself:\n\n   >>> 1 .__hash__() == hash(1)\n   True\n   >>> int.__hash__() == hash(int)\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   TypeError: descriptor \'__hash__\' of \'int\' object needs an argument\n\nIncorrectly attempting to invoke an unbound method of a class in this\nway is sometimes referred to as \'metaclass confusion\', and is avoided\nby bypassing the instance when looking up special methods:\n\n   >>> type(1).__hash__(1) == hash(1)\n   True\n   >>> type(int).__hash__(int) == hash(int)\n   True\n\nIn addition to bypassing any instance attributes in the interest of\ncorrectness, implicit special method lookup may also bypass the\n``__getattribute__()`` method even of the object\'s metaclass:\n\n   >>> class Meta(type):\n   ...    def __getattribute__(*args):\n   ...       print "Metaclass getattribute invoked"\n   ...       return type.__getattribute__(*args)\n   ...\n   >>> class C(object):\n   ...     __metaclass__ = Meta\n   ...     def __len__(self):\n   ...         return 10\n   ...     def __getattribute__(*args):\n   ...         print "Class getattribute invoked"\n   ...         return object.__getattribute__(*args)\n   ...\n   >>> c = C()\n   >>> c.__len__()                 # Explicit lookup via instance\n   Class getattribute invoked\n   10\n   >>> type(c).__len__(c)          # Explicit lookup via type\n   Metaclass getattribute invoked\n   10\n   >>> len(c)                      # Implicit lookup\n   10\n\nBypassing the ``__getattribute__()`` machinery in this fashion\nprovides significant scope for speed optimisations within the\ninterpreter, at the cost of some flexibility in the handling of\nspecial methods (the special method *must* be set on the class object\nitself in order to be consistently invoked by the interpreter).\n\n-[ Footnotes ]-\n\n[1] It *is* possible in some cases to change an object\'s type, under\n    certain controlled conditions. It generally isn\'t a good idea\n    though, since it can lead to some very strange behaviour if it is\n    handled incorrectly.\n\n[2] A descriptor can define any combination of ``__get__()``,\n    ``__set__()`` and ``__delete__()``.  If it does not define\n    ``__get__()``, then accessing the attribute even on an instance\n    will return the descriptor object itself.  If the descriptor\n    defines ``__set__()`` and/or ``__delete__()``, it is a data\n    descriptor; if it defines neither, it is a non-data descriptor.\n\n[3] For operands of the same type, it is assumed that if the non-\n    reflected method (such as ``__add__()``) fails the operation is\n    not supported, which is why the reflected method is not called.\n',
+ 'specialnames': '\nSpecial method names\n********************\n\nA class can implement certain operations that are invoked by special\nsyntax (such as arithmetic operations or subscripting and slicing) by\ndefining methods with special names. This is Python\'s approach to\n*operator overloading*, allowing classes to define their own behavior\nwith respect to language operators.  For instance, if a class defines\na method named ``__getitem__()``, and ``x`` is an instance of this\nclass, then ``x[i]`` is roughly equivalent to ``type(x).__getitem__(x,\ni)``.  Except where mentioned, attempts to execute an operation raise\nan exception when no appropriate method is defined (typically\n``AttributeError`` or ``TypeError``).\n\nWhen implementing a class that emulates any built-in type, it is\nimportant that the emulation only be implemented to the degree that it\nmakes sense for the object being modelled.  For example, some\nsequences may work well with retrieval of individual elements, but\nextracting a slice may not make sense.  (One example of this is the\n``NodeList`` interface in the W3C\'s Document Object Model.)\n\n\nBasic customization\n===================\n\nobject.__new__(cls[, ...])\n\n   Called to create a new instance of class *cls*.  ``__new__()`` is a\n   static method (special-cased so you need not declare it as such)\n   that takes the class of which an instance was requested as its\n   first argument.  The remaining arguments are those passed to the\n   object constructor expression (the call to the class).  The return\n   value of ``__new__()`` should be the new object instance (usually\n   an instance of *cls*).\n\n   Typical implementations create a new instance of the class by\n   invoking the superclass\'s ``__new__()`` method using\n   ``super(currentclass, cls).__new__(cls[, ...])`` with appropriate\n   arguments and then modifying the newly-created instance as\n   necessary before returning it.\n\n   If ``__new__()`` returns an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will be invoked like\n   ``__init__(self[, ...])``, where *self* is the new instance and the\n   remaining arguments are the same as were passed to ``__new__()``.\n\n   If ``__new__()`` does not return an instance of *cls*, then the new\n   instance\'s ``__init__()`` method will not be invoked.\n\n   ``__new__()`` is intended mainly to allow subclasses of immutable\n   types (like int, str, or tuple) to customize instance creation.  It\n   is also commonly overridden in custom metaclasses in order to\n   customize class creation.\n\nobject.__init__(self[, ...])\n\n   Called when the instance is created.  The arguments are those\n   passed to the class constructor expression.  If a base class has an\n   ``__init__()`` method, the derived class\'s ``__init__()`` method,\n   if any, must explicitly call it to ensure proper initialization of\n   the base class part of the instance; for example:\n   ``BaseClass.__init__(self, [args...])``.  As a special constraint\n   on constructors, no value may be returned; doing so will cause a\n   ``TypeError`` to be raised at runtime.\n\nobject.__del__(self)\n\n   Called when the instance is about to be destroyed.  This is also\n   called a destructor.  If a base class has a ``__del__()`` method,\n   the derived class\'s ``__del__()`` method, if any, must explicitly\n   call it to ensure proper deletion of the base class part of the\n   instance.  Note that it is possible (though not recommended!) for\n   the ``__del__()`` method to postpone destruction of the instance by\n   creating a new reference to it.  It may then be called at a later\n   time when this new reference is deleted.  It is not guaranteed that\n   ``__del__()`` methods are called for objects that still exist when\n   the interpreter exits.\n\n   Note: ``del x`` doesn\'t directly call ``x.__del__()`` --- the former\n     decrements the reference count for ``x`` by one, and the latter\n     is only called when ``x``\'s reference count reaches zero.  Some\n     common situations that may prevent the reference count of an\n     object from going to zero include: circular references between\n     objects (e.g., a doubly-linked list or a tree data structure with\n     parent and child pointers); a reference to the object on the\n     stack frame of a function that caught an exception (the traceback\n     stored in ``sys.exc_info()[2]`` keeps the stack frame alive); or\n     a reference to the object on the stack frame that raised an\n     unhandled exception in interactive mode (the traceback stored in\n     ``sys.last_traceback`` keeps the stack frame alive).  The first\n     situation can only be remedied by explicitly breaking the cycles;\n     the latter two situations can be resolved by storing ``None`` in\n     ``sys.last_traceback``. Circular references which are garbage are\n     detected when the option cycle detector is enabled (it\'s on by\n     default), but can only be cleaned up if there are no Python-\n     level ``__del__()`` methods involved. Refer to the documentation\n     for the ``gc`` module for more information about how\n     ``__del__()`` methods are handled by the cycle detector,\n     particularly the description of the ``garbage`` value.\n\n   Warning: Due to the precarious circumstances under which ``__del__()``\n     methods are invoked, exceptions that occur during their execution\n     are ignored, and a warning is printed to ``sys.stderr`` instead.\n     Also, when ``__del__()`` is invoked in response to a module being\n     deleted (e.g., when execution of the program is done), other\n     globals referenced by the ``__del__()`` method may already have\n     been deleted.  For this reason, ``__del__()`` methods should do\n     the absolute minimum needed to maintain external invariants.\n     Starting with version 1.5, Python guarantees that globals whose\n     name begins with a single underscore are deleted from their\n     module before other globals are deleted; if no other references\n     to such globals exist, this may help in assuring that imported\n     modules are still available at the time when the ``__del__()``\n     method is called.\n\nobject.__repr__(self)\n\n   Called by the ``repr()`` built-in function to compute the\n   "official" string representation of an object.  If at all possible,\n   this should look like a valid Python expression that could be used\n   to recreate an object with the same value (given an appropriate\n   environment).  If this is not possible, a string of the form\n   ``<...some useful description...>`` should be returned. The return\n   value must be a string object. If a class defines ``__repr__()``\n   but not ``__str__()``, then ``__repr__()`` is also used when an\n   "informal" string representation of instances of that class is\n   required.\n\n   This is typically used for debugging, so it is important that the\n   representation is information-rich and unambiguous.\n\nobject.__str__(self)\n\n   Called by the ``str()`` built-in function and by the ``print()``\n   function to compute the "informal" string representation of an\n   object.  This differs from ``__repr__()`` in that it does not have\n   to be a valid Python expression: a more convenient or concise\n   representation may be used instead. The return value must be a\n   string object.\n\nobject.__format__(self, format_spec)\n\n   Called by the ``format()`` built-in function (and by extension, the\n   ``format()`` method of class ``str``) to produce a "formatted"\n   string representation of an object. The ``format_spec`` argument is\n   a string that contains a description of the formatting options\n   desired. The interpretation of the ``format_spec`` argument is up\n   to the type implementing ``__format__()``, however most classes\n   will either delegate formatting to one of the built-in types, or\n   use a similar formatting option syntax.\n\n   See *Format Specification Mini-Language* for a description of the\n   standard formatting syntax.\n\n   The return value must be a string object.\n\nobject.__lt__(self, other)\nobject.__le__(self, other)\nobject.__eq__(self, other)\nobject.__ne__(self, other)\nobject.__gt__(self, other)\nobject.__ge__(self, other)\n\n   These are the so-called "rich comparison" methods. The\n   correspondence between operator symbols and method names is as\n   follows: ``x<y`` calls ``x.__lt__(y)``, ``x<=y`` calls\n   ``x.__le__(y)``, ``x==y`` calls ``x.__eq__(y)``, ``x!=y`` calls\n   ``x.__ne__(y)``, ``x>y`` calls ``x.__gt__(y)``, and ``x>=y`` calls\n   ``x.__ge__(y)``.\n\n   A rich comparison method may return the singleton\n   ``NotImplemented`` if it does not implement the operation for a\n   given pair of arguments. By convention, ``False`` and ``True`` are\n   returned for a successful comparison. However, these methods can\n   return any value, so if the comparison operator is used in a\n   Boolean context (e.g., in the condition of an ``if`` statement),\n   Python will call ``bool()`` on the value to determine if the result\n   is true or false.\n\n   There are no implied relationships among the comparison operators.\n   The truth of ``x==y`` does not imply that ``x!=y`` is false.\n   Accordingly, when defining ``__eq__()``, one should also define\n   ``__ne__()`` so that the operators will behave as expected.  See\n   the paragraph on ``__hash__()`` for some important notes on\n   creating *hashable* objects which support custom comparison\n   operations and are usable as dictionary keys.\n\n   There are no swapped-argument versions of these methods (to be used\n   when the left argument does not support the operation but the right\n   argument does); rather, ``__lt__()`` and ``__gt__()`` are each\n   other\'s reflection, ``__le__()`` and ``__ge__()`` are each other\'s\n   reflection, and ``__eq__()`` and ``__ne__()`` are their own\n   reflection.\n\n   Arguments to rich comparison methods are never coerced.\n\nobject.__hash__(self)\n\n   Called for the key object for dictionary operations, and by the\n   built-in function ``hash()``.  Should return an integer usable as a\n   hash value for dictionary operations.  The only required property\n   is that objects which compare equal have the same hash value; it is\n   advised to somehow mix together (e.g., using exclusive or) the hash\n   values for the components of the object that also play a part in\n   comparison of objects.\n\n   If a class does not define an ``__eq__()`` method it should not\n   define a ``__hash__()`` operation either; if it defines\n   ``__eq__()`` but not ``__hash__()``, its instances will not be\n   usable as dictionary keys.  If a class defines mutable objects and\n   implements an ``__eq__()`` method, it should not implement\n   ``__hash__()``, since the dictionary implementation requires that a\n   key\'s hash value is immutable (if the object\'s hash value changes,\n   it will be in the wrong hash bucket).\n\n   User-defined classes have ``__eq__()`` and ``__hash__()`` methods\n   by default; with them, all objects compare unequal (except with\n   themselves) and ``x.__hash__()`` returns ``id(x)``.\n\n   Classes which inherit a ``__hash__()`` method from a parent class\n   but change the meaning of ``__eq__()`` such that the hash value\n   returned is no longer appropriate (e.g. by switching to a value-\n   based concept of equality instead of the default identity based\n   equality) can explicitly flag themselves as being unhashable by\n   setting ``__hash__ = None`` in the class definition. Doing so means\n   that not only will instances of the class raise an appropriate\n   ``TypeError`` when a program attempts to retrieve their hash value,\n   but they will also be correctly identified as unhashable when\n   checking ``isinstance(obj, collections.Hashable)`` (unlike classes\n   which define their own ``__hash__()`` to explicitly raise\n   ``TypeError``).\n\n   If a class that overrrides ``__eq__()`` needs to retain the\n   implementation of ``__hash__()`` from a parent class, the\n   interpreter must be told this explicitly by setting ``__hash__ =\n   <ParentClass>.__hash__``. Otherwise the inheritance of\n   ``__hash__()`` will be blocked, just as if ``__hash__`` had been\n   explicitly set to ``None``.\n\nobject.__bool__(self)\n\n   Called to implement truth value testing, and the built-in operation\n   ``bool()``; should return ``False`` or ``True``. When this method\n   is not defined, ``__len__()`` is called, if it is defined (see\n   below) and ``True`` is returned when the length is not zero.  If a\n   class defines neither ``__len__()`` nor ``__bool__()``, all its\n   instances are considered true.\n\n\nCustomizing attribute access\n============================\n\nThe following methods can be defined to customize the meaning of\nattribute access (use of, assignment to, or deletion of ``x.name``)\nfor class instances.\n\nobject.__getattr__(self, name)\n\n   Called when an attribute lookup has not found the attribute in the\n   usual places (i.e. it is not an instance attribute nor is it found\n   in the class tree for ``self``).  ``name`` is the attribute name.\n   This method should return the (computed) attribute value or raise\n   an ``AttributeError`` exception.\n\n   Note that if the attribute is found through the normal mechanism,\n   ``__getattr__()`` is not called.  (This is an intentional asymmetry\n   between ``__getattr__()`` and ``__setattr__()``.) This is done both\n   for efficiency reasons and because otherwise ``__getattr__()``\n   would have no way to access other attributes of the instance.  Note\n   that at least for instance variables, you can fake total control by\n   not inserting any values in the instance attribute dictionary (but\n   instead inserting them in another object).  See the\n   ``__getattribute__()`` method below for a way to actually get total\n   control over attribute access.\n\nobject.__getattribute__(self, name)\n\n   Called unconditionally to implement attribute accesses for\n   instances of the class. If the class also defines\n   ``__getattr__()``, the latter will not be called unless\n   ``__getattribute__()`` either calls it explicitly or raises an\n   ``AttributeError``. This method should return the (computed)\n   attribute value or raise an ``AttributeError`` exception. In order\n   to avoid infinite recursion in this method, its implementation\n   should always call the base class method with the same name to\n   access any attributes it needs, for example,\n   ``object.__getattribute__(self, name)``.\n\n   Note: This method may still be bypassed when looking up special methods\n     as the result of implicit invocation via language syntax or\n     builtin functions. See *Special method lookup*.\n\nobject.__setattr__(self, name, value)\n\n   Called when an attribute assignment is attempted.  This is called\n   instead of the normal mechanism (i.e. store the value in the\n   instance dictionary). *name* is the attribute name, *value* is the\n   value to be assigned to it.\n\n   If ``__setattr__()`` wants to assign to an instance attribute, it\n   should call the base class method with the same name, for example,\n   ``object.__setattr__(self, name, value)``.\n\nobject.__delattr__(self, name)\n\n   Like ``__setattr__()`` but for attribute deletion instead of\n   assignment.  This should only be implemented if ``del obj.name`` is\n   meaningful for the object.\n\nobject.__dir__(self)\n\n   Called when ``dir()`` is called on the object.  A list must be\n   returned.\n\n\nImplementing Descriptors\n------------------------\n\nThe following methods only apply when an instance of the class\ncontaining the method (a so-called *descriptor* class) appears in the\nclass dictionary of another class, known as the *owner* class.  In the\nexamples below, "the attribute" refers to the attribute whose name is\nthe key of the property in the owner class\' ``__dict__``.\n\nobject.__get__(self, instance, owner)\n\n   Called to get the attribute of the owner class (class attribute\n   access) or of an instance of that class (instance attribute\n   access). *owner* is always the owner class, while *instance* is the\n   instance that the attribute was accessed through, or ``None`` when\n   the attribute is accessed through the *owner*.  This method should\n   return the (computed) attribute value or raise an\n   ``AttributeError`` exception.\n\nobject.__set__(self, instance, value)\n\n   Called to set the attribute on an instance *instance* of the owner\n   class to a new value, *value*.\n\nobject.__delete__(self, instance)\n\n   Called to delete the attribute on an instance *instance* of the\n   owner class.\n\n\nInvoking Descriptors\n--------------------\n\nIn general, a descriptor is an object attribute with "binding\nbehavior", one whose attribute access has been overridden by methods\nin the descriptor protocol:  ``__get__()``, ``__set__()``, and\n``__delete__()``. If any of those methods are defined for an object,\nit is said to be a descriptor.\n\nThe default behavior for attribute access is to get, set, or delete\nthe attribute from an object\'s dictionary. For instance, ``a.x`` has a\nlookup chain starting with ``a.__dict__[\'x\']``, then\n``type(a).__dict__[\'x\']``, and continuing through the base classes of\n``type(a)`` excluding metaclasses.\n\nHowever, if the looked-up value is an object defining one of the\ndescriptor methods, then Python may override the default behavior and\ninvoke the descriptor method instead.  Where this occurs in the\nprecedence chain depends on which descriptor methods were defined and\nhow they were called.\n\nThe starting point for descriptor invocation is a binding, ``a.x``.\nHow the arguments are assembled depends on ``a``:\n\nDirect Call\n   The simplest and least common call is when user code directly\n   invokes a descriptor method:    ``x.__get__(a)``.\n\nInstance Binding\n   If binding to an object instance, ``a.x`` is transformed into the\n   call: ``type(a).__dict__[\'x\'].__get__(a, type(a))``.\n\nClass Binding\n   If binding to a class, ``A.x`` is transformed into the call:\n   ``A.__dict__[\'x\'].__get__(None, A)``.\n\nSuper Binding\n   If ``a`` is an instance of ``super``, then the binding ``super(B,\n   obj).m()`` searches ``obj.__class__.__mro__`` for the base class\n   ``A`` immediately preceding ``B`` and then invokes the descriptor\n   with the call: ``A.__dict__[\'m\'].__get__(obj, A)``.\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  Normally, data\ndescriptors define both ``__get__()`` and ``__set__()``, while non-\ndata descriptors have just the ``__get__()`` method.  Data descriptors\nalways override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances. [2]\n\nPython methods (including ``staticmethod()`` and ``classmethod()``)\nare implemented as non-data descriptors.  Accordingly, instances can\nredefine and override methods.  This allows individual instances to\nacquire behaviors that differ from other instances of the same class.\n\nThe ``property()`` function is implemented as a data descriptor.\nAccordingly, instances cannot override the behavior of a property.\n\n\n__slots__\n---------\n\nBy default, instances of classes have a dictionary for attribute\nstorage.  This wastes space for objects having very few instance\nvariables.  The space consumption can become acute when creating large\nnumbers of instances.\n\nThe default can be overridden by defining *__slots__* in a class\ndefinition. The *__slots__* declaration takes a sequence of instance\nvariables and reserves just enough space in each instance to hold a\nvalue for each variable.  Space is saved because *__dict__* is not\ncreated for each instance.\n\nobject.__slots__\n\n   This class variable can be assigned a string, iterable, or sequence\n   of strings with variable names used by instances.  If defined in a\n   class, *__slots__* reserves space for the declared variables and\n   prevents the automatic creation of *__dict__* and *__weakref__* for\n   each instance.\n\n\nNotes on using *__slots__*\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n* When inheriting from a class without *__slots__*, the *__dict__*\n  attribute of that class will always be accessible, so a *__slots__*\n  definition in the subclass is meaningless.\n\n* Without a *__dict__* variable, instances cannot be assigned new\n  variables not listed in the *__slots__* definition.  Attempts to\n  assign to an unlisted variable name raises ``AttributeError``. If\n  dynamic assignment of new variables is desired, then add\n  ``\'__dict__\'`` to the sequence of strings in the *__slots__*\n  declaration.\n\n* Without a *__weakref__* variable for each instance, classes defining\n  *__slots__* do not support weak references to its instances. If weak\n  reference support is needed, then add ``\'__weakref__\'`` to the\n  sequence of strings in the *__slots__* declaration.\n\n* *__slots__* are implemented at the class level by creating\n  descriptors (*Implementing Descriptors*) for each variable name.  As\n  a result, class attributes cannot be used to set default values for\n  instance variables defined by *__slots__*; otherwise, the class\n  attribute would overwrite the descriptor assignment.\n\n* If a class defines a slot also defined in a base class, the instance\n  variable defined by the base class slot is inaccessible (except by\n  retrieving its descriptor directly from the base class). This\n  renders the meaning of the program undefined.  In the future, a\n  check may be added to prevent this.\n\n* The action of a *__slots__* declaration is limited to the class\n  where it is defined.  As a result, subclasses will have a *__dict__*\n  unless they also define *__slots__*.\n\n* Nonempty *__slots__* does not work for classes derived from\n  "variable-length" built-in types such as ``int``, ``str`` and\n  ``tuple``.\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings may\n  also be used; however, in the future, special meaning may be\n  assigned to the values corresponding to each key.\n\n* *__class__* assignment works only if both classes have the same\n  *__slots__*.\n\n\nCustomizing class creation\n==========================\n\nBy default, classes are constructed using ``type()``. A class\ndefinition is read into a separate namespace and the value of class\nname is bound to the result of ``type(name, bases, dict)``.\n\nWhen the class definition is read, if a callable ``metaclass`` keyword\nargument is passed after the bases in the class definition, the\ncallable given will be called instead of ``type()``.  If other keyword\narguments are passed, they will also be passed to the metaclass.  This\nallows classes or functions to be written which monitor or alter the\nclass creation process:\n\n* Modifying the class dictionary prior to the class being created.\n\n* Returning an instance of another class -- essentially performing the\n  role of a factory function.\n\nThese steps will have to be performed in the metaclass\'s ``__new__()``\nmethod -- ``type.__new__()`` can then be called from this method to\ncreate a class with different properties.  This example adds a new\nelement to the class dictionary before creating the class:\n\n   class metacls(type):\n       def __new__(mcs, name, bases, dict):\n           dict[\'foo\'] = \'metacls was here\'\n           return type.__new__(mcs, name, bases, dict)\n\nYou can of course also override other class methods (or add new\nmethods); for example defining a custom ``__call__()`` method in the\nmetaclass allows custom behavior when the class is called, e.g. not\nalways creating a new instance.\n\nIf the metaclass has a ``__prepare__()`` attribute (usually\nimplemented as a class or static method), it is called before the\nclass body is evaluated with the name of the class and a tuple of its\nbases for arguments.  It should return an object that supports the\nmapping interface that will be used to store the namespace of the\nclass.  The default is a plain dictionary.  This could be used, for\nexample, to keep track of the order that class attributes are declared\nin by returning an ordered dictionary.\n\nThe appropriate metaclass is determined by the following precedence\nrules:\n\n* If the ``metaclass`` keyword argument is based with the bases, it is\n  used.\n\n* Otherwise, if there is at least one base class, its metaclass is\n  used.\n\n* Otherwise, the default metaclass (``type``) is used.\n\nThe potential uses for metaclasses are boundless. Some ideas that have\nbeen explored including logging, interface checking, automatic\ndelegation, automatic property creation, proxies, frameworks, and\nautomatic resource locking/synchronization.\n\n\nEmulating callable objects\n==========================\n\nobject.__call__(self[, args...])\n\n   Called when the instance is "called" as a function; if this method\n   is defined, ``x(arg1, arg2, ...)`` is a shorthand for\n   ``x.__call__(arg1, arg2, ...)``.\n\n\nEmulating container types\n=========================\n\nThe following methods can be defined to implement container objects.\nContainers usually are sequences (such as lists or tuples) or mappings\n(like dictionaries), but can represent other containers as well.  The\nfirst set of methods is used either to emulate a sequence or to\nemulate a mapping; the difference is that for a sequence, the\nallowable keys should be the integers *k* for which ``0 <= k < N``\nwhere *N* is the length of the sequence, or slice objects, which\ndefine a range of items.  It is also recommended that mappings provide\nthe methods ``keys()``, ``values()``, ``items()``, ``get()``,\n``clear()``, ``setdefault()``, ``pop()``, ``popitem()``, ``copy()``,\nand ``update()`` behaving similar to those for Python\'s standard\ndictionary objects.  The ``collections`` module provides a\n``MutableMapping`` abstract base class to help create those methods\nfrom a base set of ``__getitem__()``, ``__setitem__()``,\n``__delitem__()``, and ``keys()``. Mutable sequences should provide\nmethods ``append()``, ``count()``, ``index()``, ``extend()``,\n``insert()``, ``pop()``, ``remove()``, ``reverse()`` and ``sort()``,\nlike Python standard list objects.  Finally, sequence types should\nimplement addition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods ``__add__()``, ``__radd__()``,\n``__iadd__()``, ``__mul__()``, ``__rmul__()`` and ``__imul__()``\ndescribed below; they should not define other numerical operators.  It\nis recommended that both mappings and sequences implement the\n``__contains__()`` method to allow efficient use of the ``in``\noperator; for mappings, ``in`` should search the mapping\'s keys; for\nsequences, it should search through the values.  It is further\nrecommended that both mappings and sequences implement the\n``__iter__()`` method to allow efficient iteration through the\ncontainer; for mappings, ``__iter__()`` should be the same as\n``keys()``; for sequences, it should iterate through the values.\n\nobject.__len__(self)\n\n   Called to implement the built-in function ``len()``.  Should return\n   the length of the object, an integer ``>=`` 0.  Also, an object\n   that doesn\'t define a ``__bool__()`` method and whose ``__len__()``\n   method returns zero is considered to be false in a Boolean context.\n\nNote: Slicing is done exclusively with the following three methods.  A\n  call like\n\n     a[1:2] = b\n\n  is translated to\n\n     a[slice(1, 2, None)] = b\n\n  and so forth.  Missing slice items are always filled in with\n  ``None``.\n\nobject.__getitem__(self, key)\n\n   Called to implement evaluation of ``self[key]``. For sequence\n   types, the accepted keys should be integers and slice objects.\n   Note that the special interpretation of negative indexes (if the\n   class wishes to emulate a sequence type) is up to the\n   ``__getitem__()`` method. If *key* is of an inappropriate type,\n   ``TypeError`` may be raised; if of a value outside the set of\n   indexes for the sequence (after any special interpretation of\n   negative values), ``IndexError`` should be raised. For mapping\n   types, if *key* is missing (not in the container), ``KeyError``\n   should be raised.\n\n   Note: ``for`` loops expect that an ``IndexError`` will be raised for\n     illegal indexes to allow proper detection of the end of the\n     sequence.\n\nobject.__setitem__(self, key, value)\n\n   Called to implement assignment to ``self[key]``.  Same note as for\n   ``__getitem__()``.  This should only be implemented for mappings if\n   the objects support changes to the values for keys, or if new keys\n   can be added, or for sequences if elements can be replaced.  The\n   same exceptions should be raised for improper *key* values as for\n   the ``__getitem__()`` method.\n\nobject.__delitem__(self, key)\n\n   Called to implement deletion of ``self[key]``.  Same note as for\n   ``__getitem__()``.  This should only be implemented for mappings if\n   the objects support removal of keys, or for sequences if elements\n   can be removed from the sequence.  The same exceptions should be\n   raised for improper *key* values as for the ``__getitem__()``\n   method.\n\nobject.__iter__(self)\n\n   This method is called when an iterator is required for a container.\n   This method should return a new iterator object that can iterate\n   over all the objects in the container.  For mappings, it should\n   iterate over the keys of the container, and should also be made\n   available as the method ``keys()``.\n\n   Iterator objects also need to implement this method; they are\n   required to return themselves.  For more information on iterator\n   objects, see *Iterator Types*.\n\nobject.__reversed__(self)\n\n   Called (if present) by the ``reversed()`` builtin to implement\n   reverse iteration.  It should return a new iterator object that\n   iterates over all the objects in the container in reverse order.\n\n   If the ``__reversed__()`` method is not provided, the\n   ``reversed()`` builtin will fall back to using the sequence\n   protocol (``__len__()`` and ``__getitem__()``).  Objects should\n   normally only provide ``__reversed__()`` if they do not support the\n   sequence protocol and an efficient implementation of reverse\n   iteration is possible.\n\nThe membership test operators (``in`` and ``not in``) are normally\nimplemented as an iteration through a sequence.  However, container\nobjects can supply the following special method with a more efficient\nimplementation, which also does not require the object be a sequence.\n\nobject.__contains__(self, item)\n\n   Called to implement membership test operators.  Should return true\n   if *item* is in *self*, false otherwise.  For mapping objects, this\n   should consider the keys of the mapping rather than the values or\n   the key-item pairs.\n\n\nEmulating numeric types\n=======================\n\nThe following methods can be defined to emulate numeric objects.\nMethods corresponding to operations that are not supported by the\nparticular kind of number implemented (e.g., bitwise operations for\nnon-integral numbers) should be left undefined.\n\nobject.__add__(self, other)\nobject.__sub__(self, other)\nobject.__mul__(self, other)\nobject.__truediv__(self, other)\nobject.__floordiv__(self, other)\nobject.__mod__(self, other)\nobject.__divmod__(self, other)\nobject.__pow__(self, other[, modulo])\nobject.__lshift__(self, other)\nobject.__rshift__(self, other)\nobject.__and__(self, other)\nobject.__xor__(self, other)\nobject.__or__(self, other)\n\n   These methods are called to implement the binary arithmetic\n   operations (``+``, ``-``, ``*``, ``/``, ``//``, ``%``,\n   ``divmod()``, ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``,\n   ``|``).  For instance, to evaluate the expression ``x + y``, where\n   *x* is an instance of a class that has an ``__add__()`` method,\n   ``x.__add__(y)`` is called.  The ``__divmod__()`` method should be\n   the equivalent to using ``__floordiv__()`` and ``__mod__()``; it\n   should not be related to ``__truediv__()``.  Note that\n   ``__pow__()`` should be defined to accept an optional third\n   argument if the ternary version of the built-in ``pow()`` function\n   is to be supported.\n\n   If one of those methods does not support the operation with the\n   supplied arguments, it should return ``NotImplemented``.\n\nobject.__radd__(self, other)\nobject.__rsub__(self, other)\nobject.__rmul__(self, other)\nobject.__rtruediv__(self, other)\nobject.__rfloordiv__(self, other)\nobject.__rmod__(self, other)\nobject.__rdivmod__(self, other)\nobject.__rpow__(self, other)\nobject.__rlshift__(self, other)\nobject.__rrshift__(self, other)\nobject.__rand__(self, other)\nobject.__rxor__(self, other)\nobject.__ror__(self, other)\n\n   These methods are called to implement the binary arithmetic\n   operations (``+``, ``-``, ``*``, ``/``, ``//``, ``%``,\n   ``divmod()``, ``pow()``, ``**``, ``<<``, ``>>``, ``&``, ``^``,\n   ``|``) with reflected (swapped) operands. These functions are only\n   called if the left operand does not support the corresponding\n   operation and the operands are of different types. [3]  For\n   instance, to evaluate the expression ``x - y``, where *y* is an\n   instance of a class that has an ``__rsub__()`` method,\n   ``y.__rsub__(x)`` is called if ``x.__sub__(y)`` returns\n   *NotImplemented*.\n\n   Note that ternary ``pow()`` will not try calling ``__rpow__()``\n   (the coercion rules would become too complicated).\n\n   Note: If the right operand\'s type is a subclass of the left operand\'s\n     type and that subclass provides the reflected method for the\n     operation, this method will be called before the left operand\'s\n     non-reflected method.  This behavior allows subclasses to\n     override their ancestors\' operations.\n\nobject.__iadd__(self, other)\nobject.__isub__(self, other)\nobject.__imul__(self, other)\nobject.__itruediv__(self, other)\nobject.__ifloordiv__(self, other)\nobject.__imod__(self, other)\nobject.__ipow__(self, other[, modulo])\nobject.__ilshift__(self, other)\nobject.__irshift__(self, other)\nobject.__iand__(self, other)\nobject.__ixor__(self, other)\nobject.__ior__(self, other)\n\n   These methods are called to implement the augmented arithmetic\n   operations (``+=``, ``-=``, ``*=``, ``/=``, ``//=``, ``%=``,\n   ``**=``, ``<<=``, ``>>=``, ``&=``, ``^=``, ``|=``).  These methods\n   should attempt to do the operation in-place (modifying *self*) and\n   return the result (which could be, but does not have to be,\n   *self*).  If a specific method is not defined, the augmented\n   operation falls back to the normal methods.  For instance, to\n   evaluate the expression ``x += y``, where *x* is an instance of a\n   class that has an ``__iadd__()`` method, ``x.__iadd__(y)`` is\n   called.  If *x* is an instance of a class that does not define a\n   ``__iadd__()`` method, ``x.__add__(y)`` and ``y.__radd__(x)`` are\n   considered, as with the evaluation of ``x + y``.\n\nobject.__neg__(self)\nobject.__pos__(self)\nobject.__abs__(self)\nobject.__invert__(self)\n\n   Called to implement the unary arithmetic operations (``-``, ``+``,\n   ``abs()`` and ``~``).\n\nobject.__complex__(self)\nobject.__int__(self)\nobject.__float__(self)\nobject.__round__(self[, n])\n\n   Called to implement the built-in functions ``complex()``,\n   ``int()``, ``float()`` and ``round()``.  Should return a value of\n   the appropriate type.\n\nobject.__index__(self)\n\n   Called to implement ``operator.index()``.  Also called whenever\n   Python needs an integer object (such as in slicing, or in the\n   built-in ``bin()``, ``hex()`` and ``oct()`` functions). Must return\n   an integer.\n\n\nWith Statement Context Managers\n===============================\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a ``with`` statement. The context\nmanager handles the entry into, and the exit from, the desired runtime\ncontext for the execution of the block of code.  Context managers are\nnormally invoked using the ``with`` statement (described in section\n*The with statement*), but can also be used by directly invoking their\nmethods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n   Enter the runtime context related to this object. The ``with``\n   statement will bind this method\'s return value to the target(s)\n   specified in the ``as`` clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n   Exit the runtime context related to this object. The parameters\n   describe the exception that caused the context to be exited. If the\n   context was exited without an exception, all three arguments will\n   be ``None``.\n\n   If an exception is supplied, and the method wishes to suppress the\n   exception (i.e., prevent it from being propagated), it should\n   return a true value. Otherwise, the exception will be processed\n   normally upon exit from this method.\n\n   Note that ``__exit__()`` methods should not reraise the passed-in\n   exception; this is the caller\'s responsibility.\n\nSee also:\n\n   **PEP 0343** - The "with" statement\n      The specification, background, and examples for the Python\n      ``with`` statement.\n\n\nSpecial method lookup\n=====================\n\nFor custom classes, implicit invocations of special methods are only\nguaranteed to work correctly if defined on an object\'s type, not in\nthe object\'s instance dictionary.  That behaviour is the reason why\nthe following code raises an exception:\n\n   >>> class C(object):\n   ...     pass\n   ...\n   >>> c = C()\n   >>> c.__len__ = lambda: 5\n   >>> len(c)\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   TypeError: object of type \'C\' has no len()\n\nThe rationale behind this behaviour lies with a number of special\nmethods such as ``__hash__()`` and ``__repr__()`` that are implemented\nby all objects, including type objects. If the implicit lookup of\nthese methods used the conventional lookup process, they would fail\nwhen invoked on the type object itself:\n\n   >>> 1 .__hash__() == hash(1)\n   True\n   >>> int.__hash__() == hash(int)\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   TypeError: descriptor \'__hash__\' of \'int\' object needs an argument\n\nIncorrectly attempting to invoke an unbound method of a class in this\nway is sometimes referred to as \'metaclass confusion\', and is avoided\nby bypassing the instance when looking up special methods:\n\n   >>> type(1).__hash__(1) == hash(1)\n   True\n   >>> type(int).__hash__(int) == hash(int)\n   True\n\nIn addition to bypassing any instance attributes in the interest of\ncorrectness, implicit special method lookup may also bypass the\n``__getattribute__()`` method even of the object\'s metaclass:\n\n   >>> class Meta(type):\n   ...    def __getattribute__(*args):\n   ...       print("Metaclass getattribute invoked")\n   ...       return type.__getattribute__(*args)\n   ...\n   >>> class C(object, metaclass=Meta):\n   ...     def __len__(self):\n   ...         return 10\n   ...     def __getattribute__(*args):\n   ...         print("Class getattribute invoked")\n   ...         return object.__getattribute__(*args)\n   ...\n   >>> c = C()\n   >>> c.__len__()                 # Explicit lookup via instance\n   Class getattribute invoked\n   10\n   >>> type(c).__len__(c)          # Explicit lookup via type\n   Metaclass getattribute invoked\n   10\n   >>> len(c)                      # Implicit lookup\n   10\n\nBypassing the ``__getattribute__()`` machinery in this fashion\nprovides significant scope for speed optimisations within the\ninterpreter, at the cost of some flexibility in the handling of\nspecial methods (the special method *must* be set on the class object\nitself in order to be consistently invoked by the interpreter).\n\n-[ Footnotes ]-\n\n[1] It *is* possible in some cases to change an object\'s type, under\n    certain controlled conditions. It generally isn\'t a good idea\n    though, since it can lead to some very strange behaviour if it is\n    handled incorrectly.\n\n[2] A descriptor can define any combination of ``__get__()``,\n    ``__set__()`` and ``__delete__()``.  If it does not define\n    ``__get__()``, then accessing the attribute even on an instance\n    will return the descriptor object itself.  If the descriptor\n    defines ``__set__()`` and/or ``__delete__()``, it is a data\n    descriptor; if it defines neither, it is a non-data descriptor.\n\n[3] For operands of the same type, it is assumed that if the non-\n    reflected method (such as ``__add__()``) fails the operation is\n    not supported, which is why the reflected method is not called.\n',
  'string-methods': '\nString Methods\n**************\n\nString objects support the methods listed below.  Note that none of\nthese methods take keyword arguments.\n\nIn addition, Python\'s strings support the sequence type methods\ndescribed in the *Sequence Types --- str, bytes, bytearray, list,\ntuple, range* section. To output formatted strings, see the *String\nFormatting* section. Also, see the ``re`` module for string functions\nbased on regular expressions.\n\nstr.capitalize()\n\n   Return a copy of the string with only its first character\n   capitalized.\n\nstr.center(width[, fillchar])\n\n   Return centered in a string of length *width*. Padding is done\n   using the specified *fillchar* (default is a space).\n\nstr.count(sub[, start[, end]])\n\n   Return the number of occurrences of substring *sub* in the range\n   [*start*, *end*].  Optional arguments *start* and *end* are\n   interpreted as in slice notation.\n\nstr.encode([encoding[, errors]])\n\n   Return an encoded version of the string.  Default encoding is the\n   current default string encoding.  *errors* may be given to set a\n   different error handling scheme.  The default for *errors* is\n   ``\'strict\'``, meaning that encoding errors raise a\n   ``UnicodeError``.  Other possible values are ``\'ignore\'``,\n   ``\'replace\'``, ``\'xmlcharrefreplace\'``, ``\'backslashreplace\'`` and\n   any other name registered via ``codecs.register_error()``, see\n   section *Codec Base Classes*. For a list of possible encodings, see\n   section *Standard Encodings*.\n\nstr.endswith(suffix[, start[, end]])\n\n   Return ``True`` if the string ends with the specified *suffix*,\n   otherwise return ``False``.  *suffix* can also be a tuple of\n   suffixes to look for.  With optional *start*, test beginning at\n   that position.  With optional *end*, stop comparing at that\n   position.\n\nstr.expandtabs([tabsize])\n\n   Return a copy of the string where all tab characters are replaced\n   by one or more spaces, depending on the current column and the\n   given tab size.  The column number is reset to zero after each\n   newline occurring in the string. If *tabsize* is not given, a tab\n   size of ``8`` characters is assumed.  This doesn\'t understand other\n   non-printing characters or escape sequences.\n\nstr.find(sub[, start[, end]])\n\n   Return the lowest index in the string where substring *sub* is\n   found, such that *sub* is contained in the range [*start*, *end*].\n   Optional arguments *start* and *end* are interpreted as in slice\n   notation.  Return ``-1`` if *sub* is not found.\n\nstr.format(format_string, *args, **kwargs)\n\n   Perform a string formatting operation.  The *format_string*\n   argument can contain literal text or replacement fields delimited\n   by braces ``{}``.  Each replacement field contains either the\n   numeric index of a positional argument, or the name of a keyword\n   argument.  Returns a copy of *format_string* where each replacement\n   field is replaced with the string value of the corresponding\n   argument.\n\n   >>> "The sum of 1 + 2 is {0}".format(1+2)\n   \'The sum of 1 + 2 is 3\'\n\n   See *Format String Syntax* for a description of the various\n   formatting options that can be specified in format strings.\n\nstr.index(sub[, start[, end]])\n\n   Like ``find()``, but raise ``ValueError`` when the substring is not\n   found.\n\nstr.isalnum()\n\n   Return true if all characters in the string are alphanumeric and\n   there is at least one character, false otherwise.\n\nstr.isalpha()\n\n   Return true if all characters in the string are alphabetic and\n   there is at least one character, false otherwise.\n\nstr.isdecimal()\n\n   Return true if all characters in the string are decimal characters\n   and there is at least one character, false otherwise. Decimal\n   characters include digit characters, and all characters that that\n   can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-\n   INDIC DIGIT ZERO.\n\nstr.isdigit()\n\n   Return true if all characters in the string are digits and there is\n   at least one character, false otherwise.\n\nstr.isidentifier()\n\n   Return true if the string is a valid identifier according to the\n   language definition, section *Identifiers and keywords*.\n\nstr.islower()\n\n   Return true if all cased characters in the string are lowercase and\n   there is at least one cased character, false otherwise.\n\nstr.isnumeric()\n\n   Return true if all characters in the string are numeric characters,\n   and there is at least one character, false otherwise. Numeric\n   characters include digit characters, and all characters that have\n   the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION\n   ONE FIFTH.\n\nstr.isprintable()\n\n   Return true if all characters in the string are printable or the\n   string is empty, false otherwise.  Nonprintable characters are\n   those characters defined in the Unicode character database as\n   "Other" or "Separator", excepting the ASCII space (0x20) which is\n   considered printable.  (Note that printable characters in this\n   context are those which should not be escaped when ``repr()`` is\n   invoked on a string.  It has no bearing on the handling of strings\n   written to ``sys.stdout`` or ``sys.stderr``.)\n\nstr.isspace()\n\n   Return true if there are only whitespace characters in the string\n   and there is at least one character, false otherwise.\n\nstr.istitle()\n\n   Return true if the string is a titlecased string and there is at\n   least one character, for example uppercase characters may only\n   follow uncased characters and lowercase characters only cased ones.\n   Return false otherwise.\n\nstr.isupper()\n\n   Return true if all cased characters in the string are uppercase and\n   there is at least one cased character, false otherwise.\n\nstr.join(seq)\n\n   Return a string which is the concatenation of the strings in the\n   sequence *seq*.  A ``TypeError`` will be raised if there are any\n   non-string values in *seq*, including ``bytes`` objects.  The\n   separator between elements is the string providing this method.\n\nstr.ljust(width[, fillchar])\n\n   Return the string left justified in a string of length *width*.\n   Padding is done using the specified *fillchar* (default is a\n   space).  The original string is returned if *width* is less than\n   ``len(s)``.\n\nstr.lower()\n\n   Return a copy of the string converted to lowercase.\n\nstr.lstrip([chars])\n\n   Return a copy of the string with leading characters removed.  The\n   *chars* argument is a string specifying the set of characters to be\n   removed.  If omitted or ``None``, the *chars* argument defaults to\n   removing whitespace.  The *chars* argument is not a prefix; rather,\n   all combinations of its values are stripped:\n\n   >>> \'   spacious   \'.lstrip()\n   \'spacious   \'\n   >>> \'www.example.com\'.lstrip(\'cmowz.\')\n   \'example.com\'\n\nstr.maketrans(x[, y[, z]])\n\n   This static method returns a translation table usable for\n   ``str.translate()``.\n\n   If there is only one argument, it must be a dictionary mapping\n   Unicode ordinals (integers) or characters (strings of length 1) to\n   Unicode ordinals, strings (of arbitrary lengths) or None.\n   Character keys will then be converted to ordinals.\n\n   If there are two arguments, they must be strings of equal length,\n   and in the resulting dictionary, each character in x will be mapped\n   to the character at the same position in y.  If there is a third\n   argument, it must be a string, whose characters will be mapped to\n   None in the result.\n\nstr.partition(sep)\n\n   Split the string at the first occurrence of *sep*, and return a\n   3-tuple containing the part before the separator, the separator\n   itself, and the part after the separator.  If the separator is not\n   found, return a 3-tuple containing the string itself, followed by\n   two empty strings.\n\nstr.replace(old, new[, count])\n\n   Return a copy of the string with all occurrences of substring *old*\n   replaced by *new*.  If the optional argument *count* is given, only\n   the first *count* occurrences are replaced.\n\nstr.rfind(sub[, start[, end]])\n\n   Return the highest index in the string where substring *sub* is\n   found, such that *sub* is contained within s[start,end].  Optional\n   arguments *start* and *end* are interpreted as in slice notation.\n   Return ``-1`` on failure.\n\nstr.rindex(sub[, start[, end]])\n\n   Like ``rfind()`` but raises ``ValueError`` when the substring *sub*\n   is not found.\n\nstr.rjust(width[, fillchar])\n\n   Return the string right justified in a string of length *width*.\n   Padding is done using the specified *fillchar* (default is a\n   space). The original string is returned if *width* is less than\n   ``len(s)``.\n\nstr.rpartition(sep)\n\n   Split the string at the last occurrence of *sep*, and return a\n   3-tuple containing the part before the separator, the separator\n   itself, and the part after the separator.  If the separator is not\n   found, return a 3-tuple containing two empty strings, followed by\n   the string itself.\n\nstr.rsplit([sep[, maxsplit]])\n\n   Return a list of the words in the string, using *sep* as the\n   delimiter string. If *maxsplit* is given, at most *maxsplit* splits\n   are done, the *rightmost* ones.  If *sep* is not specified or\n   ``None``, any whitespace string is a separator.  Except for\n   splitting from the right, ``rsplit()`` behaves like ``split()``\n   which is described in detail below.\n\nstr.rstrip([chars])\n\n   Return a copy of the string with trailing characters removed.  The\n   *chars* argument is a string specifying the set of characters to be\n   removed.  If omitted or ``None``, the *chars* argument defaults to\n   removing whitespace.  The *chars* argument is not a suffix; rather,\n   all combinations of its values are stripped:\n\n   >>> \'   spacious   \'.rstrip()\n   \'   spacious\'\n   >>> \'mississippi\'.rstrip(\'ipz\')\n   \'mississ\'\n\nstr.split([sep[, maxsplit]])\n\n   Return a list of the words in the string, using *sep* as the\n   delimiter string.  If *maxsplit* is given, at most *maxsplit*\n   splits are done (thus, the list will have at most ``maxsplit+1``\n   elements).  If *maxsplit* is not specified, then there is no limit\n   on the number of splits (all possible splits are made).\n\n   If *sep* is given, consecutive delimiters are not grouped together\n   and are deemed to delimit empty strings (for example,\n   ``\'1,,2\'.split(\',\')`` returns ``[\'1\', \'\', \'2\']``).  The *sep*\n   argument may consist of multiple characters (for example,\n   ``\'1<>2<>3\'.split(\'<>\')`` returns ``[\'1\', \'2\', \'3\']``). Splitting\n   an empty string with a specified separator returns ``[\'\']``.\n\n   If *sep* is not specified or is ``None``, a different splitting\n   algorithm is applied: runs of consecutive whitespace are regarded\n   as a single separator, and the result will contain no empty strings\n   at the start or end if the string has leading or trailing\n   whitespace.  Consequently, splitting an empty string or a string\n   consisting of just whitespace with a ``None`` separator returns\n   ``[]``.\n\n   For example, ``\' 1  2   3  \'.split()`` returns ``[\'1\', \'2\', \'3\']``,\n   and ``\'  1  2   3  \'.split(None, 1)`` returns ``[\'1\', \'2   3  \']``.\n\nstr.splitlines([keepends])\n\n   Return a list of the lines in the string, breaking at line\n   boundaries.  Line breaks are not included in the resulting list\n   unless *keepends* is given and true.\n\nstr.startswith(prefix[, start[, end]])\n\n   Return ``True`` if string starts with the *prefix*, otherwise\n   return ``False``. *prefix* can also be a tuple of prefixes to look\n   for.  With optional *start*, test string beginning at that\n   position.  With optional *end*, stop comparing string at that\n   position.\n\nstr.strip([chars])\n\n   Return a copy of the string with the leading and trailing\n   characters removed. The *chars* argument is a string specifying the\n   set of characters to be removed. If omitted or ``None``, the\n   *chars* argument defaults to removing whitespace. The *chars*\n   argument is not a prefix or suffix; rather, all combinations of its\n   values are stripped:\n\n   >>> \'   spacious   \'.strip()\n   \'spacious\'\n   >>> \'www.example.com\'.strip(\'cmowz.\')\n   \'example\'\n\nstr.swapcase()\n\n   Return a copy of the string with uppercase characters converted to\n   lowercase and vice versa.\n\nstr.title()\n\n   Return a titlecased version of the string: words start with\n   uppercase characters, all remaining cased characters are lowercase.\n\nstr.translate(map)\n\n   Return a copy of the *s* where all characters have been mapped\n   through the *map* which must be a dictionary of Unicode\n   ordinals(integers) to Unicode ordinals, strings or ``None``.\n   Unmapped characters are left untouched. Characters mapped to\n   ``None`` are deleted.\n\n   A *map* for ``translate()`` is usually best created by\n   ``str.maketrans()``.\n\n   You can use the ``maketrans()`` helper function in the ``string``\n   module to create a translation table. For string objects, set the\n   *table* argument to ``None`` for translations that only delete\n   characters:\n\n   Note: An even more flexible approach is to create a custom character\n     mapping codec using the ``codecs`` module (see\n     ``encodings.cp1251`` for an example).\n\nstr.upper()\n\n   Return a copy of the string converted to uppercase.\n\nstr.zfill(width)\n\n   Return the numeric string left filled with zeros in a string of\n   length *width*.  A sign prefix is handled correctly.  The original\n   string is returned if *width* is less than ``len(s)``.\n',
  'strings': '\nString and Bytes literals\n*************************\n\nString literals are described by the following lexical definitions:\n\n   stringliteral   ::= [stringprefix](shortstring | longstring)\n   stringprefix    ::= "r" | "R"\n   shortstring     ::= "\'" shortstringitem* "\'" | \'"\' shortstringitem* \'"\'\n   longstring      ::= "\'\'\'" longstringitem* "\'\'\'" | \'"""\' longstringitem* \'"""\'\n   shortstringitem ::= shortstringchar | stringescapeseq\n   longstringitem  ::= longstringchar | stringescapeseq\n   shortstringchar ::= <any source character except "\\" or newline or the quote>\n   longstringchar  ::= <any source character except "\\">\n   stringescapeseq ::= "\\" <any source character>\n\n   bytesliteral   ::= bytesprefix(shortbytes | longbytes)\n   bytesprefix    ::= "b" | "B"\n   shortbytes     ::= "\'" shortbytesitem* "\'" | \'"\' shortbytesitem* \'"\'\n   longbytes      ::= "\'\'\'" longbytesitem* "\'\'\'" | \'"""\' longbytesitem* \'"""\'\n   shortbytesitem ::= shortbyteschar | bytesescapeseq\n   longbytesitem  ::= longbyteschar | bytesescapeseq\n   shortbyteschar ::= <any ASCII character except "\\" or newline or the quote>\n   longbyteschar  ::= <any ASCII character except "\\">\n   bytesescapeseq ::= "\\" <any ASCII character>\n\nOne syntactic restriction not indicated by these productions is that\nwhitespace is not allowed between the **stringprefix** or\n**bytesprefix** and the rest of the literal. The source character set\nis defined by the encoding declaration; it is UTF-8 if no encoding\ndeclaration is given in the source file; see section *Encoding\ndeclarations*.\n\nIn plain English: Both types of literals can be enclosed in matching\nsingle quotes (``\'``) or double quotes (``"``).  They can also be\nenclosed in matching groups of three single or double quotes (these\nare generally referred to as *triple-quoted strings*).  The backslash\n(``\\``) character is used to escape characters that otherwise have a\nspecial meaning, such as newline, backslash itself, or the quote\ncharacter.\n\nString literals may optionally be prefixed with a letter ``\'r\'`` or\n``\'R\'``; such strings are called *raw strings* and treat backslashes\nas literal characters.  As a result, ``\'\\U\'`` and ``\'\\u\'`` escapes in\nraw strings are not treated specially.\n\nBytes literals are always prefixed with ``\'b\'`` or ``\'B\'``; they\nproduce an instance of the ``bytes`` type instead of the ``str`` type.\nThey may only contain ASCII characters; bytes with a numeric value of\n128 or greater must be expressed with escapes.\n\nIn triple-quoted strings, unescaped newlines and quotes are allowed\n(and are retained), except that three unescaped quotes in a row\nterminate the string.  (A "quote" is the character used to open the\nstring, i.e. either ``\'`` or ``"``.)\n\nUnless an ``\'r\'`` or ``\'R\'`` prefix is present, escape sequences in\nstrings are interpreted according to rules similar to those used by\nStandard C.  The recognized escape sequences are:\n\n+-------------------+-----------------------------------+---------+\n| Escape Sequence   | Meaning                           | Notes   |\n+===================+===================================+=========+\n| ``\\newline``      | Backslash and newline ignored     |         |\n+-------------------+-----------------------------------+---------+\n| ``\\\\``            | Backslash (``\\``)                 |         |\n+-------------------+-----------------------------------+---------+\n| ``\\\'``            | Single quote (``\'``)              |         |\n+-------------------+-----------------------------------+---------+\n| ``\\"``            | Double quote (``"``)              |         |\n+-------------------+-----------------------------------+---------+\n| ``\\a``            | ASCII Bell (BEL)                  |         |\n+-------------------+-----------------------------------+---------+\n| ``\\b``            | ASCII Backspace (BS)              |         |\n+-------------------+-----------------------------------+---------+\n| ``\\f``            | ASCII Formfeed (FF)               |         |\n+-------------------+-----------------------------------+---------+\n| ``\\n``            | ASCII Linefeed (LF)               |         |\n+-------------------+-----------------------------------+---------+\n| ``\\r``            | ASCII Carriage Return (CR)        |         |\n+-------------------+-----------------------------------+---------+\n| ``\\t``            | ASCII Horizontal Tab (TAB)        |         |\n+-------------------+-----------------------------------+---------+\n| ``\\v``            | ASCII Vertical Tab (VT)           |         |\n+-------------------+-----------------------------------+---------+\n| ``\\ooo``          | Character with octal value *ooo*  | (1,3)   |\n+-------------------+-----------------------------------+---------+\n| ``\\xhh``          | Character with hex value *hh*     | (2,3)   |\n+-------------------+-----------------------------------+---------+\n\nEscape sequences only recognized in string literals are:\n\n+-------------------+-----------------------------------+---------+\n| Escape Sequence   | Meaning                           | Notes   |\n+===================+===================================+=========+\n| ``\\N{name}``      | Character named *name* in the     |         |\n|                   | Unicode database                  |         |\n+-------------------+-----------------------------------+---------+\n| ``\\uxxxx``        | Character with 16-bit hex value   | (4)     |\n|                   | *xxxx*                            |         |\n+-------------------+-----------------------------------+---------+\n| ``\\Uxxxxxxxx``    | Character with 32-bit hex value   | (5)     |\n|                   | *xxxxxxxx*                        |         |\n+-------------------+-----------------------------------+---------+\n\nNotes:\n\n1. As in Standard C, up to three octal digits are accepted.\n\n2. Unlike in Standard C, at most two hex digits are accepted.\n\n3. In a bytes literal, hexadecimal and octal escapes denote the byte\n   with the given value. In a string literal, these escapes denote a\n   Unicode character with the given value.\n\n4. Individual code units which form parts of a surrogate pair can be\n   encoded using this escape sequence. Unlike in Standard C, exactly\n   two hex digits are required.\n\n5. Any Unicode character can be encoded this way, but characters\n   outside the Basic Multilingual Plane (BMP) will be encoded using a\n   surrogate pair if Python is compiled to use 16-bit code units (the\n   default).  Individual code units which form parts of a surrogate\n   pair can be encoded using this escape sequence.\n\nUnlike Standard C, all unrecognized escape sequences are left in the\nstring unchanged, i.e., *the backslash is left in the string*.  (This\nbehavior is useful when debugging: if an escape sequence is mistyped,\nthe resulting output is more easily recognized as broken.)  It is also\nimportant to note that the escape sequences only recognized in string\nliterals fall into the category of unrecognized escapes for bytes\nliterals.\n\nEven in a raw string, string quotes can be escaped with a backslash,\nbut the backslash remains in the string; for example, ``r"\\""`` is a\nvalid string literal consisting of two characters: a backslash and a\ndouble quote; ``r"\\"`` is not a valid string literal (even a raw\nstring cannot end in an odd number of backslashes).  Specifically, *a\nraw string cannot end in a single backslash* (since the backslash\nwould escape the following quote character).  Note also that a single\nbackslash followed by a newline is interpreted as those two characters\nas part of the string, *not* as a line continuation.\n',
  'subscriptions': '\nSubscriptions\n*************\n\nA subscription selects an item of a sequence (string, tuple or list)\nor mapping (dictionary) object:\n\n   subscription ::= primary "[" expression_list "]"\n\nThe primary must evaluate to an object that supports subscription,\ne.g. a list or dictionary.  User-defined objects can support\nsubscription by defining a ``__getitem__()`` method.\n\nFor built-in objects, there are two types of objects that support\nsubscription:\n\nIf the primary is a mapping, the expression list must evaluate to an\nobject whose value is one of the keys of the mapping, and the\nsubscription selects the value in the mapping that corresponds to that\nkey.  (The expression list is a tuple except if it has exactly one\nitem.)\n\nIf the primary is a sequence, the expression (list) must evaluate to\nan integer. If this value is negative, the length of the sequence is\nadded to it (so that, e.g., ``x[-1]`` selects the last item of ``x``.)\nThe resulting value must be a nonnegative integer less than the number\nof items in the sequence, and the subscription selects the item whose\nindex is that value (counting from zero).\n\nA string\'s items are characters.  A character is not a separate data\ntype but a string of exactly one character.\n',