# [Python-checkins] cpython (3.4): Updated pydoc topics for 3.4.1rc1 release.

larry.hastings python-checkins at python.org
Mon Sep 22 16:21:30 CEST 2014

https://hg.python.org/cpython/rev/ba1dd18c3089
changeset:   92518:ba1dd18c3089
branch:      3.4
parent:      92497:7af0315bdfe0
user:        Larry Hastings <larry at hastings.org>
date:        Sun Sep 21 00:05:05 2014 +0100
summary:
Updated pydoc topics for 3.4.1rc1 release.

files:
Lib/pydoc_data/topics.py |  156 +++++++++++++-------------
1 files changed, 78 insertions(+), 78 deletions(-)

diff --git a/Lib/pydoc_data/topics.py b/Lib/pydoc_data/topics.py
--- a/Lib/pydoc_data/topics.py
+++ b/Lib/pydoc_data/topics.py
@@ -1,79 +1,79 @@
# -*- coding: utf-8 -*-
-# Autogenerated by Sphinx on Sat May 17 21:42:09 2014
-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 to\n\n   if __debug__:\n      if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that "__debug__" and "AssertionError" refer\nto 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 (command\nline option -O).  The current code generator emits no code for an\nassert statement when optimization is requested at compile time.  Note\nthat it is unnecessary to include the source code for the expression\nthat failed in the error message; it will be displayed as part of the\nstack trace.\n\nAssignments to "__debug__" are illegal.  The value for the built-in\nvariable is determined when the interpreter starts.\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, with leading underscores removed and a single underscore\ninserted, in front of the name.  For example, the identifier "__spam"\noccurring in a class named "Ham" will be transformed to "_Ham__spam".\nThis transformation is independent of the syntactical context in which\nthe identifier is used.  If the transformed name is extremely long\n(longer than 255 characters), implementation defined truncation may\nhappen. If the class name consists only of underscores, no\ntransformation 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\nAll literals correspond to immutable data types, and hence the\nobject's identity is less important than its value.  Multiple\nevaluations of literals with the same value (either the same\noccurrence in the program text or a different occurrence) may obtain\nthe same object or a different object with 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") for\nclass 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. This\n   method should return the (computed) attribute value or raise an\n   "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 for\n   efficiency reasons and because otherwise "__getattr__()" would have\n   no way to access other attributes of the instance.  Note that at\n   least for instance variables, you can fake total control by not\n   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 "__getattr__()",\n   the latter will not be called unless "__getattribute__()" either\n   calls it explicitly or raises an "AttributeError". This method\n   should return the (computed) attribute value or raise an\n   "AttributeError" exception. In order to avoid infinite recursion in\n   this method, its implementation should always call the base class\n   method with the same name to access any attributes it needs, for\n   example, "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     built-in 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 sequence must be\n   returned. "dir()" converts the returned sequence to a list and\n   sorts it.\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 an\n*owner* class (the descriptor must be in either the owner\'s class\ndictionary or in the class dictionary for one of its parents).  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 "AttributeError"\n   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\nThe attribute "__objclass__" is interpreted by the "inspect" module as\nspecifying the class where this object was defined (setting this\nappropriately can assist in runtime introspection of dynamic class\nattributes). For callables, it may indicate that an instance of the\ngiven type (or a subclass) is expected or required as the first\npositional argument (for example, CPython sets this attribute for\nunbound methods that are implemented in C).\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, it\nis 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". How\nthe 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 "A"\n   immediately preceding "B" and then invokes the descriptor with the\n   call: "A.__dict__[\'m\'].__get__(obj, obj.__class__)".\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  A descriptor can define\nany combination of "__get__()", "__set__()" and "__delete__()".  If it\ndoes not define "__get__()", then accessing the attribute will return\nthe descriptor object itself unless there is a value in the object\'s\ninstance dictionary.  If the descriptor defines "__set__()" and/or\n"__delete__()", it is a data descriptor; if it defines neither, it is\na non-data descriptor.  Normally, data descriptors define both\n"__get__()" and "__set__()", while non-data descriptors have just the\n"__get__()" method.  Data descriptors with "__set__()" and "__get__()"\ndefined always override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances.\n\nPython methods (including "staticmethod()" and "classmethod()") are\nimplemented 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* 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__* (which must only contain names\n  of any *additional* slots).\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* Nonempty *__slots__* does not work for classes derived from\n  "variable-length" built-in types such as "int", "bytes" 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-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 raised.\nOtherwise, the type and value of the object produced is determined by\nthe object.  Multiple evaluations of the same attribute reference may\nyield 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 ::= augtarget augop (expression_list | yield_expression)\n   augtarget                 ::= identifier | attributeref | subscription | slicing\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 the\naugmented version, "x" is only evaluated once. Also, when possible,\nthe actual operation is performed *in-place*, meaning that rather than\ncreating a new object and assigning that to the target, the old object\nis 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 same *caveat about\nclass and instance attributes* applies as for regular assignments.\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 arguments.\nThe arguments must either both be numbers, or one argument must be an\ninteger and the other must be a sequence. In the former case, the\nnumbers are converted to a common type and then multiplied together.\nIn the latter case, sequence repetition is performed; a negative\nrepetition 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. Division of integers 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 "4*0.7 +\n0.34".)  The modulo operator always yields a result with the same sign\nas its second operand (or zero); the absolute value of the result is\nstrictly smaller than the absolute value of the second operand [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 also\nconnected 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 *printf-style String Formatting*.\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 arguments.\nThe numeric arguments are first converted to a common type.\n',
- 'bitwise': '\nBinary bitwise operations\n*************************\n\nEach of the three bitwise operations has a different priority level:\n\n   and_expr ::= shift_expr | and_expr "&" shift_expr\n   xor_expr ::= and_expr | xor_expr "^" and_expr\n   or_expr  ::= xor_expr | or_expr "|" xor_expr\n\nThe "&" operator yields the bitwise AND of its arguments, which must\nbe integers.\n\nThe "^" operator yields the bitwise XOR (exclusive OR) of its\narguments, which must be integers.\n\nThe "|" operator yields the bitwise (inclusive) OR of its arguments,\nwhich must be integers.\n',
- 'bltin-code-objects': '\nCode Objects\n************\n\nCode objects are used by the implementation to represent "pseudo-\ncompiled" executable Python code such as a function body. They differ\nfrom function objects because they don\'t contain a reference to their\nglobal execution environment.  Code objects are returned by the built-\nin "compile()" function and can be extracted from function objects\nthrough their "__code__" attribute. See also the "code" module.\n\nA code object can be executed or evaluated by passing it (instead of a\nsource string) to the "exec()" or "eval()"  built-in functions.\n\nSee *The standard type hierarchy* for more information.\n',
- 'bltin-ellipsis-object': '\nThe Ellipsis Object\n*******************\n\nThis object is commonly used by slicing (see *Slicings*).  It supports\nno special operations.  There is exactly one ellipsis object, named\n"Ellipsis" (a built-in name).  "type(Ellipsis)()" produces the\n"Ellipsis" singleton.\n\nIt is written as "Ellipsis" or "...".\n',
- 'bltin-null-object': '\nThe Null Object\n***************\n\nThis object is returned by functions that don\'t explicitly return a\nvalue.  It supports no special operations.  There is exactly one null\nobject, named "None" (a built-in name).  "type(None)()" produces the\nsame singleton.\n\nIt is written as "None".\n',
- 'bltin-type-objects': '\nType Objects\n************\n\nType objects represent the various object types.  An object\'s type is\naccessed by the built-in function "type()".  There are no special\noperations on types.  The standard module "types" defines names for\nall standard built-in types.\n\nTypes are written like this: "<class \'int\'>".\n',
- 'booleans': '\nBoolean operations\n******************\n\n   or_test  ::= and_test | or_test "or" and_test\n   and_test ::= not_test | and_test "and" not_test\n   not_test ::= comparison | "not" not_test\n\nIn the context of Boolean operations, and also when expressions are\nused by control flow statements, the following values are interpreted\nas false: "False", "None", numeric zero of all types, and empty\nstrings and containers (including strings, tuples, lists,\ndictionaries, sets and frozensets).  All other values are interpreted\nas true.  User-defined objects can customize their truth value by\nproviding a "__bool__()" method.\n\nThe operator "not" yields "True" if its argument is false, "False"\notherwise.\n\nThe expression "x and y" first evaluates *x*; if *x* is false, its\nvalue is returned; otherwise, *y* is evaluated and the resulting value\nis returned.\n\nThe expression "x or y" first evaluates *x*; if *x* is true, its value\nis returned; otherwise, *y* is evaluated and the resulting value is\nreturned.\n\n(Note that neither "and" nor "or" restrict the value and type they\nreturn to "False" and "True", but rather return the last evaluated\nargument.  This is sometimes useful, e.g., if "s" is a string that\nshould be replaced by a default value if it is empty, the expression\n"s or \'foo\'" yields the desired value.  Because "not" has to invent a\nvalue anyway, it does not bother to return a value of the same type as\nits argument, so e.g., "not \'foo\'" yields "False", not "\'\'".)\n',
- 'break': '\nThe "break" statement\n*********************\n\n   break_stmt ::= "break"\n\n"break" may only occur syntactically nested in a "for" or "while"\nloop, but not nested in a function or class definition within that\nloop.\n\nIt terminates the nearest enclosing loop, skipping the optional "else"\nclause if the loop has one.\n\nIf a "for" loop is terminated by "break", the loop control target\nkeeps its current value.\n\nWhen "break" passes control out of a "try" statement with a "finally"\nclause, that "finally" clause is executed before really leaving the\nloop.\n',
- 'callable-types': '\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',
- 'calls': '\nCalls\n*****\n\nA call calls a callable object (e.g., a *function*) with a possibly\nempty series of *arguments*:\n\n   call                 ::= primary "(" [argument_list [","] | comprehension] ")"\n   argument_list        ::= positional_arguments ["," keyword_arguments]\n                       ["," "*" expression] ["," keyword_arguments]\n                       ["," "**" expression]\n                     | keyword_arguments ["," "*" expression]\n                       ["," keyword_arguments] ["," "**" expression]\n                     | "*" expression ["," keyword_arguments] ["," "**" expression]\n                     | "**" expression\n   positional_arguments ::= expression ("," expression)*\n   keyword_arguments    ::= keyword_item ("," keyword_item)*\n   keyword_item         ::= identifier "=" expression\n\nA trailing comma may be present after the positional and keyword\narguments but does not affect the semantics.\n\nThe primary must evaluate to a callable object (user-defined\nfunctions, built-in functions, methods of built-in objects, class\nobjects, methods of class instances, and all objects having a\n"__call__()" method are callable).  All argument expressions are\nevaluated before the call is attempted.  Please refer to section\n*Function definitions* for the syntax of formal *parameter* lists.\n\nIf keyword arguments are present, they are first converted to\npositional arguments, as follows.  First, a list of unfilled slots is\ncreated for the formal parameters.  If there are N positional\narguments, they are placed in the first N slots.  Next, for each\nkeyword argument, the identifier is used to determine the\ncorresponding slot (if the identifier is the same as the first formal\nparameter name, the first slot is used, and so on).  If the slot is\nalready filled, a "TypeError" exception is raised. Otherwise, the\nvalue of the argument is placed in the slot, filling it (even if the\nexpression is "None", it fills the slot).  When all arguments have\nbeen processed, the slots that are still unfilled are filled with the\ncorresponding default value from the function definition.  (Default\nvalues are calculated, once, when the function is defined; thus, a\nmutable object such as a list or dictionary used as default value will\nbe shared by all calls that don\'t specify an argument value for the\ncorresponding slot; this should usually be avoided.)  If there are any\nunfilled slots for which no default value is specified, a "TypeError"\nexception is raised.  Otherwise, the list of filled slots is used as\nthe argument list for the call.\n\n**CPython implementation detail:** An implementation may provide\nbuilt-in functions whose positional parameters do not have names, even\nif they are \'named\' for the purpose of documentation, and which\ntherefore cannot be supplied by keyword.  In CPython, this is the case\nfor functions implemented in C that use "PyArg_ParseTuple()" to parse\ntheir arguments.\n\nIf there are more positional arguments than there are formal parameter\nslots, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "*identifier" is present; in this case, that formal\nparameter receives a tuple containing the excess positional arguments\n(or an empty tuple if there were no excess positional arguments).\n\nIf any keyword argument does not correspond to a formal parameter\nname, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "**identifier" is present; in this case, that formal\nparameter receives a dictionary containing the excess keyword\narguments (using the keywords as keys and the argument values as\ncorresponding values), or a (new) empty dictionary if there were no\nexcess keyword arguments.\n\nIf the syntax "*expression" appears in the function call, "expression"\nmust evaluate to an iterable.  Elements from this iterable are treated\nas if they were additional positional arguments; if there are\npositional arguments *x1*, ..., *xN*, and "expression" evaluates to a\nsequence *y1*, ..., *yM*, this is equivalent to a call with M+N\npositional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.\n\nA consequence of this is that although the "*expression" syntax may\nappear *after* some keyword arguments, it is processed *before* the\nkeyword arguments (and the "**expression" argument, if any -- see\nbelow).  So:\n\n   >>> def f(a, b):\n   ...  print(a, b)\n   ...\n   >>> f(b=1, *(2,))\n   2 1\n   >>> f(a=1, *(2,))\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in ?\n   TypeError: f() got multiple values for keyword argument \'a\'\n   >>> f(1, *(2,))\n   1 2\n\nIt is unusual for both keyword arguments and the "*expression" syntax\nto be used in the same call, so in practice this confusion does not\narise.\n\nIf the syntax "**expression" appears in the function call,\n"expression" must evaluate to a mapping, the contents of which are\ntreated as additional keyword arguments.  In the case of a keyword\nappearing in both "expression" and as an explicit keyword argument, a\n"TypeError" exception is raised.\n\nFormal parameters using the syntax "*identifier" or "**identifier"\ncannot be used as positional argument slots or as keyword argument\nnames.\n\nA call always returns some value, possibly "None", unless it raises an\nexception.  How this value is computed depends on the type of the\ncallable object.\n\nIf it is---\n\na user-defined function:\n   The code block for the function is executed, passing it the\n   argument list.  The first thing the code block will do is bind the\n   formal parameters to the arguments; this is described in section\n   *Function definitions*.  When the code block executes a "return"\n   statement, this specifies the return value of the function call.\n\na built-in function or method:\n   The result is up to the interpreter; see *Built-in Functions* for\n   the descriptions of built-in functions and methods.\n\na class object:\n   A new instance of that class is returned.\n\na class instance method:\n   The corresponding user-defined function is called, with an argument\n   list that is one longer than the argument list of the call: the\n   instance becomes the first argument.\n\na class instance:\n   The class must define a "__call__()" method; the effect is then the\n   same as if that method was called.\n',
- 'class': '\nClass definitions\n*****************\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n   classdef    ::= [decorators] "class" classname [inheritance] ":" suite\n   inheritance ::= "(" [parameter_list] ")"\n   classname   ::= identifier\n\nA class definition is an executable statement.  The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing.  Classes without an inheritance\nlist inherit, by default, from the base class "object"; hence,\n\n   class Foo:\n       pass\n\nis equivalent to\n\n   class Foo(object):\n       pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.)  When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n   @f1(arg)\n   @f2\n   class Foo: pass\n\nis equivalent to\n\n   class Foo: pass\n   Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators.  The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances.  Instance attributes\ncan be set in a method with "self.name = value".  Both class and\ninstance attributes are accessible through the notation ""self.name"",\nand an instance attribute hides a class attribute with the same name\nwhen accessed in this way.  Class attributes can be used as defaults\nfor instance attributes, but using mutable values there can lead to\nunexpected results.  *Descriptors* can be used to create instance\nvariables with different implementation details.\n\nSee also:\n\n   **PEP 3115** - Metaclasses in Python 3 **PEP 3129** - Class\n   Decorators\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless there\n    is a "finally" clause which happens to raise another exception.\n    That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of an\n    exception or the execution of a "return", "continue", or "break"\n    statement.\n\n[3] A string literal appearing as the first statement in the function\n    body is transformed into the function\'s "__doc__" attribute and\n    therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n    body is transformed into the namespace\'s "__doc__" item and\n    therefore the class\'s *docstring*.\n',
- 'comparisons': '\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 only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound 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", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. The are\n  identical to themselves, "x is x" but are not equal to themselves,\n  "x != x".  Additionally, comparing any value to a not-a-number value\n  will return "False".  For example, both "3 < float(\'NaN\')" and\n  "float(\'NaN\') < 3" will return "False".\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, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in 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\nComparison of objects of the differing types depends on whether either\nof the types provide explicit support for the comparison.  Most\nnumeric types can be compared with one another.  When cross-type\ncomparison is not supported, the comparison method returns\n"NotImplemented".\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" tests\nwhether a the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative 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 y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
- '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 manager\nhandles the entry into, and the exit from, the desired runtime context\nfor the execution of the block of code.  Context managers are normally\ninvoked using the "with" statement (described in section *The with\nstatement*), but can also be used by directly invoking their methods.\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 "while"\nloop, but not nested in a function or class definition or "finally"\nclause within that loop.  It continues with the next cycle of the\nnearest 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',
- '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 than 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\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\nChanged in version 3.2: Previously it was illegal to delete a name\nfrom the local namespace if it occurs as a free variable in a nested\nblock.\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',
- 'dynamic-features': '\nInteraction with dynamic features\n*********************************\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name.  An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nThe "eval()" and "exec()" functions do not have access to the full\nenvironment for resolving names.  Names may be resolved in the local\nand global namespaces of the caller.  Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace.  [1]\nThe "exec()" and "eval()" functions have optional arguments to\noverride the global and local namespace.  If only one namespace is\nspecified, it is used for both.\n',
- 'else': '\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',
- 'exceptions': '\nExceptions\n**********\n\nExceptions are a means of breaking out of the normal flow of control\nof a code block in order to handle errors or other exceptional\nconditions.  An exception is *raised* at the point where the error is\ndetected; it may be *handled* by the surrounding code block or by any\ncode block that directly or indirectly invoked the code block where\nthe error occurred.\n\nThe Python interpreter raises an exception when it detects a run-time\nerror (such as division by zero).  A Python program can also\nexplicitly raise an exception with the "raise" statement. Exception\nhandlers are specified with the "try" ... "except" statement.  The\n"finally" clause of such a statement can be used to specify cleanup\ncode which does not handle the exception, but is executed whether an\nexception occurred or not in the preceding code.\n\nPython uses the "termination" model of error handling: an exception\nhandler can find out what happened and continue execution at an outer\nlevel, but it cannot repair the cause of the error and retry the\nfailing operation (except by re-entering the offending piece of code\nfrom the top).\n\nWhen an exception is not handled at all, the interpreter terminates\nexecution of the program, or returns to its interactive main loop.  In\neither case, it prints a stack backtrace, except when the exception is\n"SystemExit".\n\nExceptions are identified by class instances.  The "except" clause is\nselected depending on the class of the instance: it must reference the\nclass of the instance or a base class thereof.  The instance can be\nreceived by the handler and can carry additional information about the\nexceptional condition.\n\nNote: Exception messages are not part of the Python API.  Their contents\n  may change from one version of Python to the next without warning\n  and should not be relied on by code which will run under multiple\n  versions of the interpreter.\n\nSee also the description of the "try" statement in section *The try\nstatement* and "raise" statement in section *The raise statement*.\n\n-[ Footnotes ]-\n\n[1] This limitation occurs because the code that is executed by these\n    operations is not available at the time the module is compiled.\n',
- 'exprlists': '\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',
- 'floating': '\nFloating point literals\n***********************\n\nFloating point literals are described by the following lexical\ndefinitions:\n\n   floatnumber   ::= pointfloat | exponentfloat\n   pointfloat    ::= [intpart] fraction | intpart "."\n   exponentfloat ::= (intpart | pointfloat) exponent\n   intpart       ::= digit+\n   fraction      ::= "." digit+\n   exponent      ::= ("e" | "E") ["+" | "-"] digit+\n\nNote that the integer and exponent parts are always interpreted using\nradix 10. For example, "077e010" is legal, and denotes the same number\nas "77e10". The allowed range of floating point literals is\nimplementation-dependent. Some examples of floating point literals:\n\n   3.14    10.    .001    1e100    3.14e-10    0e0\n\nNote that numeric literals do not include a sign; a phrase like "-1"\nis actually an expression composed of the unary operator "-" and the\nliteral "1".\n',
- 'for': '\nThe "for" statement\n*******************\n\nThe "for" statement is used to iterate over the elements of a sequence\n(such as a string, tuple or list) or other iterable object:\n\n   for_stmt ::= "for" target_list "in" expression_list ":" suite\n                ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject.  An iterator is created for the result of the\n"expression_list".  The suite is then executed once for each item\nprovided by the iterator, in the order of ascending indices.  Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted.  When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a "StopIteration" exception),\nthe suite in the "else" clause, if present, is executed, and the loop\nterminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and continues\nwith the next item, or with the "else" clause if there was no next\nitem.\n\nThe suite may assign to the variable(s) in the target list; this does\nnot affect the next item assigned to it.\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, it will not have been assigned to at all\nby the loop.  Hint: the built-in function "range()" returns an\niterator of integers suitable to emulate the effect of Pascal\'s "for i\n:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".\n\nNote: There is a subtlety when the sequence is being modified by the loop\n  (this can only occur for mutable sequences, i.e. lists).  An\n  internal counter is used to keep track of which item is used next,\n  and this is incremented on each iteration.  When this counter has\n  reached the length of the sequence the loop terminates.  This means\n  that if the suite deletes the current (or a previous) item from the\n  sequence, the next item will be skipped (since it gets the index of\n  the current item which has already been treated).  Likewise, if the\n  suite inserts an item in the sequence before the current item, the\n  current item will be treated again the next time through the loop.\n  This can lead to nasty bugs that can be avoided by making a\n  temporary copy using a slice of the whole sequence, e.g.,\n\n     for x in a[:]:\n         if x < 0: a.remove(x)\n',
- 'function': '\nFunction definitions\n********************\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n   funcdef        ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n   decorators     ::= decorator+\n   decorator      ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n   dotted_name    ::= identifier ("." identifier)*\n   parameter_list ::= (defparameter ",")*\n                      ( "*" [parameter] ("," defparameter)* ["," "**" parameter]\n                      | "**" parameter\n                      | defparameter [","] )\n   parameter      ::= identifier [":" expression]\n   defparameter   ::= parameter ["=" expression]\n   funcname       ::= identifier\n\nA function definition is an executable statement.  Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function).  This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition.  The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object.  Multiple decorators are applied in\nnested fashion. For example, the following code\n\n   @f1(arg)\n   @f2\n   def func(): pass\n\nis equivalent to\n\n   def func(): pass\n   func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* "="\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted.  If a parameter has a default value, all following\nparameters up until the ""*"" must also have a default value --- this\nis a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call.  This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended.  A way around this is to use\n"None" as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n   def whats_on_the_telly(penguin=None):\n       if penguin is None:\n           penguin = []\n       penguin.append("property of the zoo")\n       return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values.  If the form\n""*identifier"" is present, it is initialized to a tuple receiving any\nexcess positional parameters, defaulting to the empty tuple.  If the\nform ""**identifier"" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after ""*"" or ""*identifier"" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form "": expression"" following\nthe parameter name.  Any parameter may have an annotation even those\nof the form "*identifier" or "**identifier".  Functions may have\n"return" annotation of the form ""-> expression"" after the parameter\nlist.  These annotations can be any valid Python expression and are\nevaluated when the function definition is executed.  Annotations may\nbe evaluated in a different order than they appear in the source code.\nThe presence of annotations does not change the semantics of a\nfunction.  The annotation values are available as values of a\ndictionary keyed by the parameters\' names in the "__annotations__"\nattribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions.  This uses lambda\nexpressions, described in section *Lambdas*.  Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression.  The ""def"" form is actually more powerful since it\nallows the execution of multiple statements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects.  A ""def""\nstatement executed inside a function definition defines a local\nfunction that can be returned or passed around.  Free variables used\nin the nested function can access the local variables of the function\ncontaining the def.  See section *Naming and binding* for details.\n\nSee also:\n\n   **PEP 3107** - Function Annotations\n      The original specification for function annotations.\n',
- 'global': '\nThe "global" statement\n**********************\n\n   global_stmt ::= "global" identifier ("," identifier)*\n\nThe "global" statement is a declaration which holds for the entire\ncurrent code block.  It means that the listed identifiers are to be\ninterpreted as globals.  It would be impossible to assign to a global\nvariable without "global", although free variables may refer to\nglobals without being declared global.\n\nNames listed in a "global" statement must not be used in the same code\nblock textually preceding that "global" statement.\n\nNames listed in a "global" statement must not be defined as formal\nparameters or in a "for" loop control target, "class" definition,\nfunction definition, or "import" statement.\n\n**CPython implementation detail:** The current implementation does not\nenforce the latter two restrictions, but programs should not abuse\nthis freedom, as future implementations may enforce them or silently\nchange the meaning of the program.\n\n**Programmer\'s note:** the "global" is a directive to the parser.  It\napplies only to code parsed at the same time as the "global"\nstatement. In particular, a "global" statement contained in a string\nor code object supplied to the built-in "exec()" function does not\naffect the code block *containing* the function call, and code\ncontained in such a string is unaffected by "global" statements in the\ncode containing the function call.  The same applies to the "eval()"\nand "compile()" functions.\n',
- 'id-classes': '\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 the\n   last evaluation; it is stored in the "builtins" module.  When not\n   in interactive mode, "_" has no special meaning and is not defined.\n   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).  Current\n   system names are discussed in the *Special method names* section\n   and elsewhere.  More will likely be defined in future versions of\n   Python.  *Any* use of "__*__" names, in any context, that does not\n   follow explicitly documented use, is subject to breakage without\n   warning.\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',
- '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 first\ncharacter, 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   ::= xid_start xid_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   xid_start    ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">\n   xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">\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\n* *Other_ID_Start* - explicit list of characters in PropList.txt to\n  support backwards compatibility\n\n* *Other_ID_Continue* - likewise\n\nAll identifiers are converted into the normal form NFKC while parsing;\ncomparison of identifiers is based on NFKC.\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 the\n   last evaluation; it is stored in the "builtins" module.  When not\n   in interactive mode, "_" has no special meaning and is not defined.\n   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).  Current\n   system names are discussed in the *Special method names* section\n   and elsewhere.  More will likely be defined in future versions of\n   Python.  *Any* use of "__*__" names, in any context, that does not\n   follow explicitly documented use, is subject to breakage without\n   warning.\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',
- '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 only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound 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", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. The are\n  identical to themselves, "x is x" but are not equal to themselves,\n  "x != x".  Additionally, comparing any value to a not-a-number value\n  will return "False".  For example, both "3 < float(\'NaN\')" and\n  "float(\'NaN\') < 3" will return "False".\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, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in 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\nComparison of objects of the differing types depends on whether either\nof the types provide explicit support for the comparison.  Most\nnumeric types can be compared with one another.  When cross-type\ncomparison is not supported, the comparison method returns\n"NotImplemented".\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" tests\nwhether a the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative 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 y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\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': '\nLambdas\n*******\n\n   lambda_expr        ::= "lambda" [parameter_list]: expression\n   lambda_expr_nocond ::= "lambda" [parameter_list]: expression_nocond\n\nLambda expressions (sometimes called lambda forms) have the same\nsyntactic position as expressions.  They are a shorthand to create\nanonymous functions; the expression "lambda arguments: expression"\nyields a function object.  The unnamed object behaves like a function\nobject defined with\n\n   def <lambda>(arguments):\n       return expression\n\nSee section *Function definitions* for the syntax of parameter lists.\nNote that functions created with lambda expressions cannot contain\nstatements or annotations.\n',
- 'lists': '\nList displays\n*************\n\nA list display is a possibly empty series of expressions enclosed in\nsquare brackets:\n\n   list_display ::= "[" [expression_list | comprehension] "]"\n\nA list display yields a new list object, the contents being specified\nby either a list of expressions or a comprehension.  When a comma-\nseparated list of expressions is supplied, its elements are evaluated\nfrom left to right and placed into the list object in that order.\nWhen a comprehension is supplied, the list is constructed from the\nelements resulting from the comprehension.\n',
- 'naming': '\nNaming and binding\n******************\n\n*Names* refer to objects.  Names are introduced by name binding\noperations. Each occurrence of a name in the program text refers to\nthe *binding* of that name established in the innermost function block\ncontaining the use.\n\nA *block* is a piece of Python program text that is executed as a\nunit. The following are blocks: a module, a function body, and a class\ndefinition. Each command typed interactively is a block.  A script\nfile (a file given as standard input to the interpreter or specified\non the interpreter command line the first argument) is a code block.\nA script command (a command specified on the interpreter command line\nwith the \'**-c**\' option) is a code block.  The string argument passed\nto the built-in functions "eval()" and "exec()" is a code block.\n\nA code block is executed in an *execution frame*.  A frame contains\nsome administrative information (used for debugging) and determines\nwhere and how execution continues after the code block\'s execution has\ncompleted.\n\nA *scope* defines the visibility of a name within a block.  If a local\nvariable is defined in a block, its scope includes that block.  If the\ndefinition occurs in a function block, the scope extends to any blocks\ncontained within the defining one, unless a contained block introduces\na different binding for the name.  The scope of names defined in a\nclass block is limited to the class block; it does not extend to the\ncode blocks of methods -- this includes comprehensions and generator\nexpressions since they are implemented using a function scope.  This\nmeans that the following will fail:\n\n   class A:\n       a = 42\n       b = list(a + i for i in range(10))\n\nWhen a name is used in a code block, it is resolved using the nearest\nenclosing scope.  The set of all such scopes visible to a code block\nis called the block\'s *environment*.\n\nIf a name is bound in a block, it is a local variable of that block,\nunless declared as "nonlocal".  If a name is bound at the module\nlevel, it is a global variable.  (The variables of the module code\nblock are local and global.)  If a variable is used in a code block\nbut not defined there, it is a *free variable*.\n\nWhen a name is not found at all, a "NameError" exception is raised.\nIf the name refers to a local variable that has not been bound, a\n"UnboundLocalError" exception is raised.  "UnboundLocalError" is a\nsubclass of "NameError".\n\nThe following constructs bind names: formal parameters to functions,\n"import" statements, class and function definitions (these bind the\nclass or function name in the defining block), and targets that are\nidentifiers if occurring in an assignment, "for" loop header, or after\n"as" in a "with" statement or "except" clause. The "import" statement\nof the form "from ... import *" binds all names defined in the\nimported module, except those beginning with an underscore.  This form\nmay only be used at the module level.\n\nA target occurring in a "del" statement is also considered bound for\nthis purpose (though the actual semantics are to unbind the name).\n\nEach assignment or import statement occurs within a block defined by a\nclass or function definition or at the module level (the top-level\ncode block).\n\nIf a name binding operation occurs anywhere within a code block, all\nuses of the name within the block are treated as references to the\ncurrent block.  This can lead to errors when a name is used within a\nblock before it is bound.  This rule is subtle.  Python lacks\ndeclarations and allows name binding operations to occur anywhere\nwithin a code block.  The local variables of a code block can be\ndetermined by scanning the entire text of the block for name binding\noperations.\n\nIf the "global" statement occurs within a block, all uses of the name\nspecified in the statement refer to the binding of that name in the\ntop-level namespace.  Names are resolved in the top-level namespace by\nsearching the global namespace, i.e. the namespace of the module\ncontaining the code block, and the builtins namespace, the namespace\nof the module "builtins".  The global namespace is searched first.  If\nthe name is not found there, the builtins namespace is searched.  The\nglobal statement must precede all uses of the name.\n\nThe builtins namespace associated with the execution of a code block\nis actually found by looking up the name "__builtins__" in its global\nnamespace; this should be a dictionary or a module (in the latter case\nthe module\'s dictionary is used).  By default, when in the "__main__"\nmodule, "__builtins__" is the built-in module "builtins"; when in any\nother module, "__builtins__" is an alias for the dictionary of the\n"builtins" module itself.  "__builtins__" can be set to a user-created\ndictionary to create a weak form of restricted execution.\n\n**CPython implementation detail:** Users should not touch\n"__builtins__"; it is strictly an implementation detail.  Users\nwanting to override values in the builtins namespace should "import"\nthe "builtins" module and modify its attributes appropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported.  The main module for a script is always called\n"__main__".\n\nThe "global" statement has the same scope as a name binding operation\nin the same block.  If the nearest enclosing scope for a free variable\ncontains a global statement, the free variable is treated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class.  Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n=================================\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name.  An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nThe "eval()" and "exec()" functions do not have access to the full\nenvironment for resolving names.  Names may be resolved in the local\nand global namespaces of the caller.  Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace.  [1]\nThe "exec()" and "eval()" functions have optional arguments to\noverride the global and local namespace.  If only one namespace is\nspecified, it is used for both.\n',
- 'nonlocal': '\nThe "nonlocal" statement\n************************\n\n   nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*\n\nThe "nonlocal" statement causes the listed identifiers to refer to\npreviously bound variables in the nearest enclosing scope.  This is\nimportant because the default behavior for binding is to search the\nlocal namespace first.  The statement allows encapsulated code to\nrebind variables outside of the local scope besides the global\n(module) scope.\n\nNames listed in a "nonlocal" statement, unlike to those listed in a\n"global" statement, must refer to pre-existing bindings in an\nenclosing scope (the scope in which a new binding should be created\ncannot be determined unambiguously).\n\nNames listed in a "nonlocal" statement must not collide with pre-\nexisting bindings in the local scope.\n\nSee also:\n\n   **PEP 3104** - Access to Names in Outer Scopes\n      The specification for the "nonlocal" statement.\n',
- 'numbers': '\nNumeric literals\n****************\n\nThere are three types of numeric literals: integers, floating point\nnumbers, and imaginary numbers.  There are no complex literals\n(complex numbers can be formed by adding a real number and an\nimaginary number).\n\nNote that numeric literals do not include a sign; a phrase like "-1"\nis actually an expression composed of the unary operator \'"-"\' and the\nliteral "1".\n',
- 'numeric-types': '\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 ("+", "-", "*", "/", "//", "%", "divmod()", "pow()",\n   "**", "<<", ">>", "&", "^", "|").  For instance, to evaluate the\n   expression "x + y", where *x* is an instance of a class that has an\n   "__add__()" method, "x.__add__(y)" is called.  The "__divmod__()"\n   method should be the equivalent to using "__floordiv__()" and\n   "__mod__()"; it should not be related to "__truediv__()".  Note\n   that "__pow__()" should be defined to accept an optional third\n   argument if the ternary version of the built-in "pow()" function is\n   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 ("+", "-", "*", "/", "//", "%", "divmod()", "pow()",\n   "**", "<<", ">>", "&", "^", "|") with reflected (swapped) operands.\n   These functions are only called if the left operand does not\n   support the corresponding operation and the operands are of\n   different types. [2]  For instance, to evaluate the expression "x -\n   y", where *y* is an instance of a class that has an "__rsub__()"\n   method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns\n   *NotImplemented*.\n\n   Note that ternary "pow()" will not try calling "__rpow__()" (the\n   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   assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",\n   ">>=", "&=", "^=", "|=").  These methods should attempt to do the\n   operation in-place (modifying *self*) and return the result (which\n   could be, but does not have to be, *self*).  If a specific method\n   is not defined, the augmented assignment falls back to the normal\n   methods.  For instance, if *x* is an instance of a class with an\n   "__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)"\n   . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as\n   with the evaluation of "x + y". In certain situations, augmented\n   assignment can result in unexpected errors (see *Why does\n   a_tuple[i] += [\'item\'] raise an exception when the addition\n   works?*), but this behavior is in fact part of the data model.\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()", "int()",\n   "float()" and "round()".  Should return a value of the appropriate\n   type.\n\nobject.__index__(self)\n\n   Called to implement "operator.index()", and whenever Python needs\n   to losslessly convert the numeric object to an integer object (such\n   as in slicing, or in the built-in "bin()", "hex()" and "oct()"\n   functions). Presence of this method indicates that the numeric\n   object is an integer type.  Must return an integer.\n\n   Note: When "__index__()" is defined, "__int__()" should also be\n     defined, and both shuld return the same value, in order to have a\n     coherent integer type class.\n',
- 'objects': '\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data.  All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value.  An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory.  The \'"is"\' operator compares the\nidentity of two objects; the "id()" function returns an integer\nrepresenting its identity.\n\n**CPython implementation detail:** For CPython, "id(x)" is the memory\naddress where "x" is stored.\n\nAn object\'s type determines the operations that the object supports\n(e.g., "does it have a length?") and also defines the possible values\nfor objects of that type.  The "type()" function returns an object\'s\ntype (which is an object itself).  Like its identity, an object\'s\n*type* is also unchangeable. [1]\n\nThe *value* of some objects can change.  Objects whose value can\nchange are said to be *mutable*; objects whose value is unchangeable\nonce they are created are called *immutable*. (The value of an\nimmutable container object that contains a reference to a mutable\nobject can change when the latter\'s value is changed; however the\ncontainer is still considered immutable, because the collection of\nobjects it contains cannot be changed.  So, immutability is not\nstrictly the same as having an unchangeable value, it is more subtle.)\nAn object\'s mutability is determined by its type; for instance,\nnumbers, strings and tuples are immutable, while dictionaries and\nlists are mutable.\n\nObjects are never explicitly destroyed; however, when they become\nunreachable they may be garbage-collected.  An implementation is\nallowed to postpone garbage collection or omit it altogether --- it is\na matter of implementation quality how garbage collection is\nimplemented, as long as no objects are collected that are still\nreachable.\n\n**CPython implementation detail:** CPython currently uses a reference-\ncounting scheme with (optional) delayed detection of cyclically linked\ngarbage, which collects most objects as soon as they become\nunreachable, but is not guaranteed to collect garbage containing\ncircular references.  See the documentation of the "gc" module for\ninformation on controlling the collection of cyclic garbage. Other\nimplementations act differently and CPython may change. Do not depend\non immediate finalization of objects when they become unreachable (ex:\nalways close files).\n\nNote that the use of the implementation\'s tracing or debugging\nfacilities may keep objects alive that would normally be collectable.\nAlso note that catching an exception with a \'"try"..."except"\'\nstatement may keep objects alive.\n\nSome objects contain references to "external" resources such as open\nfiles or windows.  It is understood that these resources are freed\nwhen the object is garbage-collected, but since garbage collection is\nnot guaranteed to happen, such objects also provide an explicit way to\nrelease the external resource, usually a "close()" method. Programs\nare strongly recommended to explicitly close such objects.  The\n\'"try"..."finally"\' statement and the \'"with"\' statement provide\nconvenient ways to do this.\n\nSome objects contain references to other objects; these are called\n*containers*. Examples of containers are tuples, lists and\ndictionaries.  The references are part of a container\'s value.  In\nmost cases, when we talk about the value of a container, we imply the\nvalues, not the identities of the contained objects; however, when we\ntalk about the mutability of a container, only the identities of the\nimmediately contained objects are implied.  So, if an immutable\ncontainer (like a tuple) contains a reference to a mutable object, its\nvalue changes if that mutable object is changed.\n\nTypes affect almost all aspects of object behavior.  Even the\nimportance of object identity is affected in some sense: for immutable\ntypes, operations that compute new values may actually return a\nreference to any existing object with the same type and value, while\nfor mutable objects this is not allowed.  E.g., after "a = 1; b = 1",\n"a" and "b" may or may not refer to the same object with the value\none, depending on the implementation, but after "c = []; d = []", "c"\nand "d" are guaranteed to refer to two different, unique, newly\ncreated empty lists. (Note that "c = d = []" assigns the same object\nto both "c" and "d".)\n',
- 'operator-summary': '\nOperator precedence\n*******************\n\nThe following table summarizes the operator precedences in Python,\nfrom lowest precedence (least binding) to highest precedence (most\nbinding).  Operators in the same box have the same precedence.  Unless\nthe syntax is explicitly given, operators are binary.  Operators in\nthe same box group left to right (except for comparisons, including\ntests, which all have the same precedence and chain from left to right\n--- see section *Comparisons* --- and exponentiation, which groups\nfrom right to left).\n\n+-------------------------------------------------+---------------------------------------+\n| Operator                                        | Description                           |\n+=================================================+=======================================+\n| "lambda"                                        | Lambda expression                     |\n+-------------------------------------------------+---------------------------------------+\n| "if" -- "else"                                  | Conditional expression                |\n+-------------------------------------------------+---------------------------------------+\n| "or"                                            | Boolean OR                            |\n+-------------------------------------------------+---------------------------------------+\n| "and"                                           | Boolean AND                           |\n+-------------------------------------------------+---------------------------------------+\n| "not" "x"                                       | Boolean NOT                           |\n+-------------------------------------------------+---------------------------------------+\n| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership     |\n| ">=", "!=", "=="                                | tests and identity tests              |\n+-------------------------------------------------+---------------------------------------+\n| "|"                                             | Bitwise OR                            |\n+-------------------------------------------------+---------------------------------------+\n| "^"                                             | Bitwise XOR                           |\n+-------------------------------------------------+---------------------------------------+\n| "&"                                             | Bitwise AND                           |\n+-------------------------------------------------+---------------------------------------+\n| "<<", ">>"                                      | Shifts                                |\n+-------------------------------------------------+---------------------------------------+\n| "+", "-"                                        | Addition and subtraction              |\n+-------------------------------------------------+---------------------------------------+\n| "*", "/", "//", "%"                             | Multiplication, division, remainder   |\n+-------------------------------------------------+---------------------------------------+\n| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |\n+-------------------------------------------------+---------------------------------------+\n| "**"                                            | Exponentiation [6]                    |\n+-------------------------------------------------+---------------------------------------+\n| "x[index]", "x[index:index]",                   | Subscription, slicing, call,          |\n| "x(arguments...)", "x.attribute"                | attribute reference                   |\n+-------------------------------------------------+---------------------------------------+\n| "(expressions...)", "[expressions...]", "{key:  | Binding or tuple display, list        |\n| value...}", "{expressions...}"                  | display, dictionary display, set      |\n+-------------------------------------------------+---------------------------------------+\n\n-[ Footnotes ]-\n\n[1] While "abs(x%y) < abs(y)" is true mathematically, for floats it\n    may not be true numerically due to roundoff.  For example, and\n    assuming a platform on which a Python float is an IEEE 754 double-\n    precision number, in order that "-1e-100 % 1e100" have the same\n    sign as "1e100", the computed result is "-1e-100 + 1e100", which\n    is numerically exactly equal to "1e100".  The function\n    "math.fmod()" returns a result whose sign matches the sign of the\n    first argument instead, and so returns "-1e-100" in this case.\n    Which approach is more appropriate depends on the application.\n\n[2] If x is very close to an exact integer multiple of y, it\'s\n    possible for "x//y" to be one larger than "(x-x%y)//y" due to\n    rounding.  In such cases, Python returns the latter result, in\n    order to preserve that "divmod(x,y)[0] * y + x % y" be very close\n    to "x".\n\n[3] While comparisons between strings make sense at the byte level,\n    they may be counter-intuitive to users.  For example, the strings\n    ""\\u00C7"" and ""\\u0327\\u0043"" compare differently, even though\n    they both represent the same unicode character (LATIN CAPITAL\n    LETTER C WITH CEDILLA).  To compare strings in a human\n    recognizable way, compare using "unicodedata.normalize()".\n\n[4] Due to automatic garbage-collection, free lists, and the dynamic\n    nature of descriptors, you may notice seemingly unusual behaviour\n    in certain uses of the "is" operator, like those involving\n    comparisons between instance methods, or constants.  Check their\n    documentation for more info.\n\n[5] The "%" operator is also used for string formatting; the same\n    precedence applies.\n\n[6] The power operator "**" binds less tightly than an arithmetic or\n    bitwise unary operator on its right, that is, "2**-1" is "0.5".\n',
- 'pass': '\nThe "pass" statement\n********************\n\n   pass_stmt ::= "pass"\n\n"pass" is a null operation --- when it is executed, nothing happens.\nIt is useful as a placeholder when a statement is required\nsyntactically, but no code needs to be executed, for example:\n\n   def f(arg): pass    # a function that does nothing (yet)\n\n   class C: pass       # a class with no methods (yet)\n',
- 'power': '\nThe power operator\n******************\n\nThe power operator binds more tightly than unary operators on its\nleft; it binds less tightly than unary operators on its right.  The\nsyntax is:\n\n   power ::= primary ["**" u_expr]\n\nThus, in an unparenthesized sequence of power and unary operators, the\noperators are evaluated from right to left (this does not constrain\nthe evaluation order for the operands): "-1**2" results in "-1".\n\nThe power operator has the same semantics as the built-in "pow()"\nfunction, when called with two arguments: it yields its left argument\nraised to the power of its right argument.  The numeric arguments are\nfirst converted to a common type, and the result is of that type.\n\nFor int operands, the result has the same type as the operands unless\nthe second argument is negative; in that case, all arguments are\nconverted to float and a float result is delivered. For example,\n"10**2" returns "100", but "10**-2" returns "0.01".\n\nRaising "0.0" to a negative power results in a "ZeroDivisionError".\nRaising a negative number to a fractional power results in a "complex"\nnumber. (In earlier versions it raised a "ValueError".)\n',
- 'raise': '\nThe "raise" statement\n*********************\n\n   raise_stmt ::= "raise" [expression ["from" expression]]\n\nIf no expressions are present, "raise" re-raises the last exception\nthat was active in the current scope.  If no exception is active in\nthe current scope, a "RuntimeError" exception is raised indicating\nthat this is an error.\n\nOtherwise, "raise" evaluates the first expression as the exception\nobject.  It must be either a subclass or an instance of\n"BaseException". If it is a class, the exception instance will be\nobtained when needed by instantiating the class with no arguments.\n\nThe *type* of the exception is the exception instance\'s class, the\n*value* is the instance itself.\n\nA traceback object is normally created automatically when an exception\nis raised and attached to it as the "__traceback__" attribute, which\nis writable. You can create an exception and set your own traceback in\none step using the "with_traceback()" exception method (which returns\nthe same exception instance, with its traceback set to its argument),\nlike so:\n\n   raise Exception("foo occurred").with_traceback(tracebackobj)\n\nThe "from" clause is used for exception chaining: if given, the second\n*expression* must be another exception class or instance, which will\nthen be attached to the raised exception as the "__cause__" attribute\n(which is writable).  If the raised exception is not handled, both\nexceptions will be printed:\n\n   >>> try:\n   ...     print(1 / 0)\n   ... except Exception as exc:\n   ...     raise RuntimeError("Something bad happened") from exc\n   ...\n   Traceback (most recent call last):\n     File "<stdin>", line 2, in <module>\n   ZeroDivisionError: int division or modulo by zero\n\n   The above exception was the direct cause of the following exception:\n\n   Traceback (most recent call last):\n     File "<stdin>", line 4, in <module>\n   RuntimeError: Something bad happened\n\nA similar mechanism works implicitly if an exception is raised inside\nan exception handler: the previous exception is then attached as the\nnew exception\'s "__context__" attribute:\n\n   >>> try:\n   ...     print(1 / 0)\n   ... except:\n   ...     raise RuntimeError("Something bad happened")\n   ...\n   Traceback (most recent call last):\n     File "<stdin>", line 2, in <module>\n   ZeroDivisionError: int division or modulo by zero\n\n   During handling of the above exception, another exception occurred:\n\n   Traceback (most recent call last):\n     File "<stdin>", line 4, in <module>\n   RuntimeError: Something bad happened\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information about handling exceptions is in section\n*The try statement*.\n',
- 'return': '\nThe "return" statement\n**********************\n\n   return_stmt ::= "return" [expression_list]\n\n"return" may only occur syntactically nested in a function definition,\nnot within a nested class definition.\n\nIf an expression list is present, it is evaluated, else "None" is\nsubstituted.\n\n"return" leaves the current function call with the expression list (or\n"None") as return value.\n\nWhen "return" passes control out of a "try" statement with a "finally"\nclause, that "finally" clause is executed before really leaving the\nfunction.\n\nIn a generator function, the "return" statement indicates that the\ngenerator is done and will cause "StopIteration" to be raised. The\nreturned value (if any) is used as an argument to construct\n"StopIteration" and becomes the "StopIteration.value" attribute.\n',
- 'sequence-types': '\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" where\n*N* is the length of the sequence, or slice objects, which define a\nrange of items.  It is also recommended that mappings provide the\nmethods "keys()", "values()", "items()", "get()", "clear()",\n"setdefault()", "pop()", "popitem()", "copy()", and "update()"\nbehaving similar to those for Python\'s standard dictionary objects.\nThe "collections" module provides a "MutableMapping" abstract base\nclass to help create those methods from a base set of "__getitem__()",\n"__setitem__()", "__delitem__()", and "keys()". Mutable sequences\nshould provide methods "append()", "count()", "index()", "extend()",\n"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python\nstandard list objects.  Finally, sequence types should implement\naddition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods "__add__()", "__radd__()",\n"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described\nbelow; they should not define other numerical operators.  It is\nrecommended that both mappings and sequences implement the\n"__contains__()" method to allow efficient use of the "in" operator;\nfor mappings, "in" should search the mapping\'s keys; for sequences, it\nshould search through the values.  It is further recommended that both\nmappings and sequences implement the "__iter__()" method to allow\nefficient iteration through the container; for mappings, "__iter__()"\nshould be the same as "keys()"; for sequences, it should iterate\nthrough 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 that\n   doesn\'t define a "__bool__()" method and whose "__len__()" method\n   returns zero is considered to be false in a Boolean context.\n\nobject.__length_hint__(self)\n\n   Called to implement "operator.length_hint()". Should return an\n   estimated length for the object (which may be greater or less than\n   the actual length). The length must be an integer ">=" 0. This\n   method is purely an optimization and is never required for\n   correctness.\n\n   New in version 3.4.\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 "None".\n\nobject.__getitem__(self, key)\n\n   Called to implement evaluation of "self[key]". For sequence types,\n   the accepted keys should be integers and slice objects.  Note that\n   the special interpretation of negative indexes (if the class wishes\n   to emulate a sequence type) is up to the "__getitem__()" method. If\n   *key* is of an inappropriate type, "TypeError" may be raised; if of\n   a value outside the set of indexes for the sequence (after any\n   special interpretation of negative values), "IndexError" should be\n   raised. For mapping types, if *key* is missing (not in the\n   container), "KeyError" 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__()" 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()" built-in 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 "reversed()"\n   built-in will fall back to using the sequence protocol ("__len__()"\n   and "__getitem__()").  Objects that support the sequence protocol\n   should only provide "__reversed__()" if they can provide an\n   implementation that is more efficient than the one provided by\n   "reversed()".\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   For objects that don\'t define "__contains__()", the membership test\n   first tries iteration via "__iter__()", then the old sequence\n   iteration protocol via "__getitem__()", see *this section in the\n   language reference*.\n',
- '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 floor division by "pow(2,n)".\nA left shift by *n* bits is defined as multiplication with "pow(2,n)".\n\nNote: In the current implementation, the right-hand operand is required to\n  be at most "sys.maxsize".  If the right-hand operand is larger than\n  "sys.maxsize" an "OverflowError" exception is raised.\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 slicing:\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 "__getitem__()"\nmethod as normal subscription) with a key that is constructed from the\nslice list, as follows.  If the slice list contains at least one\ncomma, the key is a tuple containing the conversion of the slice\nitems; otherwise, the conversion of the lone slice item is the key.\nThe conversion of a slice item that is an expression is that\nexpression.  The conversion of a proper slice is a slice object (see\nsection *The standard type hierarchy*) whose "start", "stop" and\n"step" attributes are the values of the expressions given as lower\nbound, upper bound and stride, respectively, substituting "None" for\nmissing 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.\n\nclass.__name__\n\n   The name of the class or type.\n\nclass.__qualname__\n\n   The *qualified name* of the class or type.\n\n   New in version 3.3.\n\nclass.__mro__\n\n   This attribute is a tuple of classes that are considered when\n   looking for base classes during method resolution.\n\nclass.mro()\n\n   This method can be overridden by a metaclass to customize the\n   method resolution order for its instances.  It is called at class\n   instantiation, and its result is stored in "__mro__".\n\nclass.__subclasses__()\n\n   Each class keeps a list of weak references to its immediate\n   subclasses.  This method returns a list of all those references\n   still alive. Example:\n\n      >>> int.__subclasses__()\n      [<class \'bool\'>]\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 "[1.0,\n    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] Cased characters are those with general category property being\n    one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase), or "Lt"\n    (Letter, titlecase).\n\n[5] To format only a tuple you should therefore provide a singleton\n    tuple whose only element is the tuple to be formatted.\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" | "u" | "R" | "U"\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" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"\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 "bytesprefix"\nand the rest of the literal. The source character set is defined by\nthe encoding declaration; it is UTF-8 if no encoding declaration is\ngiven in the source file; see section *Encoding declarations*.\n\nIn plain English: Both types of literals can be enclosed in matching\nsingle quotes ("\'") or double quotes (""").  They can also be enclosed\nin matching groups of three single or double quotes (these are\ngenerally 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\nBytes literals are always prefixed with "\'b\'" or "\'B\'"; they produce\nan instance of the "bytes" type instead of the "str" type.  They may\nonly contain ASCII characters; bytes with a numeric value of 128 or\ngreater must be expressed with escapes.\n\nAs of Python 3.3 it is possible again to prefix unicode strings with a\n"u" prefix to simplify maintenance of dual 2.x and 3.x codebases.\n\nBoth string and bytes literals may optionally be prefixed with a\nletter "\'r\'" or "\'R\'"; such strings are called *raw strings* and treat\nbackslashes as literal characters.  As a result, in string literals,\n"\'\\U\'" and "\'\\u\'" escapes in raw strings are not treated specially.\nGiven that Python 2.x\'s raw unicode literals behave differently than\nPython 3.x\'s the "\'ur\'" syntax is not supported.\n\n   New in version 3.3: The "\'rb\'" prefix of raw bytes literals has\n   been added as a synonym of "\'br\'".\n\n   New in version 3.3: Support for the unicode legacy literal\n   ("u\'value\'") was reintroduced to simplify the maintenance of dual\n   Python 2.x and 3.x codebases. See **PEP 414** for more information.\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+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\n+-------------------+-----------------------------------+---------+\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     | (4)     |\n+-------------------+-----------------------------------+---------+\n| "\\uxxxx"          | Character with 16-bit hex value   | (5)     |\n+-------------------+-----------------------------------+---------+\n| "\\Uxxxxxxxx"      | Character with 32-bit hex value   | (6)     |\n+-------------------+-----------------------------------+---------+\n\nNotes:\n\n1. As in Standard C, up to three octal digits are accepted.\n\n2. Unlike in Standard C, exactly two hex digits are required.\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. Changed in version 3.3: Support for name aliases [1] has been\n   added.\n\n5. Individual code units which form parts of a surrogate pair can be\n   encoded using this escape sequence.  Exactly four hex digits are\n   required.\n\n6. Any Unicode character can be encoded this way.  Exactly eight hex\n   digits are required.\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 string\ncannot end in an odd number of backslashes).  Specifically, *a raw\nstring cannot end in a single backslash* (since the backslash would\nescape 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 or a slice (as discussed in the following section).\n\nThe formal syntax makes no special provision for negative indices in\nsequences; however, built-in sequences all provide a "__getitem__()"\nmethod that interprets negative indices by adding the length of the\nsequence to the index (so that "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). Since the support for\nnegative indices and slicing occurs in the object\'s "__getitem__()"\nmethod, subclasses overriding this method will need to explicitly add\nthat support.\n\nA string\'s items are characters.  A character is not a separate data\ntype but a string of exactly one character.\n',
- 'truth': '\nTruth Value Testing\n*******************\n\nAny object can be tested for truth value, for use in an "if" or\n"while" condition or as operand of the Boolean operations below. The\nfollowing values are considered false:\n\n* "None"\n\n* "False"\n\n* zero of any numeric type, for example, "0", "0.0", "0j".\n\n* any empty sequence, for example, "\'\'", "()", "[]".\n\n* any empty mapping, for example, "{}".\n\n* instances of user-defined classes, if the class defines a\n  "__bool__()" or "__len__()" method, when that method returns the\n  integer zero or "bool" value "False". [1]\n\nAll other values are considered true --- so objects of many types are\nalways true.\n\nOperations and built-in functions that have a Boolean result always\nreturn "0" or "False" for false and "1" or "True" for true, unless\notherwise stated. (Important exception: the Boolean operations "or"\nand "and" always return one of their operands.)\n',
- 'try': '\nThe "try" statement\n*******************\n\nThe "try" statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" target]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe "except" clause(s) specify one or more exception handlers. When no\nexception occurs in the "try" clause, no exception handler is\nexecuted. When an exception occurs in the "try" suite, a search for an\nexception handler is started.  This search inspects the except clauses\nin turn until one is found that matches the exception.  An expression-\nless except clause, if present, must be last; it matches any\nexception.  For an except clause with an expression, that expression\nis evaluated, and the clause matches the exception if the resulting\nobject is "compatible" with the exception.  An object is compatible\nwith an exception if it is the class or a base class of the exception\nobject or a tuple containing an item compatible with the exception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire "try" statement raised\nthe exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the "as" keyword in that except clause, if\npresent, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using "as target", it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the "sys" module and can be access via\n"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the\nexception class, the exception instance and a traceback object (see\nsection *The standard type hierarchy*) identifying the point in the\nprogram where the exception occurred.  "sys.exc_info()" values are\nrestored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional "else" clause is executed if and when control flows off\nthe end of the "try" clause. [2] Exceptions in the "else" clause are\nnot handled by the preceding "except" clauses.\n\nIf "finally" is present, it specifies a \'cleanup\' handler.  The "try"\nclause is executed, including any "except" and "else" clauses.  If an\nexception occurs in any of the clauses and is not handled, the\nexception is temporarily saved. The "finally" clause is executed.  If\nthere is a saved exception it is re-raised at the end of the "finally"\nclause.  If the "finally" clause raises another exception, the saved\nexception is set as the context of the new exception. If the "finally"\nclause executes a "return" or "break" statement, the saved exception\nis discarded:\n\n   >>> def f():\n   ...     try:\n   ...         1/0\n   ...     finally:\n   ...         return 42\n   ...\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the "finally" clause.\n\nWhen a "return", "break" or "continue" statement is executed in the\n"try" suite of a "try"..."finally" statement, the "finally" clause is\nalso executed \'on the way out.\' A "continue" statement is illegal in\nthe "finally" clause. (The reason is a problem with the current\nimplementation --- this restriction may be lifted in the future).\n\nThe return value of a function is determined by the last "return"\nstatement executed.  Since the "finally" clause always executes, a\n"return" statement executed in the "finally" clause will always be the\nlast one executed:\n\n   >>> def foo():\n   ...     try:\n   ...         return \'try\'\n   ...     finally:\n   ...         return \'finally\'\n   ...\n   >>> foo()\n   \'finally\'\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the "raise" statement to\ngenerate exceptions may be found in section *The raise statement*.\n',
- 'typesfunctions': '\nFunctions\n*********\n\nFunction objects are created by function definitions.  The only\noperation on a function object is to call it: "func(argument-list)".\n\nThere are really two flavors of function objects: built-in functions\nand user-defined functions.  Both support the same operation (to call\nthe function), but the implementation is different, hence the\ndifferent object types.\n\nSee *Function definitions* for more information.\n',
- 'typesmapping': '\nMapping Types --- "dict"\n************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects.  There is currently only one standard\nmapping type, the *dictionary*.  (For other containers see the built-\nin "list", "set", and "tuple" classes, and the "collections" module.)\n\nA dictionary\'s keys are *almost* arbitrary values.  Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys.  Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as "1" and "1.0") then they can be used interchangeably to index\nthe same dictionary entry.  (Note however, that since computers store\nfloating-point numbers as approximations it is usually unwise to use\nthem as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of "key:\nvalue" pairs within braces, for example: "{\'jack\': 4098, \'sjoerd\':\n4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the "dict"\nconstructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n   Return a new dictionary initialized from an optional positional\n   argument and a possibly empty set of keyword arguments.\n\n   If no positional argument is given, an empty dictionary is created.\n   If a positional argument is given and it is a mapping object, a\n   dictionary is created with the same key-value pairs as the mapping\n   object.  Otherwise, the positional argument must be an *iterator*\n   object.  Each item in the iterable must itself be an iterator with\n   exactly two objects.  The first object of each item becomes a key\n   in the new dictionary, and the second object the corresponding\n   value.  If a key occurs more than once, the last value for that key\n   becomes the corresponding value in the new dictionary.\n\n   If keyword arguments are given, the keyword arguments and their\n   values are added to the dictionary created from the positional\n   argument.  If a key being added is already present, the value from\n   the keyword argument replaces the value from the positional\n   argument.\n\n   To illustrate, the following examples all return a dictionary equal\n   to "{"one": 1, "two": 2, "three": 3}":\n\n      >>> a = dict(one=1, two=2, three=3)\n      >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n      >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n      >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n      >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n      >>> a == b == c == d == e\n      True\n\n   Providing keyword arguments as in the first example only works for\n   keys that are valid Python identifiers.  Otherwise, any valid keys\n   can be used.\n\n   These are the operations that dictionaries support (and therefore,\n   custom mapping types should support too):\n\n   len(d)\n\n      Return the number of items in the dictionary *d*.\n\n   d[key]\n\n      Return the item of *d* with key *key*.  Raises a "KeyError" if\n      *key* is not in the map.\n\n      If a subclass of dict defines a method "__missing__()", if the\n      key *key* is not present, the "d[key]" operation calls that\n      method with the key *key* as argument.  The "d[key]" operation\n      then returns or raises whatever is returned or raised by the\n      "__missing__(key)" call if the key is not present. No other\n      operations or methods invoke "__missing__()". If "__missing__()"\n      is not defined, "KeyError" is raised. "__missing__()" must be a\n      method; it cannot be an instance variable:\n\n         >>> class Counter(dict):\n         ...     def __missing__(self, key):\n         ...         return 0\n         >>> c = Counter()\n         >>> c[\'red\']\n         0\n         >>> c[\'red\'] += 1\n         >>> c[\'red\']\n         1\n\n      See "collections.Counter" for a complete implementation\n      including other methods helpful for accumulating and managing\n      tallies.\n\n   d[key] = value\n\n      Set "d[key]" to *value*.\n\n   del d[key]\n\n      Remove "d[key]" from *d*.  Raises a "KeyError" if *key* is not\n      in the map.\n\n   key in d\n\n      Return "True" if *d* has a key *key*, else "False".\n\n   key not in d\n\n      Equivalent to "not key in d".\n\n   iter(d)\n\n      Return an iterator over the keys of the dictionary.  This is a\n      shortcut for "iter(d.keys())".\n\n   clear()\n\n      Remove all items from the dictionary.\n\n   copy()\n\n      Return a shallow copy of the dictionary.\n\n   classmethod fromkeys(seq[, value])\n\n      Create a new dictionary with keys from *seq* and values set to\n      *value*.\n\n      "fromkeys()" is a class method that returns a new dictionary.\n      *value* defaults to "None".\n\n   get(key[, default])\n\n      Return the value for *key* if *key* is in the dictionary, else\n      *default*. If *default* is not given, it defaults to "None", so\n      that this method never raises a "KeyError".\n\n   items()\n\n      Return a new view of the dictionary\'s items ("(key, value)"\n      pairs). See the *documentation of view objects*.\n\n   keys()\n\n      Return a new view of the dictionary\'s keys.  See the\n      *documentation of view objects*.\n\n   pop(key[, default])\n\n      If *key* is in the dictionary, remove it and return its value,\n      else return *default*.  If *default* is not given and *key* is\n      not in the dictionary, a "KeyError" is raised.\n\n   popitem()\n\n      Remove and return an arbitrary "(key, value)" pair from the\n      dictionary.\n\n      "popitem()" is useful to destructively iterate over a\n      dictionary, as often used in set algorithms.  If the dictionary\n      is empty, calling "popitem()" raises a "KeyError".\n\n   setdefault(key[, default])\n\n      If *key* is in the dictionary, return its value.  If not, insert\n      *key* with a value of *default* and return *default*.  *default*\n      defaults to "None".\n\n   update([other])\n\n      Update the dictionary with the key/value pairs from *other*,\n      overwriting existing keys.  Return "None".\n\n      "update()" accepts either another dictionary object or an\n      iterable of key/value pairs (as tuples or other iterables of\n      length two).  If keyword arguments are specified, the dictionary\n      is then updated with those key/value pairs: "d.update(red=1,\n      blue=2)".\n\n   values()\n\n      Return a new view of the dictionary\'s values.  See the\n      *documentation of view objects*.\n\nSee also:\n\n   "types.MappingProxyType" can be used to create a read-only view of\n   a "dict".\n\n\nDictionary view objects\n=======================\n\nThe objects returned by "dict.keys()", "dict.values()" and\n"dict.items()" are *view objects*.  They provide a dynamic view on the\ndictionary\'s entries, which means that when the dictionary changes,\nthe view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n   Return the number of entries in the dictionary.\n\niter(dictview)\n\n   Return an iterator over the keys, values or items (represented as\n   tuples of "(key, value)") in the dictionary.\n\n   Keys and values are iterated over in an arbitrary order which is\n   non-random, varies across Python implementations, and depends on\n   the dictionary\'s history of insertions and deletions. If keys,\n   values and items views are iterated over with no intervening\n   modifications to the dictionary, the order of items will directly\n   correspond.  This allows the creation of "(value, key)" pairs using\n   "zip()": "pairs = zip(d.values(), d.keys())".  Another way to\n   create the same list is "pairs = [(v, k) for (k, v) in d.items()]".\n\n   Iterating views while adding or deleting entries in the dictionary\n   may raise a "RuntimeError" or fail to iterate over all entries.\n\nx in dictview\n\n   Return "True" if *x* is in the underlying dictionary\'s keys, values\n   or items (in the latter case, *x* should be a "(key, value)"\n   tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that "(key, value)" pairs are unique\nand hashable, then the items view is also set-like.  (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class "collections.abc.Set" are available (for example, "==",\n"<", or "^").\n\nAn example of dictionary view usage:\n\n   >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n   >>> keys = dishes.keys()\n   >>> values = dishes.values()\n\n   >>> # iteration\n   >>> n = 0\n   >>> for val in values:\n   ...     n += val\n   >>> print(n)\n   504\n\n   >>> # keys and values are iterated over in the same order\n   >>> list(keys)\n   [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n   >>> list(values)\n   [2, 1, 1, 500]\n\n   >>> # view objects are dynamic and reflect dict changes\n   >>> del dishes[\'eggs\']\n   >>> del dishes[\'sausage\']\n   >>> list(keys)\n   [\'spam\', \'bacon\']\n\n   >>> # set operations\n   >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n   {\'bacon\'}\n   >>> keys ^ {\'sausage\', \'juice\'}\n   {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n',
- 'typesmethods': '\nMethods\n*******\n\nMethods are functions that are called using the attribute notation.\nThere are two flavors: built-in methods (such as "append()" on lists)\nand class instance methods.  Built-in methods are described with the\ntypes that support them.\n\nIf you access a method (a function defined in a class namespace)\nthrough an instance, you get a special object: a *bound method* (also\ncalled *instance method*) object. When called, it will add the "self"\nargument to the argument list.  Bound methods have two special read-\nonly attributes: "m.__self__" is the object on which the method\noperates, and "m.__func__" is the function implementing the method.\nCalling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to\ncalling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".\n\nLike function objects, bound method objects support getting arbitrary\nattributes.  However, since method attributes are actually stored on\nthe underlying function object ("meth.__func__"), setting method\nattributes on bound methods is disallowed.  Attempting to set an\nattribute on a method results in an "AttributeError" being raised.  In\norder to set a method attribute, you need to explicitly set it on the\nunderlying function object:\n\n   >>> class C:\n   ...     def method(self):\n   ...         pass\n   ...\n   >>> c = C()\n   >>> c.method.whoami = \'my name is method\'  # can\'t set on the method\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   AttributeError: \'method\' object has no attribute \'whoami\'\n   >>> c.method.__func__.whoami = \'my name is method\'\n   >>> c.method.whoami\n   \'my name is method\'\n\nSee *The standard type hierarchy* for more information.\n',
- 'typesmodules': '\nModules\n*******\n\nThe only special operation on a module is attribute access: "m.name",\nwhere *m* is a module and *name* accesses a name defined in *m*\'s\nsymbol table. Module attributes can be assigned to.  (Note that the\n"import" statement is not, strictly speaking, an operation on a module\nobject; "import foo" does not require a module object named *foo* to\nexist, rather it requires an (external) *definition* for a module\nnamed *foo* somewhere.)\n\nA special attribute of every module is "__dict__". This is the\ndictionary containing the module\'s symbol table. Modifying this\ndictionary will actually change the module\'s symbol table, but direct\nassignment to the "__dict__" attribute is not possible (you can write\n"m.__dict__[\'a\'] = 1", which defines "m.a" to be "1", but you can\'t\nwrite "m.__dict__ = {}").  Modifying "__dict__" directly is not\nrecommended.\n\nModules built into the interpreter are written like this: "<module\n\'sys\' (built-in)>".  If loaded from a file, they are written as\n"<module \'os\' from \'/usr/local/lib/pythonX.Y/os.pyc\'>".\n',
- 'typesseq-mutable': '\nMutable Sequence Types\n**********************\n\nThe operations in the following table are defined on mutable sequence\ntypes. The "collections.abc.MutableSequence" ABC is provided to make\nit easier to correctly implement these operations on custom sequence\ntypes.\n\nIn the table *s* is an instance of a mutable sequence type, *t* is any\niterable object and *x* is an arbitrary object that meets any type and\nvalue restrictions imposed by *s* (for example, "bytearray" only\naccepts integers that meet the value restriction "0 <= x <= 255").\n\n+--------------------------------+----------------------------------+-----------------------+\n| Operation                      | Result                           | Notes                 |\n+================================+==================================+=======================+\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n| "s[i:j:k] = t"                 | the elements of "s[i:j:k]" are   | (1)                   |\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n| "s.clear()"                    | removes all items from "s" (same | (5)                   |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.copy()"                     | creates a shallow copy of "s"    | (5)                   |\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n+--------------------------------+----------------------------------+-----------------------+\n| "s.pop([i])"                   | retrieves the item at *i* and    | (2)                   |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.remove(x)"                  | remove the first item from *s*   | (3)                   |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.reverse()"                  | reverses the items of *s* in     | (4)                   |\n+--------------------------------+----------------------------------+-----------------------+\n\nNotes:\n\n1. *t* must have the same length as the slice it is replacing.\n\n2. The optional argument *i* defaults to "-1", so that by default the\n   last item is removed and returned.\n\n3. "remove" raises "ValueError" when *x* is not found in *s*.\n\n4. The "reverse()" method modifies the sequence in place for economy\n   of space when reversing a large sequence.  To remind users that it\n   operates by side effect, it does not return the reversed sequence.\n\n5. "clear()" and "copy()" are included for consistency with the\n   interfaces of mutable containers that don\'t support slicing\n   operations (such as "dict" and "set")\n\n   New in version 3.3: "clear()" and "copy()" methods.\n',
- 'unary': '\nUnary arithmetic and bitwise operations\n***************************************\n\nAll unary arithmetic and bitwise operations have the same priority:\n\n   u_expr ::= power | "-" u_expr | "+" u_expr | "~" u_expr\n\nThe unary "-" (minus) operator yields the negation of its numeric\nargument.\n\nThe unary "+" (plus) operator yields its numeric argument unchanged.\n\nThe unary "~" (invert) operator yields the bitwise inversion of its\ninteger argument.  The bitwise inversion of "x" is defined as\n"-(x+1)".  It only applies to integral numbers.\n\nIn all three cases, if the argument does not have the proper type, a\n"TypeError" exception is raised.\n',
- 'while': '\nThe "while" statement\n*********************\n\nThe "while" statement is used for repeated execution as long as an\nexpression is true:\n\n   while_stmt ::= "while" expression ":" suite\n                  ["else" ":" suite]\n\nThis repeatedly tests the expression and, if it is true, executes the\nfirst suite; if the expression is false (which may be the first time\nit is tested) the suite of the "else" clause, if present, is executed\nand the loop terminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and goes back\nto testing the expression.\n',
- 'with': '\nThe "with" statement\n********************\n\nThe "with" statement is used to wrap the execution of a block with\nmethods defined by a context manager (see section *With Statement\nContext Managers*). This allows common "try"..."except"..."finally"\nusage patterns to be encapsulated for convenient reuse.\n\n   with_stmt ::= "with" with_item ("," with_item)* ":" suite\n   with_item ::= expression ["as" target]\n\nThe execution of the "with" statement with one "item" proceeds as\nfollows:\n\n1. The context expression (the expression given in the "with_item") is\n   evaluated to obtain a context manager.\n\n2. The context manager\'s "__exit__()" is loaded for later use.\n\n3. The context manager\'s "__enter__()" method is invoked.\n\n4. If a target was included in the "with" statement, the return value\n   from "__enter__()" is assigned to it.\n\n   Note: The "with" statement guarantees that if the "__enter__()" method\n     returns without an error, then "__exit__()" will always be\n     called. Thus, if an error occurs during the assignment to the\n     target list, it will be treated the same as an error occurring\n     within the suite would be. See step 6 below.\n\n5. The suite is executed.\n\n6. The context manager\'s "__exit__()" method is invoked.  If an\n   exception caused the suite to be exited, its type, value, and\n   traceback are passed as arguments to "__exit__()". Otherwise, three\n   "None" arguments are supplied.\n\n   If the suite was exited due to an exception, and the return value\n   from the "__exit__()" method was false, the exception is reraised.\n   If the return value was true, the exception is suppressed, and\n   execution continues with the statement following the "with"\n   statement.\n\n   If the suite was exited for any reason other than an exception, the\n   return value from "__exit__()" is ignored, and execution proceeds\n   at the normal location for the kind of exit that was taken.\n\nWith more than one item, the context managers are processed as if\nmultiple "with" statements were nested:\n\n   with A() as a, B() as b:\n       suite\n\nis equivalent to\n\n   with A() as a:\n       with B() as b:\n           suite\n\nChanged in version 3.1: Support for multiple context expressions.\n\nSee also:\n\n   **PEP 0343** - The "with" statement\n      The specification, background, and examples for the Python\n      "with" statement.\n',
- 'yield': '\nThe "yield" statement\n*********************\n\n   yield_stmt ::= yield_expression\n\nA "yield" statement is semantically equivalent to a *yield\nexpression*. The yield statement can be used to omit the parentheses\nthat would otherwise be required in the equivalent yield expression\nstatement. For example, the yield statements\n\n   yield <expr>\n   yield from <expr>\n\nare equivalent to the yield expression statements\n\n   (yield <expr>)\n   (yield from <expr>)\n\nYield expressions and statements are only used when defining a\n*generator* function, and are only used in the body of the generator\nfunction.  Using yield in a function definition is sufficient to cause\nthat definition to create a generator function instead of a normal\nfunction.\n\nFor full details of "yield" semantics, refer to the *Yield\nexpressions* section.\n'}
+# Autogenerated by Sphinx on Sun Sep 21 00:02:20 2014
+topics = {'assert': b'\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 to\n\n   if __debug__:\n      if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that "__debug__" and "AssertionError" refer\nto 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 (command\nline option -O).  The current code generator emits no code for an\nassert statement when optimization is requested at compile time.  Note\nthat it is unnecessary to include the source code for the expression\nthat failed in the error message; it will be displayed as part of the\nstack trace.\n\nAssignments to "__debug__" are illegal.  The value for the built-in\nvariable is determined when the interpreter starts.\n',
+ 'atom-identifiers': b'\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, with leading underscores removed and a single underscore\ninserted, in front of the name.  For example, the identifier "__spam"\noccurring in a class named "Ham" will be transformed to "_Ham__spam".\nThis transformation is independent of the syntactical context in which\nthe identifier is used.  If the transformed name is extremely long\n(longer than 255 characters), implementation defined truncation may\nhappen. If the class name consists only of underscores, no\ntransformation is done.\n',
+ 'atom-literals': b"\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\nAll literals correspond to immutable data types, and hence the\nobject's identity is less important than its value.  Multiple\nevaluations of literals with the same value (either the same\noccurrence in the program text or a different occurrence) may obtain\nthe same object or a different object with the same value.\n",
+ 'attribute-access': b'\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") for\nclass 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. This\n   method should return the (computed) attribute value or raise an\n   "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 for\n   efficiency reasons and because otherwise "__getattr__()" would have\n   no way to access other attributes of the instance.  Note that at\n   least for instance variables, you can fake total control by not\n   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 "__getattr__()",\n   the latter will not be called unless "__getattribute__()" either\n   calls it explicitly or raises an "AttributeError". This method\n   should return the (computed) attribute value or raise an\n   "AttributeError" exception. In order to avoid infinite recursion in\n   this method, its implementation should always call the base class\n   method with the same name to access any attributes it needs, for\n   example, "object.__getattribute__(self, name)".\n\n   Note: This method may still be bypassed when looking up special\n     methods as the result of implicit invocation via language syntax\n     or built-in 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 sequence must be\n   returned. "dir()" converts the returned sequence to a list and\n   sorts it.\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 an\n*owner* class (the descriptor must be in either the owner\'s class\ndictionary or in the class dictionary for one of its parents).  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 "AttributeError"\n   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\nThe attribute "__objclass__" is interpreted by the "inspect" module as\nspecifying the class where this object was defined (setting this\nappropriately can assist in runtime introspection of dynamic class\nattributes). For callables, it may indicate that an instance of the\ngiven type (or a subclass) is expected or required as the first\npositional argument (for example, CPython sets this attribute for\nunbound methods that are implemented in C).\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, it\nis 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". How\nthe 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 "A"\n   immediately preceding "B" and then invokes the descriptor with the\n   call: "A.__dict__[\'m\'].__get__(obj, obj.__class__)".\n\nFor instance bindings, the precedence of descriptor invocation depends\non the which descriptor methods are defined.  A descriptor can define\nany combination of "__get__()", "__set__()" and "__delete__()".  If it\ndoes not define "__get__()", then accessing the attribute will return\nthe descriptor object itself unless there is a value in the object\'s\ninstance dictionary.  If the descriptor defines "__set__()" and/or\n"__delete__()", it is a data descriptor; if it defines neither, it is\na non-data descriptor.  Normally, data descriptors define both\n"__get__()" and "__set__()", while non-data descriptors have just the\n"__get__()" method.  Data descriptors with "__set__()" and "__get__()"\ndefined always override a redefinition in an instance dictionary.  In\ncontrast, non-data descriptors can be overridden by instances.\n\nPython methods (including "staticmethod()" and "classmethod()") are\nimplemented 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\n  defining *__slots__* do not support weak references to its\n  instances. If weak reference support is needed, then add\n  "\'__weakref__\'" to the sequence of strings in the *__slots__*\n  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* 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__* (which must only contain names\n  of any *additional* slots).\n\n* If a class defines a slot also defined in a base class, the\n  instance variable defined by the base class slot is inaccessible\n  (except by retrieving its descriptor directly from the base class).\n  This renders the meaning of the program undefined.  In the future, a\n  check may be added to prevent this.\n\n* Nonempty *__slots__* does not work for classes derived from\n  "variable-length" built-in types such as "int", "bytes" and "tuple".\n\n* Any non-string iterable may be assigned to *__slots__*. Mappings\n  may 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': b'\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.  This\nproduction can be customized by overriding the "__getattr__()" method.\nIf this attribute 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': b'\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 ::= augtarget augop (expression_list | yield_expression)\n   augtarget                 ::= identifier | attributeref | subscription | slicing\n   augop                     ::= "+=" | "-=" | "*=" | "/=" | "//=" | "%=" | "**="\n             | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions of 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 the\naugmented version, "x" is only evaluated once. Also, when possible,\nthe actual operation is performed *in-place*, meaning that rather than\ncreating a new object and assigning that to the target, the old object\nis modified instead.\n\nUnlike normal assignments, augmented assignments evaluate the left-\nhand side *before* evaluating the right-hand side.  For example, "a[i]\n+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs\nthe addition, and lastly, it writes the result back to "a[i]".\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 same *caveat about\nclass and instance attributes* applies as for regular assignments.\n',
+ 'binary': b'\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 arguments.\nThe arguments must either both be numbers, or one argument must be an\ninteger and the other must be a sequence. In the former case, the\nnumbers are converted to a common type and then multiplied together.\nIn the latter case, sequence repetition is performed; a negative\nrepetition 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. Division of integers 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 "4*0.7 +\n0.34".)  The modulo operator always yields a result with the same sign\nas its second operand (or zero); the absolute value of the result is\nstrictly smaller than the absolute value of the second operand [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 also\nconnected 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 *printf-style String Formatting*.\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 be 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 arguments.\nThe numeric arguments are first converted to a common type.\n',
+ 'bitwise': b'\nBinary bitwise operations\n*************************\n\nEach of the three bitwise operations has a different priority level:\n\n   and_expr ::= shift_expr | and_expr "&" shift_expr\n   xor_expr ::= and_expr | xor_expr "^" and_expr\n   or_expr  ::= xor_expr | or_expr "|" xor_expr\n\nThe "&" operator yields the bitwise AND of its arguments, which must\nbe integers.\n\nThe "^" operator yields the bitwise XOR (exclusive OR) of its\narguments, which must be integers.\n\nThe "|" operator yields the bitwise (inclusive) OR of its arguments,\nwhich must be integers.\n',
+ 'bltin-code-objects': b'\nCode Objects\n************\n\nCode objects are used by the implementation to represent "pseudo-\ncompiled" executable Python code such as a function body. They differ\nfrom function objects because they don\'t contain a reference to their\nglobal execution environment.  Code objects are returned by the built-\nin "compile()" function and can be extracted from function objects\nthrough their "__code__" attribute. See also the "code" module.\n\nA code object can be executed or evaluated by passing it (instead of a\nsource string) to the "exec()" or "eval()"  built-in functions.\n\nSee *The standard type hierarchy* for more information.\n',
+ 'bltin-ellipsis-object': b'\nThe Ellipsis Object\n*******************\n\nThis object is commonly used by slicing (see *Slicings*).  It supports\nno special operations.  There is exactly one ellipsis object, named\n"Ellipsis" (a built-in name).  "type(Ellipsis)()" produces the\n"Ellipsis" singleton.\n\nIt is written as "Ellipsis" or "...".\n',
+ 'bltin-null-object': b'\nThe Null Object\n***************\n\nThis object is returned by functions that don\'t explicitly return a\nvalue.  It supports no special operations.  There is exactly one null\nobject, named "None" (a built-in name).  "type(None)()" produces the\nsame singleton.\n\nIt is written as "None".\n',
+ 'bltin-type-objects': b'\nType Objects\n************\n\nType objects represent the various object types.  An object\'s type is\naccessed by the built-in function "type()".  There are no special\noperations on types.  The standard module "types" defines names for\nall standard built-in types.\n\nTypes are written like this: "<class \'int\'>".\n',
+ 'booleans': b'\nBoolean operations\n******************\n\n   or_test  ::= and_test | or_test "or" and_test\n   and_test ::= not_test | and_test "and" not_test\n   not_test ::= comparison | "not" not_test\n\nIn the context of Boolean operations, and also when expressions are\nused by control flow statements, the following values are interpreted\nas false: "False", "None", numeric zero of all types, and empty\nstrings and containers (including strings, tuples, lists,\ndictionaries, sets and frozensets).  All other values are interpreted\nas true.  User-defined objects can customize their truth value by\nproviding a "__bool__()" method.\n\nThe operator "not" yields "True" if its argument is false, "False"\notherwise.\n\nThe expression "x and y" first evaluates *x*; if *x* is false, its\nvalue is returned; otherwise, *y* is evaluated and the resulting value\nis returned.\n\nThe expression "x or y" first evaluates *x*; if *x* is true, its value\nis returned; otherwise, *y* is evaluated and the resulting value is\nreturned.\n\n(Note that neither "and" nor "or" restrict the value and type they\nreturn to "False" and "True", but rather return the last evaluated\nargument.  This is sometimes useful, e.g., if "s" is a string that\nshould be replaced by a default value if it is empty, the expression\n"s or \'foo\'" yields the desired value.  Because "not" has to create a\nnew value, it returns a boolean value regardless of the type of its\nargument (for example, "not \'foo\'" produces "False" rather than "\'\'".)\n',
+ 'break': b'\nThe "break" statement\n*********************\n\n   break_stmt ::= "break"\n\n"break" may only occur syntactically nested in a "for" or "while"\nloop, but not nested in a function or class definition within that\nloop.\n\nIt terminates the nearest enclosing loop, skipping the optional "else"\nclause if the loop has one.\n\nIf a "for" loop is terminated by "break", the loop control target\nkeeps its current value.\n\nWhen "break" passes control out of a "try" statement with a "finally"\nclause, that "finally" clause is executed before really leaving the\nloop.\n',
+ 'callable-types': b'\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',
+ 'calls': b'\nCalls\n*****\n\nA call calls a callable object (e.g., a *function*) with a possibly\nempty series of *arguments*:\n\n   call                 ::= primary "(" [argument_list [","] | comprehension] ")"\n   argument_list        ::= positional_arguments ["," keyword_arguments]\n                       ["," "*" expression] ["," keyword_arguments]\n                       ["," "**" expression]\n                     | keyword_arguments ["," "*" expression]\n                       ["," keyword_arguments] ["," "**" expression]\n                     | "*" expression ["," keyword_arguments] ["," "**" expression]\n                     | "**" expression\n   positional_arguments ::= expression ("," expression)*\n   keyword_arguments    ::= keyword_item ("," keyword_item)*\n   keyword_item         ::= identifier "=" expression\n\nAn optional trailing comma may be present after the positional and\nkeyword arguments but does not affect the semantics.\n\nThe primary must evaluate to a callable object (user-defined\nfunctions, built-in functions, methods of built-in objects, class\nobjects, methods of class instances, and all objects having a\n"__call__()" method are callable).  All argument expressions are\nevaluated before the call is attempted.  Please refer to section\n*Function definitions* for the syntax of formal *parameter* lists.\n\nIf keyword arguments are present, they are first converted to\npositional arguments, as follows.  First, a list of unfilled slots is\ncreated for the formal parameters.  If there are N positional\narguments, they are placed in the first N slots.  Next, for each\nkeyword argument, the identifier is used to determine the\ncorresponding slot (if the identifier is the same as the first formal\nparameter name, the first slot is used, and so on).  If the slot is\nalready filled, a "TypeError" exception is raised. Otherwise, the\nvalue of the argument is placed in the slot, filling it (even if the\nexpression is "None", it fills the slot).  When all arguments have\nbeen processed, the slots that are still unfilled are filled with the\ncorresponding default value from the function definition.  (Default\nvalues are calculated, once, when the function is defined; thus, a\nmutable object such as a list or dictionary used as default value will\nbe shared by all calls that don\'t specify an argument value for the\ncorresponding slot; this should usually be avoided.)  If there are any\nunfilled slots for which no default value is specified, a "TypeError"\nexception is raised.  Otherwise, the list of filled slots is used as\nthe argument list for the call.\n\n**CPython implementation detail:** An implementation may provide\nbuilt-in functions whose positional parameters do not have names, even\nif they are \'named\' for the purpose of documentation, and which\ntherefore cannot be supplied by keyword.  In CPython, this is the case\nfor functions implemented in C that use "PyArg_ParseTuple()" to parse\ntheir arguments.\n\nIf there are more positional arguments than there are formal parameter\nslots, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "*identifier" is present; in this case, that formal\nparameter receives a tuple containing the excess positional arguments\n(or an empty tuple if there were no excess positional arguments).\n\nIf any keyword argument does not correspond to a formal parameter\nname, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "**identifier" is present; in this case, that formal\nparameter receives a dictionary containing the excess keyword\narguments (using the keywords as keys and the argument values as\ncorresponding values), or a (new) empty dictionary if there were no\nexcess keyword arguments.\n\nIf the syntax "*expression" appears in the function call, "expression"\nmust evaluate to an iterable.  Elements from this iterable are treated\nas if they were additional positional arguments; if there are\npositional arguments *x1*, ..., *xN*, and "expression" evaluates to a\nsequence *y1*, ..., *yM*, this is equivalent to a call with M+N\npositional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.\n\nA consequence of this is that although the "*expression" syntax may\nappear *after* some keyword arguments, it is processed *before* the\nkeyword arguments (and the "**expression" argument, if any -- see\nbelow).  So:\n\n   >>> def f(a, b):\n   ...  print(a, b)\n   ...\n   >>> f(b=1, *(2,))\n   2 1\n   >>> f(a=1, *(2,))\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in ?\n   TypeError: f() got multiple values for keyword argument \'a\'\n   >>> f(1, *(2,))\n   1 2\n\nIt is unusual for both keyword arguments and the "*expression" syntax\nto be used in the same call, so in practice this confusion does not\narise.\n\nIf the syntax "**expression" appears in the function call,\n"expression" must evaluate to a mapping, the contents of which are\ntreated as additional keyword arguments.  In the case of a keyword\nappearing in both "expression" and as an explicit keyword argument, a\n"TypeError" exception is raised.\n\nFormal parameters using the syntax "*identifier" or "**identifier"\ncannot be used as positional argument slots or as keyword argument\nnames.\n\nA call always returns some value, possibly "None", unless it raises an\nexception.  How this value is computed depends on the type of the\ncallable object.\n\nIf it is---\n\na user-defined function:\n   The code block for the function is executed, passing it the\n   argument list.  The first thing the code block will do is bind the\n   formal parameters to the arguments; this is described in section\n   *Function definitions*.  When the code block executes a "return"\n   statement, this specifies the return value of the function call.\n\na built-in function or method:\n   The result is up to the interpreter; see *Built-in Functions* for\n   the descriptions of built-in functions and methods.\n\na class object:\n   A new instance of that class is returned.\n\na class instance method:\n   The corresponding user-defined function is called, with an argument\n   list that is one longer than the argument list of the call: the\n   instance becomes the first argument.\n\na class instance:\n   The class must define a "__call__()" method; the effect is then the\n   same as if that method was called.\n',
+ 'class': b'\nClass definitions\n*****************\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n   classdef    ::= [decorators] "class" classname [inheritance] ":" suite\n   inheritance ::= "(" [parameter_list] ")"\n   classname   ::= identifier\n\nA class definition is an executable statement.  The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing.  Classes without an inheritance\nlist inherit, by default, from the base class "object"; hence,\n\n   class Foo:\n       pass\n\nis equivalent to\n\n   class Foo(object):\n       pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.)  When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n   @f1(arg)\n   @f2\n   class Foo: pass\n\nis equivalent to\n\n   class Foo: pass\n   Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators.  The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances.  Instance attributes\ncan be set in a method with "self.name = value".  Both class and\ninstance attributes are accessible through the notation ""self.name"",\nand an instance attribute hides a class attribute with the same name\nwhen accessed in this way.  Class attributes can be used as defaults\nfor instance attributes, but using mutable values there can lead to\nunexpected results.  *Descriptors* can be used to create instance\nvariables with different implementation details.\n\nSee also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -\n  Class Decorators\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless\n    there is a "finally" clause which happens to raise another\n    exception. That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of\n    an exception or the execution of a "return", "continue", or\n    "break" statement.\n\n[3] A string literal appearing as the first statement in the\n    function body is transformed into the function\'s "__doc__"\n    attribute and therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n    body is transformed into the namespace\'s "__doc__" item and\n    therefore the class\'s *docstring*.\n',
+ 'comparisons': b'\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 only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound 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", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. The\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of 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, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\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" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative 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 y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
+ 'context-managers': b'\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 manager\nhandles the entry into, and the exit from, the desired runtime context\nfor the execution of the block of code.  Context managers are normally\ninvoked using the "with" statement (described in section *The with\nstatement*), but can also be used by directly invoking their methods.\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: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n',
+ 'continue': b'\nThe "continue" statement\n************************\n\n   continue_stmt ::= "continue"\n\n"continue" may only occur syntactically nested in a "for" or "while"\nloop, but not nested in a function or class definition or "finally"\nclause within that loop.  It continues with the next cycle of the\nnearest 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': b'\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 as follows:\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\n  other 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 as a\nleft argument to the \'%\' operator).  Extensions must define their own\nconversion behavior.\n',
+ 'del': b'\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 than 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\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\nChanged in version 3.2: Previously it was illegal to delete a name\nfrom the local namespace if it occurs as a free variable in a nested\nblock.\n',
+ 'dict': b'\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',
+ 'dynamic-features': b'\nInteraction with dynamic features\n*********************************\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name.  An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nThe "eval()" and "exec()" functions do not have access to the full\nenvironment for resolving names.  Names may be resolved in the local\nand global namespaces of the caller.  Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace.  [1]\nThe "exec()" and "eval()" functions have optional arguments to\noverride the global and local namespace.  If only one namespace is\nspecified, it is used for both.\n',
+ 'else': b'\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',
+ 'exceptions': b'\nExceptions\n**********\n\nExceptions are a means of breaking out of the normal flow of control\nof a code block in order to handle errors or other exceptional\nconditions.  An exception is *raised* at the point where the error is\ndetected; it may be *handled* by the surrounding code block or by any\ncode block that directly or indirectly invoked the code block where\nthe error occurred.\n\nThe Python interpreter raises an exception when it detects a run-time\nerror (such as division by zero).  A Python program can also\nexplicitly raise an exception with the "raise" statement. Exception\nhandlers are specified with the "try" ... "except" statement.  The\n"finally" clause of such a statement can be used to specify cleanup\ncode which does not handle the exception, but is executed whether an\nexception occurred or not in the preceding code.\n\nPython uses the "termination" model of error handling: an exception\nhandler can find out what happened and continue execution at an outer\nlevel, but it cannot repair the cause of the error and retry the\nfailing operation (except by re-entering the offending piece of code\nfrom the top).\n\nWhen an exception is not handled at all, the interpreter terminates\nexecution of the program, or returns to its interactive main loop.  In\neither case, it prints a stack backtrace, except when the exception is\n"SystemExit".\n\nExceptions are identified by class instances.  The "except" clause is\nselected depending on the class of the instance: it must reference the\nclass of the instance or a base class thereof.  The instance can be\nreceived by the handler and can carry additional information about the\nexceptional condition.\n\nNote: Exception messages are not part of the Python API.  Their\n  contents may change from one version of Python to the next without\n  warning and should not be relied on by code which will run under\n  multiple versions of the interpreter.\n\nSee also the description of the "try" statement in section *The try\nstatement* and "raise" statement in section *The raise statement*.\n\n-[ Footnotes ]-\n\n[1] This limitation occurs because the code that is executed by\n    these operations is not available at the time the module is\n    compiled.\n',
+ 'exprlists': b'\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',
+ 'floating': b'\nFloating point literals\n***********************\n\nFloating point literals are described by the following lexical\ndefinitions:\n\n   floatnumber   ::= pointfloat | exponentfloat\n   pointfloat    ::= [intpart] fraction | intpart "."\n   exponentfloat ::= (intpart | pointfloat) exponent\n   intpart       ::= digit+\n   fraction      ::= "." digit+\n   exponent      ::= ("e" | "E") ["+" | "-"] digit+\n\nNote that the integer and exponent parts are always interpreted using\nradix 10. For example, "077e010" is legal, and denotes the same number\nas "77e10". The allowed range of floating point literals is\nimplementation-dependent. Some examples of floating point literals:\n\n   3.14    10.    .001    1e100    3.14e-10    0e0\n\nNote that numeric literals do not include a sign; a phrase like "-1"\nis actually an expression composed of the unary operator "-" and the\nliteral "1".\n',
+ 'for': b'\nThe "for" statement\n*******************\n\nThe "for" statement is used to iterate over the elements of a sequence\n(such as a string, tuple or list) or other iterable object:\n\n   for_stmt ::= "for" target_list "in" expression_list ":" suite\n                ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject.  An iterator is created for the result of the\n"expression_list".  The suite is then executed once for each item\nprovided by the iterator, in the order returned by the iterator.  Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted.  When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a "StopIteration" exception),\nthe suite in the "else" clause, if present, is executed, and the loop\nterminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and continues\nwith the next item, or with the "else" clause if there is no next\nitem.\n\nThe for-loop makes assignments to the variables(s) in the target list.\nThis overwrites all previous assignments to those variables including\nthose made in the suite of the for-loop:\n\n   for i in range(10):\n       print(i)\n       i = 5             # this will not affect the for-loop\n                         # because i will be overwritten with the next\n                         # index in the range\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, they will not have been assigned to at\nall by the loop.  Hint: the built-in function "range()" returns an\niterator of integers suitable to emulate the effect of Pascal\'s "for i\n:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".\n\nNote: There is a subtlety when the sequence is being modified by the\n  loop (this can only occur for mutable sequences, i.e. lists).  An\n  internal counter is used to keep track of which item is used next,\n  and this is incremented on each iteration.  When this counter has\n  reached the length of the sequence the loop terminates.  This means\n  that if the suite deletes the current (or a previous) item from the\n  sequence, the next item will be skipped (since it gets the index of\n  the current item which has already been treated).  Likewise, if the\n  suite inserts an item in the sequence before the current item, the\n  current item will be treated again the next time through the loop.\n  This can lead to nasty bugs that can be avoided by making a\n  temporary copy using a slice of the whole sequence, e.g.,\n\n     for x in a[:]:\n         if x < 0: a.remove(x)\n',
+ 'function': b'\nFunction definitions\n********************\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n   funcdef        ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n   decorators     ::= decorator+\n   decorator      ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n   dotted_name    ::= identifier ("." identifier)*\n   parameter_list ::= (defparameter ",")*\n                      | "*" [parameter] ("," defparameter)* ["," "**" parameter]\n                      | "**" parameter\n                      | defparameter [","] )\n   parameter      ::= identifier [":" expression]\n   defparameter   ::= parameter ["=" expression]\n   funcname       ::= identifier\n\nA function definition is an executable statement.  Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function).  This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition.  The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object.  Multiple decorators are applied in\nnested fashion. For example, the following code\n\n   @f1(arg)\n   @f2\n   def func(): pass\n\nis equivalent to\n\n   def func(): pass\n   func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* "="\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted.  If a parameter has a default value, all following\nparameters up until the ""*"" must also have a default value --- this\nis a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call.  This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended.  A way around this is to use\n"None" as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n   def whats_on_the_telly(penguin=None):\n       if penguin is None:\n           penguin = []\n       penguin.append("property of the zoo")\n       return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values.  If the form\n""*identifier"" is present, it is initialized to a tuple receiving any\nexcess positional parameters, defaulting to the empty tuple.  If the\nform ""**identifier"" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after ""*"" or ""*identifier"" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form "": expression"" following\nthe parameter name.  Any parameter may have an annotation even those\nof the form "*identifier" or "**identifier".  Functions may have\n"return" annotation of the form ""-> expression"" after the parameter\nlist.  These annotations can be any valid Python expression and are\nevaluated when the function definition is executed.  Annotations may\nbe evaluated in a different order than they appear in the source code.\nThe presence of annotations does not change the semantics of a\nfunction.  The annotation values are available as values of a\ndictionary keyed by the parameters\' names in the "__annotations__"\nattribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions.  This uses lambda\nexpressions, described in section *Lambdas*.  Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression.  The ""def"" form is actually more powerful since it\nallows the execution of multiple statements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects.  A ""def""\nstatement executed inside a function definition defines a local\nfunction that can be returned or passed around.  Free variables used\nin the nested function can access the local variables of the function\ncontaining the def.  See section *Naming and binding* for details.\n\nSee also: **PEP 3107** - Function Annotations\n\n     The original specification for function annotations.\n',
+ 'global': b'\nThe "global" statement\n**********************\n\n   global_stmt ::= "global" identifier ("," identifier)*\n\nThe "global" statement is a declaration which holds for the entire\ncurrent code block.  It means that the listed identifiers are to be\ninterpreted as globals.  It would be impossible to assign to a global\nvariable without "global", although free variables may refer to\nglobals without being declared global.\n\nNames listed in a "global" statement must not be used in the same code\nblock textually preceding that "global" statement.\n\nNames listed in a "global" statement must not be defined as formal\nparameters or in a "for" loop control target, "class" definition,\nfunction definition, or "import" statement.\n\n**CPython implementation detail:** The current implementation does not\nenforce the two restrictions, but programs should not abuse this\nfreedom, as future implementations may enforce them or silently change\nthe meaning of the program.\n\n**Programmer\'s note:** the "global" is a directive to the parser.  It\napplies only to code parsed at the same time as the "global"\nstatement. In particular, a "global" statement contained in a string\nor code object supplied to the built-in "exec()" function does not\naffect the code block *containing* the function call, and code\ncontained in such a string is unaffected by "global" statements in the\ncode containing the function call.  The same applies to the "eval()"\nand "compile()" functions.\n',
+ 'id-classes': b'\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 the\n   last evaluation; it is stored in the "builtins" module.  When not\n   in interactive mode, "_" has no special meaning and is not defined.\n   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).  Current\n   system names are discussed in the *Special method names* section\n   and elsewhere.  More will likely be defined in future versions of\n   Python.  *Any* use of "__*__" names, in any context, that does not\n   follow explicitly documented use, is subject to breakage without\n   warning.\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',
+ 'identifiers': b'\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 first\ncharacter, 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   ::= xid_start xid_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   xid_start    ::= <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">\n   xid_continue ::= <all characters in id_continue whose NFKC normalization is in "id_continue*">\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\n* *Other_ID_Start* - explicit list of characters in PropList.txt to\n  support backwards compatibility\n\n* *Other_ID_Continue* - likewise\n\nAll identifiers are converted into the normal form NFKC while parsing;\ncomparison of identifiers is based on NFKC.\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 the\n   last evaluation; it is stored in the "builtins" module.  When not\n   in interactive mode, "_" has no special meaning and is not defined.\n   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).  Current\n   system names are discussed in the *Special method names* section\n   and elsewhere.  More will likely be defined in future versions of\n   Python.  *Any* use of "__*__" names, in any context, that does not\n   follow explicitly documented use, is subject to breakage without\n   warning.\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': b'\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': b'\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',
+ 'in': b'\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 only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound 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", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. The\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of 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, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\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" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative 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 y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
+ 'integers': b'\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': b'\nLambdas\n*******\n\n   lambda_expr        ::= "lambda" [parameter_list]: expression\n   lambda_expr_nocond ::= "lambda" [parameter_list]: expression_nocond\n\nLambda expressions (sometimes called lambda forms) are used to create\nanonymous functions. The expression "lambda arguments: expression"\nyields a function object.  The unnamed object behaves like a function\nobject defined with\n\n   def <lambda>(arguments):\n       return expression\n\nSee section *Function definitions* for the syntax of parameter lists.\nNote that functions created with lambda expressions cannot contain\nstatements or annotations.\n',
+ 'lists': b'\nList displays\n*************\n\nA list display is a possibly empty series of expressions enclosed in\nsquare brackets:\n\n   list_display ::= "[" [expression_list | comprehension] "]"\n\nA list display yields a new list object, the contents being specified\nby either a list of expressions or a comprehension.  When a comma-\nseparated list of expressions is supplied, its elements are evaluated\nfrom left to right and placed into the list object in that order.\nWhen a comprehension is supplied, the list is constructed from the\nelements resulting from the comprehension.\n',
+ 'naming': b'\nNaming and binding\n******************\n\n*Names* refer to objects.  Names are introduced by name binding\noperations. Each occurrence of a name in the program text refers to\nthe *binding* of that name established in the innermost function block\ncontaining the use.\n\nA *block* is a piece of Python program text that is executed as a\nunit. The following are blocks: a module, a function body, and a class\ndefinition. Each command typed interactively is a block.  A script\nfile (a file given as standard input to the interpreter or specified\nas a command line argument to the interpreter) is a code block.  A\nscript command (a command specified on the interpreter command line\nwith the \'**-c**\' option) is a code block.  The string argument passed\nto the built-in functions "eval()" and "exec()" is a code block.\n\nA code block is executed in an *execution frame*.  A frame contains\nsome administrative information (used for debugging) and determines\nwhere and how execution continues after the code block\'s execution has\ncompleted.\n\nA *scope* defines the visibility of a name within a block.  If a local\nvariable is defined in a block, its scope includes that block.  If the\ndefinition occurs in a function block, the scope extends to any blocks\ncontained within the defining one, unless a contained block introduces\na different binding for the name.  The scope of names defined in a\nclass block is limited to the class block; it does not extend to the\ncode blocks of methods -- this includes comprehensions and generator\nexpressions since they are implemented using a function scope.  This\nmeans that the following will fail:\n\n   class A:\n       a = 42\n       b = list(a + i for i in range(10))\n\nWhen a name is used in a code block, it is resolved using the nearest\nenclosing scope.  The set of all such scopes visible to a code block\nis called the block\'s *environment*.\n\nIf a name is bound in a block, it is a local variable of that block,\nunless declared as "nonlocal".  If a name is bound at the module\nlevel, it is a global variable.  (The variables of the module code\nblock are local and global.)  If a variable is used in a code block\nbut not defined there, it is a *free variable*.\n\nWhen a name is not found at all, a "NameError" exception is raised.\nIf the name refers to a local variable that has not been bound, an\n"UnboundLocalError" exception is raised.  "UnboundLocalError" is a\nsubclass of "NameError".\n\nThe following constructs bind names: formal parameters to functions,\n"import" statements, class and function definitions (these bind the\nclass or function name in the defining block), and targets that are\nidentifiers if occurring in an assignment, "for" loop header, or after\n"as" in a "with" statement or "except" clause. The "import" statement\nof the form "from ... import *" binds all names defined in the\nimported module, except those beginning with an underscore.  This form\nmay only be used at the module level.\n\nA target occurring in a "del" statement is also considered bound for\nthis purpose (though the actual semantics are to unbind the name).\n\nEach assignment or import statement occurs within a block defined by a\nclass or function definition or at the module level (the top-level\ncode block).\n\nIf a name binding operation occurs anywhere within a code block, all\nuses of the name within the block are treated as references to the\ncurrent block.  This can lead to errors when a name is used within a\nblock before it is bound.  This rule is subtle.  Python lacks\ndeclarations and allows name binding operations to occur anywhere\nwithin a code block.  The local variables of a code block can be\ndetermined by scanning the entire text of the block for name binding\noperations.\n\nIf the "global" statement occurs within a block, all uses of the name\nspecified in the statement refer to the binding of that name in the\ntop-level namespace.  Names are resolved in the top-level namespace by\nsearching the global namespace, i.e. the namespace of the module\ncontaining the code block, and the builtins namespace, the namespace\nof the module "builtins".  The global namespace is searched first.  If\nthe name is not found there, the builtins namespace is searched.  The\nglobal statement must precede all uses of the name.\n\nThe builtins namespace associated with the execution of a code block\nis actually found by looking up the name "__builtins__" in its global\nnamespace; this should be a dictionary or a module (in the latter case\nthe module\'s dictionary is used).  By default, when in the "__main__"\nmodule, "__builtins__" is the built-in module "builtins"; when in any\nother module, "__builtins__" is an alias for the dictionary of the\n"builtins" module itself.  "__builtins__" can be set to a user-created\ndictionary to create a weak form of restricted execution.\n\n**CPython implementation detail:** Users should not touch\n"__builtins__"; it is strictly an implementation detail.  Users\nwanting to override values in the builtins namespace should "import"\nthe "builtins" module and modify its attributes appropriately.\n\nThe namespace for a module is automatically created the first time a\nmodule is imported.  The main module for a script is always called\n"__main__".\n\nThe "global" statement has the same scope as a name binding operation\nin the same block.  If the nearest enclosing scope for a free variable\ncontains a global statement, the free variable is treated as a global.\n\nA class definition is an executable statement that may use and define\nnames. These references follow the normal rules for name resolution.\nThe namespace of the class definition becomes the attribute dictionary\nof the class.  Names defined at the class scope are not visible in\nmethods.\n\n\nInteraction with dynamic features\n=================================\n\nThere are several cases where Python statements are illegal when used\nin conjunction with nested scopes that contain free variables.\n\nIf a variable is referenced in an enclosing scope, it is illegal to\ndelete the name.  An error will be reported at compile time.\n\nIf the wild card form of import --- "import *" --- is used in a\nfunction and the function contains or is a nested block with free\nvariables, the compiler will raise a "SyntaxError".\n\nThe "eval()" and "exec()" functions do not have access to the full\nenvironment for resolving names.  Names may be resolved in the local\nand global namespaces of the caller.  Free variables are not resolved\nin the nearest enclosing namespace, but in the global namespace.  [1]\nThe "exec()" and "eval()" functions have optional arguments to\noverride the global and local namespace.  If only one namespace is\nspecified, it is used for both.\n',
+ 'nonlocal': b'\nThe "nonlocal" statement\n************************\n\n   nonlocal_stmt ::= "nonlocal" identifier ("," identifier)*\n\nThe "nonlocal" statement causes the listed identifiers to refer to\npreviously bound variables in the nearest enclosing scope excluding\nglobals. This is important because the default behavior for binding is\nto search the local namespace first.  The statement allows\nencapsulated code to rebind variables outside of the local scope\nbesides the global (module) scope.\n\nNames listed in a "nonlocal" statement, unlike those listed in a\n"global" statement, must refer to pre-existing bindings in an\nenclosing scope (the scope in which a new binding should be created\ncannot be determined unambiguously).\n\nNames listed in a "nonlocal" statement must not collide with pre-\nexisting bindings in the local scope.\n\nSee also: **PEP 3104** - Access to Names in Outer Scopes\n\n     The specification for the "nonlocal" statement.\n',
+ 'numbers': b'\nNumeric literals\n****************\n\nThere are three types of numeric literals: integers, floating point\nnumbers, and imaginary numbers.  There are no complex literals\n(complex numbers can be formed by adding a real number and an\nimaginary number).\n\nNote that numeric literals do not include a sign; a phrase like "-1"\nis actually an expression composed of the unary operator \'"-"\' and the\nliteral "1".\n',
+ 'numeric-types': b'\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 ("+", "-", "*", "/", "//", "%", "divmod()", "pow()",\n   "**", "<<", ">>", "&", "^", "|").  For instance, to evaluate the\n   expression "x + y", where *x* is an instance of a class that has an\n   "__add__()" method, "x.__add__(y)" is called.  The "__divmod__()"\n   method should be the equivalent to using "__floordiv__()" and\n   "__mod__()"; it should not be related to "__truediv__()".  Note\n   that "__pow__()" should be defined to accept an optional third\n   argument if the ternary version of the built-in "pow()" function is\n   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 ("+", "-", "*", "/", "//", "%", "divmod()", "pow()",\n   "**", "<<", ">>", "&", "^", "|") with reflected (swapped) operands.\n   These functions are only called if the left operand does not\n   support the corresponding operation and the operands are of\n   different types. [2]  For instance, to evaluate the expression "x -\n   y", where *y* is an instance of a class that has an "__rsub__()"\n   method, "y.__rsub__(x)" is called if "x.__sub__(y)" returns\n   *NotImplemented*.\n\n   Note that ternary "pow()" will not try calling "__rpow__()" (the\n   coercion rules would become too complicated).\n\n   Note: If the right operand\'s type is a subclass of the left\n     operand\'s type and that subclass provides the reflected method\n     for the operation, this method will be called before the left\n     operand\'s non-reflected method.  This behavior allows subclasses\n     to 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   assignments ("+=", "-=", "*=", "/=", "//=", "%=", "**=", "<<=",\n   ">>=", "&=", "^=", "|=").  These methods should attempt to do the\n   operation in-place (modifying *self*) and return the result (which\n   could be, but does not have to be, *self*).  If a specific method\n   is not defined, the augmented assignment falls back to the normal\n   methods.  For instance, if *x* is an instance of a class with an\n   "__iadd__()" method, "x += y" is equivalent to "x = x.__iadd__(y)"\n   . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are considered, as\n   with the evaluation of "x + y". In certain situations, augmented\n   assignment can result in unexpected errors (see *Why does\n   a_tuple[i] += [\'item\'] raise an exception when the addition\n   works?*), but this behavior is in fact part of the data model.\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()", "int()",\n   "float()" and "round()".  Should return a value of the appropriate\n   type.\n\nobject.__index__(self)\n\n   Called to implement "operator.index()", and whenever Python needs\n   to losslessly convert the numeric object to an integer object (such\n   as in slicing, or in the built-in "bin()", "hex()" and "oct()"\n   functions). Presence of this method indicates that the numeric\n   object is an integer type.  Must return an integer.\n\n   Note: In order to have a coherent integer type class, when\n     "__index__()" is defined "__int__()" should also be defined, and\n     both should return the same value.\n',
+ 'objects': b'\nObjects, values and types\n*************************\n\n*Objects* are Python\'s abstraction for data.  All data in a Python\nprogram is represented by objects or by relations between objects. (In\na sense, and in conformance to Von Neumann\'s model of a "stored\nprogram computer," code is also represented by objects.)\n\nEvery object has an identity, a type and a value.  An object\'s\n*identity* never changes once it has been created; you may think of it\nas the object\'s address in memory.  The \'"is"\' operator compares the\nidentity of two objects; the "id()" function returns an integer\nrepresenting its identity.\n\n**CPython implementation detail:** For CPython, "id(x)" is the memory\naddress where "x" is stored.\n\nAn object\'s type determines the operations that the object supports\n(e.g., "does it have a length?") and also defines the possible values\nfor objects of that type.  The "type()" function returns an object\'s\ntype (which is an object itself).  Like its identity, an object\'s\n*type* is also unchangeable. [1]\n\nThe *value* of some objects can change.  Objects whose value can\nchange are said to be *mutable*; objects whose value is unchangeable\nonce they are created are called *immutable*. (The value of an\nimmutable container object that contains a reference to a mutable\nobject can change when the latter\'s value is changed; however the\ncontainer is still considered immutable, because the collection of\nobjects it contains cannot be changed.  So, immutability is not\nstrictly the same as having an unchangeable value, it is more subtle.)\nAn object\'s mutability is determined by its type; for instance,\nnumbers, strings and tuples are immutable, while dictionaries and\nlists are mutable.\n\nObjects are never explicitly destroyed; however, when they become\nunreachable they may be garbage-collected.  An implementation is\nallowed to postpone garbage collection or omit it altogether --- it is\na matter of implementation quality how garbage collection is\nimplemented, as long as no objects are collected that are still\nreachable.\n\n**CPython implementation detail:** CPython currently uses a reference-\ncounting scheme with (optional) delayed detection of cyclically linked\ngarbage, which collects most objects as soon as they become\nunreachable, but is not guaranteed to collect garbage containing\ncircular references.  See the documentation of the "gc" module for\ninformation on controlling the collection of cyclic garbage. Other\nimplementations act differently and CPython may change. Do not depend\non immediate finalization of objects when they become unreachable (so\nyou should always close files explicitly).\n\nNote that the use of the implementation\'s tracing or debugging\nfacilities may keep objects alive that would normally be collectable.\nAlso note that catching an exception with a \'"try"..."except"\'\nstatement may keep objects alive.\n\nSome objects contain references to "external" resources such as open\nfiles or windows.  It is understood that these resources are freed\nwhen the object is garbage-collected, but since garbage collection is\nnot guaranteed to happen, such objects also provide an explicit way to\nrelease the external resource, usually a "close()" method. Programs\nare strongly recommended to explicitly close such objects.  The\n\'"try"..."finally"\' statement and the \'"with"\' statement provide\nconvenient ways to do this.\n\nSome objects contain references to other objects; these are called\n*containers*. Examples of containers are tuples, lists and\ndictionaries.  The references are part of a container\'s value.  In\nmost cases, when we talk about the value of a container, we imply the\nvalues, not the identities of the contained objects; however, when we\ntalk about the mutability of a container, only the identities of the\nimmediately contained objects are implied.  So, if an immutable\ncontainer (like a tuple) contains a reference to a mutable object, its\nvalue changes if that mutable object is changed.\n\nTypes affect almost all aspects of object behavior.  Even the\nimportance of object identity is affected in some sense: for immutable\ntypes, operations that compute new values may actually return a\nreference to any existing object with the same type and value, while\nfor mutable objects this is not allowed.  E.g., after "a = 1; b = 1",\n"a" and "b" may or may not refer to the same object with the value\none, depending on the implementation, but after "c = []; d = []", "c"\nand "d" are guaranteed to refer to two different, unique, newly\ncreated empty lists. (Note that "c = d = []" assigns the same object\nto both "c" and "d".)\n',
+ 'operator-summary': b'\nOperator precedence\n*******************\n\nThe following table summarizes the operator precedence in Python, from\nlowest precedence (least binding) to highest precedence (most\nbinding).  Operators in the same box have the same precedence.  Unless\nthe syntax is explicitly given, operators are binary.  Operators in\nthe same box group left to right (except for exponentiation, which\ngroups from right to left).\n\nNote that comparisons, membership tests, and identity tests, all have\nthe same precedence and have a left-to-right chaining feature as\ndescribed in the *Comparisons* section.\n\n+-------------------------------------------------+---------------------------------------+\n| Operator                                        | Description                           |\n+=================================================+=======================================+\n| "lambda"                                        | Lambda expression                     |\n+-------------------------------------------------+---------------------------------------+\n| "if" -- "else"                                  | Conditional expression                |\n+-------------------------------------------------+---------------------------------------+\n| "or"                                            | Boolean OR                            |\n+-------------------------------------------------+---------------------------------------+\n| "and"                                           | Boolean AND                           |\n+-------------------------------------------------+---------------------------------------+\n| "not" "x"                                       | Boolean NOT                           |\n+-------------------------------------------------+---------------------------------------+\n| "in", "not in", "is", "is not", "<", "<=", ">", | Comparisons, including membership     |\n| ">=", "!=", "=="                                | tests and identity tests              |\n+-------------------------------------------------+---------------------------------------+\n| "|"                                             | Bitwise OR                            |\n+-------------------------------------------------+---------------------------------------+\n| "^"                                             | Bitwise XOR                           |\n+-------------------------------------------------+---------------------------------------+\n| "&"                                             | Bitwise AND                           |\n+-------------------------------------------------+---------------------------------------+\n| "<<", ">>"                                      | Shifts                                |\n+-------------------------------------------------+---------------------------------------+\n| "+", "-"                                        | Addition and subtraction              |\n+-------------------------------------------------+---------------------------------------+\n| "*", "/", "//", "%"                             | Multiplication, division, remainder   |\n|                                                 | [5]                                   |\n+-------------------------------------------------+---------------------------------------+\n| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |\n+-------------------------------------------------+---------------------------------------+\n| "**"                                            | Exponentiation [6]                    |\n+-------------------------------------------------+---------------------------------------+\n| "x[index]", "x[index:index]",                   | Subscription, slicing, call,          |\n| "x(arguments...)", "x.attribute"                | attribute reference                   |\n+-------------------------------------------------+---------------------------------------+\n| "(expressions...)", "[expressions...]", "{key:  | Binding or tuple display, list        |\n| value...}", "{expressions...}"                  | display, dictionary display, set      |\n|                                                 | display                               |\n+-------------------------------------------------+---------------------------------------+\n\n-[ Footnotes ]-\n\n[1] While "abs(x%y) < abs(y)" is true mathematically, for floats\n    it may not be true numerically due to roundoff.  For example, and\n    assuming a platform on which a Python float is an IEEE 754 double-\n    precision number, in order that "-1e-100 % 1e100" have the same\n    sign as "1e100", the computed result is "-1e-100 + 1e100", which\n    is numerically exactly equal to "1e100".  The function\n    "math.fmod()" returns a result whose sign matches the sign of the\n    first argument instead, and so returns "-1e-100" in this case.\n    Which approach is more appropriate depends on the application.\n\n[2] If x is very close to an exact integer multiple of y, it\'s\n    possible for "x//y" to be one larger than "(x-x%y)//y" due to\n    rounding.  In such cases, Python returns the latter result, in\n    order to preserve that "divmod(x,y)[0] * y + x % y" be very close\n    to "x".\n\n[3] While comparisons between strings make sense at the byte\n    level, they may be counter-intuitive to users.  For example, the\n    strings ""\\u00C7"" and ""\\u0327\\u0043"" compare differently, even\n    though they both represent the same unicode character (LATIN\n    CAPITAL LETTER C WITH CEDILLA).  To compare strings in a human\n    recognizable way, compare using "unicodedata.normalize()".\n\n[4] Due to automatic garbage-collection, free lists, and the\n    dynamic nature of descriptors, you may notice seemingly unusual\n    behaviour in certain uses of the "is" operator, like those\n    involving comparisons between instance methods, or constants.\n    Check their documentation for more info.\n\n[5] The "%" operator is also used for string formatting; the same\n    precedence applies.\n\n[6] The power operator "**" binds less tightly than an arithmetic\n    or bitwise unary operator on its right, that is, "2**-1" is "0.5".\n',
+ 'pass': b'\nThe "pass" statement\n********************\n\n   pass_stmt ::= "pass"\n\n"pass" is a null operation --- when it is executed, nothing happens.\nIt is useful as a placeholder when a statement is required\nsyntactically, but no code needs to be executed, for example:\n\n   def f(arg): pass    # a function that does nothing (yet)\n\n   class C: pass       # a class with no methods (yet)\n',
+ 'power': b'\nThe power operator\n******************\n\nThe power operator binds more tightly than unary operators on its\nleft; it binds less tightly than unary operators on its right.  The\nsyntax is:\n\n   power ::= primary ["**" u_expr]\n\nThus, in an unparenthesized sequence of power and unary operators, the\noperators are evaluated from right to left (this does not constrain\nthe evaluation order for the operands): "-1**2" results in "-1".\n\nThe power operator has the same semantics as the built-in "pow()"\nfunction, when called with two arguments: it yields its left argument\nraised to the power of its right argument.  The numeric arguments are\nfirst converted to a common type, and the result is of that type.\n\nFor int operands, the result has the same type as the operands unless\nthe second argument is negative; in that case, all arguments are\nconverted to float and a float result is delivered. For example,\n"10**2" returns "100", but "10**-2" returns "0.01".\n\nRaising "0.0" to a negative power results in a "ZeroDivisionError".\nRaising a negative number to a fractional power results in a "complex"\nnumber. (In earlier versions it raised a "ValueError".)\n',
+ 'raise': b'\nThe "raise" statement\n*********************\n\n   raise_stmt ::= "raise" [expression ["from" expression]]\n\nIf no expressions are present, "raise" re-raises the last exception\nthat was active in the current scope.  If no exception is active in\nthe current scope, a "RuntimeError" exception is raised indicating\nthat this is an error.\n\nOtherwise, "raise" evaluates the first expression as the exception\nobject.  It must be either a subclass or an instance of\n"BaseException". If it is a class, the exception instance will be\nobtained when needed by instantiating the class with no arguments.\n\nThe *type* of the exception is the exception instance\'s class, the\n*value* is the instance itself.\n\nA traceback object is normally created automatically when an exception\nis raised and attached to it as the "__traceback__" attribute, which\nis writable. You can create an exception and set your own traceback in\none step using the "with_traceback()" exception method (which returns\nthe same exception instance, with its traceback set to its argument),\nlike so:\n\n   raise Exception("foo occurred").with_traceback(tracebackobj)\n\nThe "from" clause is used for exception chaining: if given, the second\n*expression* must be another exception class or instance, which will\nthen be attached to the raised exception as the "__cause__" attribute\n(which is writable).  If the raised exception is not handled, both\nexceptions will be printed:\n\n   >>> try:\n   ...     print(1 / 0)\n   ... except Exception as exc:\n   ...     raise RuntimeError("Something bad happened") from exc\n   ...\n   Traceback (most recent call last):\n     File "<stdin>", line 2, in <module>\n   ZeroDivisionError: int division or modulo by zero\n\n   The above exception was the direct cause of the following exception:\n\n   Traceback (most recent call last):\n     File "<stdin>", line 4, in <module>\n   RuntimeError: Something bad happened\n\nA similar mechanism works implicitly if an exception is raised inside\nan exception handler: the previous exception is then attached as the\nnew exception\'s "__context__" attribute:\n\n   >>> try:\n   ...     print(1 / 0)\n   ... except:\n   ...     raise RuntimeError("Something bad happened")\n   ...\n   Traceback (most recent call last):\n     File "<stdin>", line 2, in <module>\n   ZeroDivisionError: int division or modulo by zero\n\n   During handling of the above exception, another exception occurred:\n\n   Traceback (most recent call last):\n     File "<stdin>", line 4, in <module>\n   RuntimeError: Something bad happened\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information about handling exceptions is in section\n*The try statement*.\n',
+ 'return': b'\nThe "return" statement\n**********************\n\n   return_stmt ::= "return" [expression_list]\n\n"return" may only occur syntactically nested in a function definition,\nnot within a nested class definition.\n\nIf an expression list is present, it is evaluated, else "None" is\nsubstituted.\n\n"return" leaves the current function call with the expression list (or\n"None") as return value.\n\nWhen "return" passes control out of a "try" statement with a "finally"\nclause, that "finally" clause is executed before really leaving the\nfunction.\n\nIn a generator function, the "return" statement indicates that the\ngenerator is done and will cause "StopIteration" to be raised. The\nreturned value (if any) is used as an argument to construct\n"StopIteration" and becomes the "StopIteration.value" attribute.\n',
+ 'sequence-types': b'\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" where\n*N* is the length of the sequence, or slice objects, which define a\nrange of items.  It is also recommended that mappings provide the\nmethods "keys()", "values()", "items()", "get()", "clear()",\n"setdefault()", "pop()", "popitem()", "copy()", and "update()"\nbehaving similar to those for Python\'s standard dictionary objects.\nThe "collections" module provides a "MutableMapping" abstract base\nclass to help create those methods from a base set of "__getitem__()",\n"__setitem__()", "__delitem__()", and "keys()". Mutable sequences\nshould provide methods "append()", "count()", "index()", "extend()",\n"insert()", "pop()", "remove()", "reverse()" and "sort()", like Python\nstandard list objects.  Finally, sequence types should implement\naddition (meaning concatenation) and multiplication (meaning\nrepetition) by defining the methods "__add__()", "__radd__()",\n"__iadd__()", "__mul__()", "__rmul__()" and "__imul__()" described\nbelow; they should not define other numerical operators.  It is\nrecommended that both mappings and sequences implement the\n"__contains__()" method to allow efficient use of the "in" operator;\nfor mappings, "in" should search the mapping\'s keys; for sequences, it\nshould search through the values.  It is further recommended that both\nmappings and sequences implement the "__iter__()" method to allow\nefficient iteration through the container; for mappings, "__iter__()"\nshould be the same as "keys()"; for sequences, it should iterate\nthrough 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 that\n   doesn\'t define a "__bool__()" method and whose "__len__()" method\n   returns zero is considered to be false in a Boolean context.\n\nobject.__length_hint__(self)\n\n   Called to implement "operator.length_hint()". Should return an\n   estimated length for the object (which may be greater or less than\n   the actual length). The length must be an integer ">=" 0. This\n   method is purely an optimization and is never required for\n   correctness.\n\n   New in version 3.4.\n\nNote: Slicing is done exclusively with the following three methods.\n  A 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 "None".\n\nobject.__getitem__(self, key)\n\n   Called to implement evaluation of "self[key]". For sequence types,\n   the accepted keys should be integers and slice objects.  Note that\n   the special interpretation of negative indexes (if the class wishes\n   to emulate a sequence type) is up to the "__getitem__()" method. If\n   *key* is of an inappropriate type, "TypeError" may be raised; if of\n   a value outside the set of indexes for the sequence (after any\n   special interpretation of negative values), "IndexError" should be\n   raised. For mapping types, if *key* is missing (not in the\n   container), "KeyError" 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__()" 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()" built-in 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 "reversed()"\n   built-in will fall back to using the sequence protocol ("__len__()"\n   and "__getitem__()").  Objects that support the sequence protocol\n   should only provide "__reversed__()" if they can provide an\n   implementation that is more efficient than the one provided by\n   "reversed()".\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   For objects that don\'t define "__contains__()", the membership test\n   first tries iteration via "__iter__()", then the old sequence\n   iteration protocol via "__getitem__()", see *this section in the\n   language reference*.\n',
+ 'shifting': b'\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 floor division by "pow(2,n)".\nA left shift by *n* bits is defined as multiplication with "pow(2,n)".\n\nNote: In the current implementation, the right-hand operand is\n  required to be at most "sys.maxsize".  If the right-hand operand is\n  larger than "sys.maxsize" an "OverflowError" exception is raised.\n',
+ 'slicings': b'\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 slicing:\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 "__getitem__()"\nmethod as normal subscription) with a key that is constructed from the\nslice list, as follows.  If the slice list contains at least one\ncomma, the key is a tuple containing the conversion of the slice\nitems; otherwise, the conversion of the lone slice item is the key.\nThe conversion of a slice item that is an expression is that\nexpression.  The conversion of a proper slice is a slice object (see\nsection *The standard type hierarchy*) whose "start", "stop" and\n"step" attributes are the values of the expressions given as lower\nbound, upper bound and stride, respectively, substituting "None" for\nmissing expressions.\n',
+ 'specialattrs': b'\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.\n\nclass.__name__\n\n   The name of the class or type.\n\nclass.__qualname__\n\n   The *qualified name* of the class or type.\n\n   New in version 3.3.\n\nclass.__mro__\n\n   This attribute is a tuple of classes that are considered when\n   looking for base classes during method resolution.\n\nclass.mro()\n\n   This method can be overridden by a metaclass to customize the\n   method resolution order for its instances.  It is called at class\n   instantiation, and its result is stored in "__mro__".\n\nclass.__subclasses__()\n\n   Each class keeps a list of weak references to its immediate\n   subclasses.  This method returns a list of all those references\n   still alive. Example:\n\n      >>> int.__subclasses__()\n      [<class \'bool\'>]\n\n-[ Footnotes ]-\n\n[1] Additional information on these special methods may be found\n    in 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] Cased characters are those with general category property\n    being one of "Lu" (Letter, uppercase), "Ll" (Letter, lowercase),\n    or "Lt" (Letter, titlecase).\n\n[5] To format only a tuple you should therefore provide a\n    singleton tuple whose only element is the tuple to be formatted.\n',
+ 'string-methods': b'\nString Methods\n**************\n\nStrings implement all of the *common* sequence operations, along with\nthe additional methods described below.\n\nStrings also support two styles of string formatting, one providing a\nlarge degree of flexibility and customization (see "str.format()",\n*Format String Syntax* and *String Formatting*) and the other based on\nC "printf" style formatting that handles a narrower range of types and\nis slightly harder to use correctly, but is often faster for the cases\nit can handle (*printf-style String Formatting*).\n\nThe *Text Processing Services* section of the standard library covers\na number of other modules that provide various text related utilities\n(including regular expression support in the "re" module).\n\nstr.capitalize()\n\n   Return a copy of the string with its first character capitalized\n   and the rest lowercased.\n\nstr.casefold()\n\n   Return a casefolded copy of the string. Casefolded strings may be\n   used for caseless matching.\n\n   Casefolding is similar to lowercasing but more aggressive because\n   it is intended to remove all case distinctions in a string. For\n   example, the German lowercase letter "\'\xc3\x9f\'" is equivalent to ""ss"".\n   Since it is already lowercase, "lower()" would do nothing to "\'\xc3\x9f\'";\n   "casefold()" converts it to ""ss"".\n\n   The casefolding algorithm is described in section 3.13 of the\n   Unicode Standard.\n\n   New in version 3.3.\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 an ASCII space). The\n   original string is returned if *width* is less than or equal to\n   "len(s)".\n\nstr.count(sub[, start[, end]])\n\n   Return the number of non-overlapping occurrences of substring *sub*\n   in the range [*start*, *end*].  Optional arguments *start* and\n   *end* are interpreted as in slice notation.\n\nstr.encode(encoding="utf-8", errors="strict")\n\n   Return an encoded version of the string as a bytes object. Default\n   encoding is "\'utf-8\'". *errors* may be given to set a different\n   error handling scheme. The default for *errors* is "\'strict\'",\n   meaning that encoding errors raise a "UnicodeError". Other possible\n   values are "\'ignore\'", "\'replace\'", "\'xmlcharrefreplace\'",\n   "\'backslashreplace\'" and any other name registered via\n   "codecs.register_error()", see section *Codec Base Classes*. For a\n   list of possible encodings, see section *Standard Encodings*.\n\n   Changed in version 3.1: Support for keyword arguments added.\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 suffixes\n   to look for.  With optional *start*, test beginning at that\n   position.  With optional *end*, stop comparing at that position.\n\nstr.expandtabs(tabsize=8)\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.  Tab positions occur every *tabsize* characters\n   (default is 8, giving tab positions at columns 0, 8, 16 and so on).\n   To expand the string, the current column is set to zero and the\n   string is examined character by character.  If the character is a\n   tab ("\\t"), one or more space characters are inserted in the result\n   until the current column is equal to the next tab position. (The\n   tab character itself is not copied.)  If the character is a newline\n   ("\\n") or return ("\\r"), it is copied and the current column is\n   reset to zero.  Any other character is copied unchanged and the\n   current column is incremented by one regardless of how the\n   character is represented when printed.\n\n   >>> \'01\\t012\\t0123\\t01234\'.expandtabs()\n   \'01      012     0123    01234\'\n   >>> \'01\\t012\\t0123\\t01234\'.expandtabs(4)\n   \'01  012 0123    01234\'\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 slice "s[start:end]".\n   Optional arguments *start* and *end* are interpreted as in slice\n   notation.  Return "-1" if *sub* is not found.\n\n   Note: The "find()" method should be used only if you need to know\n     the position of *sub*.  To check if *sub* is a substring or not,\n     use the "in" operator:\n\n        >>> \'Py\' in \'Python\'\n        True\n\nstr.format(*args, **kwargs)\n\n   Perform a string formatting operation.  The string on which this\n   method is called can contain literal text or replacement fields\n   delimited by braces "{}".  Each replacement field contains either\n   the numeric index of a positional argument, or the name of a\n   keyword argument.  Returns a copy of the string where each\n   replacement field is replaced with the string value of the\n   corresponding 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.format_map(mapping)\n\n   Similar to "str.format(**mapping)", except that "mapping" is used\n   directly and not copied to a "dict".  This is useful if for example\n   "mapping" is a dict subclass:\n\n   >>> class Default(dict):\n   ...     def __missing__(self, key):\n   ...         return key\n   ...\n   >>> \'{name} was born in {country}\'.format_map(Default(name=\'Guido\'))\n   \'Guido was born in country\'\n\n   New in version 3.2.\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.  A character "c"\n   is alphanumeric if one of the following returns "True":\n   "c.isalpha()", "c.isdecimal()", "c.isdigit()", or "c.isnumeric()".\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.  Alphabetic\n   characters are those characters defined in the Unicode character\n   database as "Letter", i.e., those with general category property\n   being one of "Lm", "Lt", "Lu", "Ll", or "Lo".  Note that this is\n   different from the "Alphabetic" property defined in the Unicode\n   Standard.\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 are those from general category "Nd". This category\n   includes digit characters, and all characters that can be used to\n   form decimal-radix numbers, e.g. U+0660, ARABIC-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.  Digits include decimal\n   characters and digits that need special handling, such as the\n   compatibility superscript digits.  Formally, a digit is a character\n   that has the property value Numeric_Type=Digit or\n   Numeric_Type=Decimal.\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\n   Use "keyword.iskeyword()" to test for reserved identifiers such as\n   "def" and "class".\n\nstr.islower()\n\n   Return true if all cased characters [4] in the string are lowercase\n   and 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.  Formally, numeric characters are those with the\n   property value Numeric_Type=Digit, Numeric_Type=Decimal or\n   Numeric_Type=Numeric.\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.  Whitespace\n   characters  are those characters defined in the Unicode character\n   database as "Other" or "Separator" and those with bidirectional\n   property being one of "WS", "B", or "S".\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 [4] in the string are uppercase\n   and there is at least one cased character, false otherwise.\n\nstr.join(iterable)\n\n   Return a string which is the concatenation of the strings in the\n   *iterable* *iterable*.  A "TypeError" will be raised if there are\n   any non-string values in *iterable*, including "bytes" objects.\n   The 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 an ASCII\n   space). The original string is returned if *width* is less than or\n   equal to "len(s)".\n\nstr.lower()\n\n   Return a copy of the string with all the cased characters [4]\n   converted to lowercase.\n\n   The lowercasing algorithm used is described in section 3.13 of the\n   Unicode Standard.\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\nstatic str.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]".\n   Optional arguments *start* and *end* are interpreted as in slice\n   notation.  Return "-1" on failure.\n\nstr.rindex(sub[, start[, end]])\n\n   Like "rfind()" but raises "ValueError" when the substring *sub* is\n   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 an ASCII\n   space). The original string is returned if *width* is less than or\n   equal to "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=None, maxsplit=-1)\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 splitting\n   from the right, "rsplit()" behaves like "split()" which is\n   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=None, maxsplit=-1)\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 or "-1", then there is\n   no limit 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* argument\n   may consist of multiple characters (for example,\n   "\'1<>2<>3\'.split(\'<>\')" returns "[\'1\', \'2\', \'3\']"). Splitting an\n   empty string with a specified separator returns "[\'\']".\n\n   For example:\n\n      >>> \'1,2,3\'.split(\',\')\n      [\'1\', \'2\', \'3\']\n      >>> \'1,2,3\'.split(\',\', maxsplit=1)\n      [\'1\', \'2 3\']\n      >>> \'1,2,,3,\'.split(\',\')\n      [\'1\', \'2\', \'\', \'3\', \'\']\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   For example:\n\n      >>> \'1 2 3\'.split()\n      [\'1\', \'2\', \'3\']\n      >>> \'1 2 3\'.split(maxsplit=1)\n      [\'1\', \'2 3\']\n      >>> \'   1   2   3   \'.split()\n      [\'1\', \'2\', \'3\']\n\nstr.splitlines([keepends])\n\n   Return a list of the lines in the string, breaking at line\n   boundaries. This method uses the *universal newlines* approach to\n   splitting lines. Line breaks are not included in the resulting list\n   unless *keepends* is given and true.\n\n   For example:\n\n      >>> \'ab c\\n\\nde fg\\rkl\\r\\n\'.splitlines()\n      [\'ab c\', \'\', \'de fg\', \'kl\']\n      >>> \'ab c\\n\\nde fg\\rkl\\r\\n\'.splitlines(keepends=True)\n      [\'ab c\\n\', \'\\n\', \'de fg\\r\', \'kl\\r\\n\']\n\n   Unlike "split()" when a delimiter string *sep* is given, this\n   method returns an empty list for the empty string, and a terminal\n   line break does not result in an extra line:\n\n      >>> "".splitlines()\n      []\n      >>> "One line\\n".splitlines()\n      [\'One line\']\n\n   For comparison, "split(\'\\n\')" gives:\n\n      >>> \'\'.split(\'\\n\')\n      [\'\']\n      >>> \'Two lines\\n\'.split(\'\\n\')\n      [\'Two lines\', \'\']\n\nstr.startswith(prefix[, start[, end]])\n\n   Return "True" if string starts with the *prefix*, otherwise return\n   "False". *prefix* can also be a tuple of prefixes to look for.\n   With optional *start*, test string beginning at that position.\n   With optional *end*, stop comparing string at that 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 *chars*\n   argument defaults to removing whitespace. The *chars* argument is\n   not a prefix or suffix; rather, all combinations of its values are\n   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. Note that it is not necessarily true that\n   "s.swapcase().swapcase() == s".\n\nstr.title()\n\n   Return a titlecased version of the string where words start with an\n   uppercase character and the remaining characters are lowercase.\n\n   For example:\n\n      >>> \'Hello world\'.title()\n      \'Hello World\'\n\n   The algorithm uses a simple language-independent definition of a\n   word as groups of consecutive letters.  The definition works in\n   many contexts but it means that apostrophes in contractions and\n   possessives form word boundaries, which may not be the desired\n   result:\n\n      >>> "they\'re bill\'s friends from the UK".title()\n      "They\'Re Bill\'S Friends From The Uk"\n\n   A workaround for apostrophes can be constructed using regular\n   expressions:\n\n      >>> import re\n      >>> def titlecase(s):\n      ...     return re.sub(r"[A-Za-z]+(\'[A-Za-z]+)?",\n      ...                   lambda mo: mo.group(0)[0].upper() +\n      ...                              mo.group(0)[1:].lower(),\n      ...                   s)\n      ...\n      >>> titlecase("they\'re bill\'s friends.")\n      "They\'re Bill\'s Friends."\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 ordinals\n   (integers) to Unicode ordinals, strings or "None".  Unmapped\n   characters are left untouched. Characters mapped to "None" are\n   deleted.\n\n   You can use "str.maketrans()" to create a translation map from\n   character-to-character mappings in different formats.\n\n   Note: An even more flexible approach is to create a custom\n     character mapping codec using the "codecs" module (see\n     "encodings.cp1251" for an example).\n\nstr.upper()\n\n   Return a copy of the string with all the cased characters [4]\n   converted to uppercase.  Note that "str.upper().isupper()" might be\n   "False" if "s" contains uncased characters or if the Unicode\n   category of the resulting character(s) is not "Lu" (Letter,\n   uppercase), but e.g. "Lt" (Letter, titlecase).\n\n   The uppercasing algorithm used is described in section 3.13 of the\n   Unicode Standard.\n\nstr.zfill(width)\n\n   Return a copy of the string left filled with ASCII "\'0\'" digits to\n   make a string of length *width*. A leading sign prefix ("\'+\'"/"\'-\'"\n   is handled by inserting the padding *after* the sign character\n   rather than before. The original string is returned if *width* is\n   less than or equal to "len(s)".\n\n   For example:\n\n      >>> "42".zfill(5)\n      \'00042\'\n      >>> "-42".zfill(5)\n      \'-0042\'\n',
+ 'strings': b'\nString and Bytes literals\n*************************\n\nString literals are described by the following lexical definitions:\n\n   stringliteral   ::= [stringprefix](shortstring | longstring)\n   stringprefix    ::= "r" | "u" | "R" | "U"\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" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"\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 "bytesprefix"\nand the rest of the literal. The source character set is defined by\nthe encoding declaration; it is UTF-8 if no encoding declaration is\ngiven in the source file; see section *Encoding declarations*.\n\nIn plain English: Both types of literals can be enclosed in matching\nsingle quotes ("\'") or double quotes (""").  They can also be enclosed\nin matching groups of three single or double quotes (these are\ngenerally 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\nBytes literals are always prefixed with "\'b\'" or "\'B\'"; they produce\nan instance of the "bytes" type instead of the "str" type.  They may\nonly contain ASCII characters; bytes with a numeric value of 128 or\ngreater must be expressed with escapes.\n\nAs of Python 3.3 it is possible again to prefix unicode strings with a\n"u" prefix to simplify maintenance of dual 2.x and 3.x codebases.\n\nBoth string and bytes literals may optionally be prefixed with a\nletter "\'r\'" or "\'R\'"; such strings are called *raw strings* and treat\nbackslashes as literal characters.  As a result, in string literals,\n"\'\\U\'" and "\'\\u\'" escapes in raw strings are not treated specially.\nGiven that Python 2.x\'s raw unicode literals behave differently than\nPython 3.x\'s the "\'ur\'" syntax is not supported.\n\n   New in version 3.3: The "\'rb\'" prefix of raw bytes literals has\n   been added as a synonym of "\'br\'".\n\n   New in version 3.3: Support for the unicode legacy literal\n   ("u\'value\'") was reintroduced to simplify the maintenance of dual\n   Python 2.x and 3.x codebases. See **PEP 414** for more information.\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     | (4)     |\n|                   | Unicode database                  |         |\n+-------------------+-----------------------------------+---------+\n| "\\uxxxx"          | Character with 16-bit hex value   | (5)     |\n|                   | *xxxx*                            |         |\n+-------------------+-----------------------------------+---------+\n| "\\Uxxxxxxxx"      | Character with 32-bit hex value   | (6)     |\n|                   | *xxxxxxxx*                        |         |\n+-------------------+-----------------------------------+---------+\n\nNotes:\n\n1. As in Standard C, up to three octal digits are accepted.\n\n2. Unlike in Standard C, exactly two hex digits are required.\n\n3. In a bytes literal, hexadecimal and octal escapes denote the\n   byte with the given value. In a string literal, these escapes\n   denote a Unicode character with the given value.\n\n4. Changed in version 3.3: Support for name aliases [1] has been\n   added.\n\n5. Individual code units which form parts of a surrogate pair can\n   be encoded using this escape sequence.  Exactly four hex digits are\n   required.\n\n6. Any Unicode character can be encoded this way.  Exactly eight\n   hex digits are required.\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 string\ncannot end in an odd number of backslashes).  Specifically, *a raw\nstring cannot end in a single backslash* (since the backslash would\nescape 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': b'\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\n(lists or dictionaries for example).  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 or a slice (as discussed in the following section).\n\nThe formal syntax makes no special provision for negative indices in\nsequences; however, built-in sequences all provide a "__getitem__()"\nmethod that interprets negative indices by adding the length of the\nsequence to the index (so that "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). Since the support for\nnegative indices and slicing occurs in the object\'s "__getitem__()"\nmethod, subclasses overriding this method will need to explicitly add\nthat support.\n\nA string\'s items are characters.  A character is not a separate data\ntype but a string of exactly one character.\n',
+ 'truth': b'\nTruth Value Testing\n*******************\n\nAny object can be tested for truth value, for use in an "if" or\n"while" condition or as operand of the Boolean operations below. The\nfollowing values are considered false:\n\n* "None"\n\n* "False"\n\n* zero of any numeric type, for example, "0", "0.0", "0j".\n\n* any empty sequence, for example, "\'\'", "()", "[]".\n\n* any empty mapping, for example, "{}".\n\n* instances of user-defined classes, if the class defines a\n  "__bool__()" or "__len__()" method, when that method returns the\n  integer zero or "bool" value "False". [1]\n\nAll other values are considered true --- so objects of many types are\nalways true.\n\nOperations and built-in functions that have a Boolean result always\nreturn "0" or "False" for false and "1" or "True" for true, unless\notherwise stated. (Important exception: the Boolean operations "or"\nand "and" always return one of their operands.)\n',
+ 'try': b'\nThe "try" statement\n*******************\n\nThe "try" statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" identifier]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe "except" clause(s) specify one or more exception handlers. When no\nexception occurs in the "try" clause, no exception handler is\nexecuted. When an exception occurs in the "try" suite, a search for an\nexception handler is started.  This search inspects the except clauses\nin turn until one is found that matches the exception.  An expression-\nless except clause, if present, must be last; it matches any\nexception.  For an except clause with an expression, that expression\nis evaluated, and the clause matches the exception if the resulting\nobject is "compatible" with the exception.  An object is compatible\nwith an exception if it is the class or a base class of the exception\nobject or a tuple containing an item compatible with the exception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire "try" statement raised\nthe exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the "as" keyword in that except clause, if\npresent, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using "as target", it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the "sys" module and can be accessed via\n"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the\nexception class, the exception instance and a traceback object (see\nsection *The standard type hierarchy*) identifying the point in the\nprogram where the exception occurred.  "sys.exc_info()" values are\nrestored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional "else" clause is executed if and when control flows off\nthe end of the "try" clause. [2] Exceptions in the "else" clause are\nnot handled by the preceding "except" clauses.\n\nIf "finally" is present, it specifies a \'cleanup\' handler.  The "try"\nclause is executed, including any "except" and "else" clauses.  If an\nexception occurs in any of the clauses and is not handled, the\nexception is temporarily saved. The "finally" clause is executed.  If\nthere is a saved exception it is re-raised at the end of the "finally"\nclause.  If the "finally" clause raises another exception, the saved\nexception is set as the context of the new exception. If the "finally"\nclause executes a "return" or "break" statement, the saved exception\nis discarded:\n\n   >>> def f():\n   ...     try:\n   ...         1/0\n   ...     finally:\n   ...         return 42\n   ...\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the "finally" clause.\n\nWhen a "return", "break" or "continue" statement is executed in the\n"try" suite of a "try"..."finally" statement, the "finally" clause is\nalso executed \'on the way out.\' A "continue" statement is illegal in\nthe "finally" clause. (The reason is a problem with the current\nimplementation --- this restriction may be lifted in the future).\n\nThe return value of a function is determined by the last "return"\nstatement executed.  Since the "finally" clause always executes, a\n"return" statement executed in the "finally" clause will always be the\nlast one executed:\n\n   >>> def foo():\n   ...     try:\n   ...         return \'try\'\n   ...     finally:\n   ...         return \'finally\'\n   ...\n   >>> foo()\n   \'finally\'\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the "raise" statement to\ngenerate exceptions may be found in section *The raise statement*.\n',
+ 'typesfunctions': b'\nFunctions\n*********\n\nFunction objects are created by function definitions.  The only\noperation on a function object is to call it: "func(argument-list)".\n\nThere are really two flavors of function objects: built-in functions\nand user-defined functions.  Both support the same operation (to call\nthe function), but the implementation is different, hence the\ndifferent object types.\n\nSee *Function definitions* for more information.\n',
+ 'typesmapping': b'\nMapping Types --- "dict"\n************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects.  There is currently only one standard\nmapping type, the *dictionary*.  (For other containers see the built-\nin "list", "set", and "tuple" classes, and the "collections" module.)\n\nA dictionary\'s keys are *almost* arbitrary values.  Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys.  Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as "1" and "1.0") then they can be used interchangeably to index\nthe same dictionary entry.  (Note however, that since computers store\nfloating-point numbers as approximations it is usually unwise to use\nthem as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of "key:\nvalue" pairs within braces, for example: "{\'jack\': 4098, \'sjoerd\':\n4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the "dict"\nconstructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n   Return a new dictionary initialized from an optional positional\n   argument and a possibly empty set of keyword arguments.\n\n   If no positional argument is given, an empty dictionary is created.\n   If a positional argument is given and it is a mapping object, a\n   dictionary is created with the same key-value pairs as the mapping\n   object.  Otherwise, the positional argument must be an *iterable*\n   object.  Each item in the iterable must itself be an iterable with\n   exactly two objects.  The first object of each item becomes a key\n   in the new dictionary, and the second object the corresponding\n   value.  If a key occurs more than once, the last value for that key\n   becomes the corresponding value in the new dictionary.\n\n   If keyword arguments are given, the keyword arguments and their\n   values are added to the dictionary created from the positional\n   argument.  If a key being added is already present, the value from\n   the keyword argument replaces the value from the positional\n   argument.\n\n   To illustrate, the following examples all return a dictionary equal\n   to "{"one": 1, "two": 2, "three": 3}":\n\n      >>> a = dict(one=1, two=2, three=3)\n      >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n      >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n      >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n      >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n      >>> a == b == c == d == e\n      True\n\n   Providing keyword arguments as in the first example only works for\n   keys that are valid Python identifiers.  Otherwise, any valid keys\n   can be used.\n\n   These are the operations that dictionaries support (and therefore,\n   custom mapping types should support too):\n\n   len(d)\n\n      Return the number of items in the dictionary *d*.\n\n   d[key]\n\n      Return the item of *d* with key *key*.  Raises a "KeyError" if\n      *key* is not in the map.\n\n      If a subclass of dict defines a method "__missing__()", if the\n      key *key* is not present, the "d[key]" operation calls that\n      method with the key *key* as argument.  The "d[key]" operation\n      then returns or raises whatever is returned or raised by the\n      "__missing__(key)" call if the key is not present. No other\n      operations or methods invoke "__missing__()". If "__missing__()"\n      is not defined, "KeyError" is raised. "__missing__()" must be a\n      method; it cannot be an instance variable:\n\n         >>> class Counter(dict):\n         ...     def __missing__(self, key):\n         ...         return 0\n         >>> c = Counter()\n         >>> c[\'red\']\n         0\n         >>> c[\'red\'] += 1\n         >>> c[\'red\']\n         1\n\n      See "collections.Counter" for a complete implementation\n      including other methods helpful for accumulating and managing\n      tallies.\n\n   d[key] = value\n\n      Set "d[key]" to *value*.\n\n   del d[key]\n\n      Remove "d[key]" from *d*.  Raises a "KeyError" if *key* is not\n      in the map.\n\n   key in d\n\n      Return "True" if *d* has a key *key*, else "False".\n\n   key not in d\n\n      Equivalent to "not key in d".\n\n   iter(d)\n\n      Return an iterator over the keys of the dictionary.  This is a\n      shortcut for "iter(d.keys())".\n\n   clear()\n\n      Remove all items from the dictionary.\n\n   copy()\n\n      Return a shallow copy of the dictionary.\n\n   classmethod fromkeys(seq[, value])\n\n      Create a new dictionary with keys from *seq* and values set to\n      *value*.\n\n      "fromkeys()" is a class method that returns a new dictionary.\n      *value* defaults to "None".\n\n   get(key[, default])\n\n      Return the value for *key* if *key* is in the dictionary, else\n      *default*. If *default* is not given, it defaults to "None", so\n      that this method never raises a "KeyError".\n\n   items()\n\n      Return a new view of the dictionary\'s items ("(key, value)"\n      pairs). See the *documentation of view objects*.\n\n   keys()\n\n      Return a new view of the dictionary\'s keys.  See the\n      *documentation of view objects*.\n\n   pop(key[, default])\n\n      If *key* is in the dictionary, remove it and return its value,\n      else return *default*.  If *default* is not given and *key* is\n      not in the dictionary, a "KeyError" is raised.\n\n   popitem()\n\n      Remove and return an arbitrary "(key, value)" pair from the\n      dictionary.\n\n      "popitem()" is useful to destructively iterate over a\n      dictionary, as often used in set algorithms.  If the dictionary\n      is empty, calling "popitem()" raises a "KeyError".\n\n   setdefault(key[, default])\n\n      If *key* is in the dictionary, return its value.  If not, insert\n      *key* with a value of *default* and return *default*.  *default*\n      defaults to "None".\n\n   update([other])\n\n      Update the dictionary with the key/value pairs from *other*,\n      overwriting existing keys.  Return "None".\n\n      "update()" accepts either another dictionary object or an\n      iterable of key/value pairs (as tuples or other iterables of\n      length two).  If keyword arguments are specified, the dictionary\n      is then updated with those key/value pairs: "d.update(red=1,\n      blue=2)".\n\n   values()\n\n      Return a new view of the dictionary\'s values.  See the\n      *documentation of view objects*.\n\nSee also: "types.MappingProxyType" can be used to create a read-only\n  view of a "dict".\n\n\nDictionary view objects\n=======================\n\nThe objects returned by "dict.keys()", "dict.values()" and\n"dict.items()" are *view objects*.  They provide a dynamic view on the\ndictionary\'s entries, which means that when the dictionary changes,\nthe view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n   Return the number of entries in the dictionary.\n\niter(dictview)\n\n   Return an iterator over the keys, values or items (represented as\n   tuples of "(key, value)") in the dictionary.\n\n   Keys and values are iterated over in an arbitrary order which is\n   non-random, varies across Python implementations, and depends on\n   the dictionary\'s history of insertions and deletions. If keys,\n   values and items views are iterated over with no intervening\n   modifications to the dictionary, the order of items will directly\n   correspond.  This allows the creation of "(value, key)" pairs using\n   "zip()": "pairs = zip(d.values(), d.keys())".  Another way to\n   create the same list is "pairs = [(v, k) for (k, v) in d.items()]".\n\n   Iterating views while adding or deleting entries in the dictionary\n   may raise a "RuntimeError" or fail to iterate over all entries.\n\nx in dictview\n\n   Return "True" if *x* is in the underlying dictionary\'s keys, values\n   or items (in the latter case, *x* should be a "(key, value)"\n   tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that "(key, value)" pairs are unique\nand hashable, then the items view is also set-like.  (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class "collections.abc.Set" are available (for example, "==",\n"<", or "^").\n\nAn example of dictionary view usage:\n\n   >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n   >>> keys = dishes.keys()\n   >>> values = dishes.values()\n\n   >>> # iteration\n   >>> n = 0\n   >>> for val in values:\n   ...     n += val\n   >>> print(n)\n   504\n\n   >>> # keys and values are iterated over in the same order\n   >>> list(keys)\n   [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n   >>> list(values)\n   [2, 1, 1, 500]\n\n   >>> # view objects are dynamic and reflect dict changes\n   >>> del dishes[\'eggs\']\n   >>> del dishes[\'sausage\']\n   >>> list(keys)\n   [\'spam\', \'bacon\']\n\n   >>> # set operations\n   >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n   {\'bacon\'}\n   >>> keys ^ {\'sausage\', \'juice\'}\n   {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n',
+ 'typesmethods': b'\nMethods\n*******\n\nMethods are functions that are called using the attribute notation.\nThere are two flavors: built-in methods (such as "append()" on lists)\nand class instance methods.  Built-in methods are described with the\ntypes that support them.\n\nIf you access a method (a function defined in a class namespace)\nthrough an instance, you get a special object: a *bound method* (also\ncalled *instance method*) object. When called, it will add the "self"\nargument to the argument list.  Bound methods have two special read-\nonly attributes: "m.__self__" is the object on which the method\noperates, and "m.__func__" is the function implementing the method.\nCalling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to\ncalling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".\n\nLike function objects, bound method objects support getting arbitrary\nattributes.  However, since method attributes are actually stored on\nthe underlying function object ("meth.__func__"), setting method\nattributes on bound methods is disallowed.  Attempting to set an\nattribute on a method results in an "AttributeError" being raised.  In\norder to set a method attribute, you need to explicitly set it on the\nunderlying function object:\n\n   >>> class C:\n   ...     def method(self):\n   ...         pass\n   ...\n   >>> c = C()\n   >>> c.method.whoami = \'my name is method\'  # can\'t set on the method\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   AttributeError: \'method\' object has no attribute \'whoami\'\n   >>> c.method.__func__.whoami = \'my name is method\'\n   >>> c.method.whoami\n   \'my name is method\'\n\nSee *The standard type hierarchy* for more information.\n',
+ 'typesmodules': b'\nModules\n*******\n\nThe only special operation on a module is attribute access: "m.name",\nwhere *m* is a module and *name* accesses a name defined in *m*\'s\nsymbol table. Module attributes can be assigned to.  (Note that the\n"import" statement is not, strictly speaking, an operation on a module\nobject; "import foo" does not require a module object named *foo* to\nexist, rather it requires an (external) *definition* for a module\nnamed *foo* somewhere.)\n\nA special attribute of every module is "__dict__". This is the\ndictionary containing the module\'s symbol table. Modifying this\ndictionary will actually change the module\'s symbol table, but direct\nassignment to the "__dict__" attribute is not possible (you can write\n"m.__dict__[\'a\'] = 1", which defines "m.a" to be "1", but you can\'t\nwrite "m.__dict__ = {}").  Modifying "__dict__" directly is not\nrecommended.\n\nModules built into the interpreter are written like this: "<module\n\'sys\' (built-in)>".  If loaded from a file, they are written as\n"<module \'os\' from \'/usr/local/lib/pythonX.Y/os.pyc\'>".\n',