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 # -*- coding: utf-8 -*-
-# Autogenerated by Sphinx on Sat Jul  4 19:11:05 2015
+# Autogenerated by Sphinx on Sat Jul 25 14:16:44 2015
 topics = {'assert': u'\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',
  'assignment': u'\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n   assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n   target_list     ::= target ("," target)* [","]\n   target          ::= identifier\n              | "(" target_list ")"\n              | "[" target_list "]"\n              | attributeref\n              | subscription\n              | slicing\n              | "*" target\n\n(See section *Primaries* for the syntax definitions for\n*attributeref*, *subscription*, and *slicing*.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable.  The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section *The standard type\nhierarchy*).\n\nAssignment of an object to a target list, optionally enclosed in\nparentheses or square brackets, is recursively defined as follows.\n\n* If the target list is a single target: The object is assigned to\n  that target.\n\n* If the target list is a comma-separated list of targets: The\n  object must be an iterable with the same number of items as there\n  are targets in the target list, and the items are assigned, from\n  left to right, to the corresponding targets.\n\n  * If the target list contains one target prefixed with an\n    asterisk, called a "starred" target: The object must be a sequence\n    with at least as many items as there are targets in the target\n    list, minus one.  The first items of the sequence are assigned,\n    from left to right, to the targets before the starred target.  The\n    final items of the sequence are assigned to the targets after the\n    starred target.  A list of the remaining items in the sequence is\n    then assigned to the starred target (the list can be empty).\n\n  * Else: The object must be a sequence with the same number of\n    items as there are targets in the target list, and the items are\n    assigned, from left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n  * If the name does not occur in a "global" or "nonlocal" statement\n    in the current code block: the name is bound to the object in the\n    current local namespace.\n\n  * Otherwise: the name is bound to the object in the global\n    namespace or the outer namespace determined by "nonlocal",\n    respectively.\n\n  The name is rebound if it was already bound.  This may cause the\n  reference count for the object previously bound to the name to reach\n  zero, causing the object to be deallocated and its destructor (if it\n  has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in\n  square brackets: The object must be an iterable with the same number\n  of items as there are targets in the target list, and its items are\n  assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n  the reference is evaluated.  It should yield an object with\n  assignable attributes; if this is not the case, "TypeError" is\n  raised.  That object is then asked to assign the assigned object to\n  the given attribute; if it cannot perform the assignment, it raises\n  an exception (usually but not necessarily "AttributeError").\n\n  Note: If the object is a class instance and the attribute reference\n  occurs on both sides of the assignment operator, the RHS expression,\n  "a.x" can access either an instance attribute or (if no instance\n  attribute exists) a class attribute.  The LHS target "a.x" is always\n  set as an instance attribute, creating it if necessary.  Thus, the\n  two occurrences of "a.x" do not necessarily refer to the same\n  attribute: if the RHS expression refers to a class attribute, the\n  LHS creates a new instance attribute as the target of the\n  assignment:\n\n     class Cls:\n         x = 3             # class variable\n     inst = Cls()\n     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3\n\n  This description does not necessarily apply to descriptor\n  attributes, such as properties created with "property()".\n\n* If the target is a subscription: The primary expression in the\n  reference is evaluated.  It should yield either a mutable sequence\n  object (such as a list) or a mapping object (such as a dictionary).\n  Next, the subscript expression is evaluated.\n\n  If the primary is a mutable sequence object (such as a list), the\n  subscript must yield an integer.  If it is negative, the sequence\'s\n  length is added to it.  The resulting value must be a nonnegative\n  integer less than the sequence\'s length, and the sequence is asked\n  to assign the assigned object to its item with that index.  If the\n  index is out of range, "IndexError" is raised (assignment to a\n  subscripted sequence cannot add new items to a list).\n\n  If the primary is a mapping object (such as a dictionary), the\n  subscript must have a type compatible with the mapping\'s key type,\n  and the mapping is then asked to create a key/datum pair which maps\n  the subscript to the assigned object.  This can either replace an\n  existing key/value pair with the same key value, or insert a new\n  key/value pair (if no key with the same value existed).\n\n  For user-defined objects, the "__setitem__()" method is called with\n  appropriate arguments.\n\n* If the target is a slicing: The primary expression in the\n  reference is evaluated.  It should yield a mutable sequence object\n  (such as a list).  The assigned object should be a sequence object\n  of the same type.  Next, the lower and upper bound expressions are\n  evaluated, insofar they are present; defaults are zero and the\n  sequence\'s length.  The bounds should evaluate to integers. If\n  either bound is negative, the sequence\'s length is added to it.  The\n  resulting bounds are clipped to lie between zero and the sequence\'s\n  length, inclusive.  Finally, the sequence object is asked to replace\n  the slice with the items of the assigned sequence.  The length of\n  the slice may be different from the length of the assigned sequence,\n  thus changing the length of the target sequence, if the target\n  sequence allows it.\n\n**CPython implementation detail:** In the current implementation, the\nsyntax for targets is taken to be the same as for expressions, and\ninvalid syntax is rejected during the code generation phase, causing\nless detailed error messages.\n\nAlthough the definition of assignment implies that overlaps between\nthe left-hand side and the right-hand side are \'simultanenous\' (for\nexample "a, b = b, a" swaps two variables), overlaps *within* the\ncollection of assigned-to variables occur left-to-right, sometimes\nresulting in confusion.  For instance, the following program prints\n"[0, 2]":\n\n   x = [0, 1]\n   i = 0\n   i, x[i] = 1, 2         # i is updated, then x[i] is updated\n   print(x)\n\nSee also: **PEP 3132** - Extended Iterable Unpacking\n\n     The specification for the "*target" feature.\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n   augmented_assignment_stmt ::= 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',
  'atom-identifiers': u'\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',
@@ -19,7 +19,7 @@ topics = {'assert': u'\nThe "assert" statement\n**********************\n\nAssert
  'calls': u'\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': u'\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',
  'comparisons': u'\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. They\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',
- 'compound': u'\nCompound statements\n*******************\n\nCompound statements contain (groups of) other statements; they affect\nor control the execution of those other statements in some way.  In\ngeneral, compound statements span multiple lines, although in simple\nincarnations a whole compound statement may be contained in one line.\n\nThe "if", "while" and "for" statements implement traditional control\nflow constructs.  "try" specifies exception handlers and/or cleanup\ncode for a group of statements, while the "with" statement allows the\nexecution of initialization and finalization code around a block of\ncode.  Function and class definitions are also syntactically compound\nstatements.\n\nA compound statement consists of one or more \'clauses.\'  A clause\nconsists of a header and a \'suite.\'  The clause headers of a\nparticular compound statement are all at the same indentation level.\nEach clause header begins with a uniquely identifying keyword and ends\nwith a colon.  A suite is a group of statements controlled by a\nclause.  A suite can be one or more semicolon-separated simple\nstatements on the same line as the header, following the header\'s\ncolon, or it can be one or more indented statements on subsequent\nlines.  Only the latter form of a suite can contain nested compound\nstatements; the following is illegal, mostly because it wouldn\'t be\nclear to which "if" clause a following "else" clause would belong:\n\n   if test1: if test2: print(x)\n\nAlso note that the semicolon binds tighter than the colon in this\ncontext, so that in the following example, either all or none of the\n"print()" calls are executed:\n\n   if x < y < z: print(x); print(y); print(z)\n\nSummarizing:\n\n   compound_stmt ::= if_stmt\n                     | while_stmt\n                     | for_stmt\n                     | try_stmt\n                     | with_stmt\n                     | funcdef\n                     | classdef\n                     | async_with_stmt\n                     | async_for_stmt\n                     | async_funcdef\n   suite         ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT\n   statement     ::= stmt_list NEWLINE | compound_stmt\n   stmt_list     ::= simple_stmt (";" simple_stmt)* [";"]\n\nNote that statements always end in a "NEWLINE" possibly followed by a\n"DEDENT".  Also note that optional continuation clauses always begin\nwith a keyword that cannot start a statement, thus there are no\nambiguities (the \'dangling "else"\' problem is solved in Python by\nrequiring nested "if" statements to be indented).\n\nThe formatting of the grammar rules in the following sections places\neach clause on a separate line for clarity.\n\n\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\n\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\n\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\n\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\n\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")\n   is 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\n   value from "__enter__()" is assigned to it.\n\n   Note: The "with" statement guarantees that if the "__enter__()"\n     method 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: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n\n\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\n\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\nCoroutines\n==========\n\nNew in version 3.5.\n\n\nCoroutine function definition\n-----------------------------\n\n   async_funcdef ::= "async" funcdef\n\nExecution of Python coroutines can be suspended and resumed at many\npoints (see *coroutine*).  In the body of a coroutine, any "await" and\n"async" identifiers become reserved keywords; "await" expressions,\n"async for" and "async with" can only be used in coroutine bodies.\nHowever, to simplify the parser, these keywords cannot be used on the\nsame line as a function or coroutine ("def" statement) header.\n\nFunctions defined with "async def" syntax are always coroutine\nfunctions, even if they do not contain "await" or "async" keywords.\n\nIt is a "SyntaxError" to use "yield" expressions in "async def"\ncoroutines.\n\nAn example of a coroutine function:\n\n   async def func(param1, param2):\n       do_stuff()\n       await some_coroutine()\n\n\nThe "async for" statement\n-------------------------\n\n   async_for_stmt ::= "async" for_stmt\n\nAn *asynchronous iterable* is able to call asynchronous code in its\n*iter* implementation, and *asynchronous iterator* can call\nasynchronous code in its *next* method.\n\nThe "async for" statement allows convenient iteration over\nasynchronous iterators.\n\nThe following code:\n\n   async for TARGET in ITER:\n       BLOCK\n   else:\n       BLOCK2\n\nIs semantically equivalent to:\n\n   iter = (ITER)\n   iter = await type(iter).__aiter__(iter)\n   running = True\n   while running:\n       try:\n           TARGET = await type(iter).__anext__(iter)\n       except StopAsyncIteration:\n           running = False\n       else:\n           BLOCK\n   else:\n       BLOCK2\n\nSee also "__aiter__()" and "__anext__()" for details.\n\nIt is a "SyntaxError" to use "async for" statement outside of an\n"async def" function.\n\n\nThe "async with" statement\n--------------------------\n\n   async_with_stmt ::= "async" with_stmt\n\nAn *asynchronous context manager* is a *context manager* that is able\nto suspend execution in its *enter* and *exit* methods.\n\nThe following code:\n\n   async with EXPR as VAR:\n       BLOCK\n\nIs semantically equivalent to:\n\n   mgr = (EXPR)\n   aexit = type(mgr).__aexit__\n   aenter = type(mgr).__aenter__(mgr)\n   exc = True\n\n   VAR = await aenter\n   try:\n       BLOCK\n   except:\n       if not await aexit(mgr, *sys.exc_info()):\n           raise\n   else:\n       await aexit(mgr, None, None, None)\n\nSee also "__aenter__()" and "__aexit__()" for details.\n\nIt is a "SyntaxError" to use "async with" statement outside of an\n"async def" function.\n\nSee also: **PEP 492** - Coroutines with async and await syntax\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',
+ 'compound': u'\nCompound statements\n*******************\n\nCompound statements contain (groups of) other statements; they affect\nor control the execution of those other statements in some way.  In\ngeneral, compound statements span multiple lines, although in simple\nincarnations a whole compound statement may be contained in one line.\n\nThe "if", "while" and "for" statements implement traditional control\nflow constructs.  "try" specifies exception handlers and/or cleanup\ncode for a group of statements, while the "with" statement allows the\nexecution of initialization and finalization code around a block of\ncode.  Function and class definitions are also syntactically compound\nstatements.\n\nA compound statement consists of one or more \'clauses.\'  A clause\nconsists of a header and a \'suite.\'  The clause headers of a\nparticular compound statement are all at the same indentation level.\nEach clause header begins with a uniquely identifying keyword and ends\nwith a colon.  A suite is a group of statements controlled by a\nclause.  A suite can be one or more semicolon-separated simple\nstatements on the same line as the header, following the header\'s\ncolon, or it can be one or more indented statements on subsequent\nlines.  Only the latter form of a suite can contain nested compound\nstatements; the following is illegal, mostly because it wouldn\'t be\nclear to which "if" clause a following "else" clause would belong:\n\n   if test1: if test2: print(x)\n\nAlso note that the semicolon binds tighter than the colon in this\ncontext, so that in the following example, either all or none of the\n"print()" calls are executed:\n\n   if x < y < z: print(x); print(y); print(z)\n\nSummarizing:\n\n   compound_stmt ::= if_stmt\n                     | while_stmt\n                     | for_stmt\n                     | try_stmt\n                     | with_stmt\n                     | funcdef\n                     | classdef\n                     | async_with_stmt\n                     | async_for_stmt\n                     | async_funcdef\n   suite         ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT\n   statement     ::= stmt_list NEWLINE | compound_stmt\n   stmt_list     ::= simple_stmt (";" simple_stmt)* [";"]\n\nNote that statements always end in a "NEWLINE" possibly followed by a\n"DEDENT".  Also note that optional continuation clauses always begin\nwith a keyword that cannot start a statement, thus there are no\nambiguities (the \'dangling "else"\' problem is solved in Python by\nrequiring nested "if" statements to be indented).\n\nThe formatting of the grammar rules in the following sections places\neach clause on a separate line for clarity.\n\n\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\n\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\n\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\n\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\n\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")\n   is 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\n   value from "__enter__()" is assigned to it.\n\n   Note: The "with" statement guarantees that if the "__enter__()"\n     method 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: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n\n\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\n\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\nCoroutines\n==========\n\nNew in version 3.5.\n\n\nCoroutine function definition\n-----------------------------\n\n   async_funcdef ::= "async" funcdef\n\nExecution of Python coroutines can be suspended and resumed at many\npoints (see *coroutine*).  In the body of a coroutine, any "await" and\n"async" identifiers become reserved keywords; "await" expressions,\n"async for" and "async with" can only be used in coroutine bodies.\n\nFunctions defined with "async def" syntax are always coroutine\nfunctions, even if they do not contain "await" or "async" keywords.\n\nIt is a "SyntaxError" to use "yield" expressions in "async def"\ncoroutines.\n\nAn example of a coroutine function:\n\n   async def func(param1, param2):\n       do_stuff()\n       await some_coroutine()\n\n\nThe "async for" statement\n-------------------------\n\n   async_for_stmt ::= "async" for_stmt\n\nAn *asynchronous iterable* is able to call asynchronous code in its\n*iter* implementation, and *asynchronous iterator* can call\nasynchronous code in its *next* method.\n\nThe "async for" statement allows convenient iteration over\nasynchronous iterators.\n\nThe following code:\n\n   async for TARGET in ITER:\n       BLOCK\n   else:\n       BLOCK2\n\nIs semantically equivalent to:\n\n   iter = (ITER)\n   iter = await type(iter).__aiter__(iter)\n   running = True\n   while running:\n       try:\n           TARGET = await type(iter).__anext__(iter)\n       except StopAsyncIteration:\n           running = False\n       else:\n           BLOCK\n   else:\n       BLOCK2\n\nSee also "__aiter__()" and "__anext__()" for details.\n\nIt is a "SyntaxError" to use "async for" statement outside of an\n"async def" function.\n\n\nThe "async with" statement\n--------------------------\n\n   async_with_stmt ::= "async" with_stmt\n\nAn *asynchronous context manager* is a *context manager* that is able\nto suspend execution in its *enter* and *exit* methods.\n\nThe following code:\n\n   async with EXPR as VAR:\n       BLOCK\n\nIs semantically equivalent to:\n\n   mgr = (EXPR)\n   aexit = type(mgr).__aexit__\n   aenter = type(mgr).__aenter__(mgr)\n   exc = True\n\n   VAR = await aenter\n   try:\n       BLOCK\n   except:\n       if not await aexit(mgr, *sys.exc_info()):\n           raise\n   else:\n       await aexit(mgr, None, None, None)\n\nSee also "__aenter__()" and "__aexit__()" for details.\n\nIt is a "SyntaxError" to use "async with" statement outside of an\n"async def" function.\n\nSee also: **PEP 492** - Coroutines with async and await syntax\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',
  'context-managers': u'\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': u'\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': u'\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',
@@ -51,7 +51,7 @@ topics = {'assert': u'\nThe "assert" statement\n**********************\n\nAssert
  'numbers': u'\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': u'\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.__matmul__(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()",\n   "pow()", "**", "<<", ">>", "&", "^", "|").  For instance, to\n   evaluate the expression "x + y", where *x* is an instance of a\n   class that has an "__add__()" method, "x.__add__(y)" is called.\n   The "__divmod__()" method should be the equivalent to using\n   "__floordiv__()" and "__mod__()"; it should not be related to\n   "__truediv__()".  Note that "__pow__()" should be defined to accept\n   an optional third argument if the ternary version of the built-in\n   "pow()" function is to be supported.\n\n   If one of those methods does not support the operation with the\n   supplied arguments, it should return "NotImplemented".\n\nobject.__radd__(self, other)\nobject.__rsub__(self, other)\nobject.__rmul__(self, other)\nobject.__rmatmul__(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()",\n   "pow()", "**", "<<", ">>", "&", "^", "|") with reflected (swapped)\n   operands.  These functions are only called if the left operand does\n   not 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.__imatmul__(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\n   do the operation in-place (modifying *self*) and return the result\n   (which could be, but does not have to be, *self*).  If a specific\n   method is not defined, the augmented assignment falls back to the\n   normal methods.  For instance, if *x* is an instance of a class\n   with an "__iadd__()" method, "x += y" is equivalent to "x =\n   x.__iadd__(y)" . Otherwise, "x.__add__(y)" and "y.__radd__(x)" are\n   considered, as with the evaluation of "x + y". In certain\n   situations, augmented assignment can result in unexpected errors\n   (see *Why does a_tuple[i] += [\'item\'] raise an exception when the\n   addition works?*), but this behavior is in fact part of the data\n   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': u'\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': u'\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, matrix multiplication |\n|                                                 | division, remainder [5]               |\n+-------------------------------------------------+---------------------------------------+\n| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |\n+-------------------------------------------------+---------------------------------------+\n| "**"                                            | Exponentiation [6]                    |\n+-------------------------------------------------+---------------------------------------+\n| "await" "x"                                     | Await expression                      |\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',
+ 'operator-summary': u'\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, matrix multiplication |\n|                                                 | division, remainder [5]               |\n+-------------------------------------------------+---------------------------------------+\n| "+x", "-x", "~x"                                | Positive, negative, bitwise NOT       |\n+-------------------------------------------------+---------------------------------------+\n| "**"                                            | Exponentiation [6]                    |\n+-------------------------------------------------+---------------------------------------+\n| "await" "x"                                     | Await expression                      |\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 ""\\u0043\\u0327"" 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': u'\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': u'\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 ::= await ["**" 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': u'\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 or a "finally" clause: the previous exception is\nthen attached as the new 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',