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✍️ Editing: topics.cpython-35.opt-1.pyc

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            Last Modified: 2024-05-31 20:51:46        

��Yf��	������������������@���s���d��d�d�d�d�d�d�d�d�d	�d
�d�d�d
�d�d�d�d�d�d�d�d�d�d�d�d�d�d�d�d�d�d�d �d!�d"�d#�d$�d%�d&�d'�d(�d)�d*�d+�d,�d-�d.�d/�d0�d1�d2�d3�d4�d5�d6�d7�d8�d9�d:�d;�d<�d=�d>�d?�d@�dA�dB�dC�dD�dE�dF�dG�dH�dI�dJ�dK�dL�dM�dN�d9�dO�dP�dQ�dR�dS�dT�dU�dV�dW�dX�dY�dZ�d[�d\�d]�d^�d_�d`�da�db�dc�dd�de�df�dg�dh�di�dj�dk�dl�dm�dn�do�dp�dq�dr�ds�dt�du�dv�dw�dx�dy�dz�d{�d|�d}�d~�d�d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��d��iM�Z��d��S)��assertas��The "assert" statement
**********************

Assert statements are a convenient way to insert debugging assertions
into a program:

assert_stmt ::= "assert" expression ["," expression]

The simple form, "assert expression", is equivalent to

if __debug__:
       if not expression: raise AssertionError

The extended form, "assert expression1, expression2", is equivalent to

if __debug__:
       if not expression1: raise AssertionError(expression2)

These equivalences assume that "__debug__" and "AssertionError" refer
to the built-in variables with those names.  In the current
implementation, the built-in variable "__debug__" is "True" under
normal circumstances, "False" when optimization is requested (command
line option -O).  The current code generator emits no code for an
assert statement when optimization is requested at compile time.  Note
that it is unnecessary to include the source code for the expression
that failed in the error message; it will be displayed as part of the
stack trace.

Assignments to "__debug__" are illegal.  The value for the built-in
variable is determined when the interpreter starts.
Z
assignmentu�$��Assignment statements
*********************

Assignment statements are used to (re)bind names to values and to
modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for *attributeref*,
*subscription*, and *slicing*.)

An assignment statement evaluates the expression list (remember that
this can be a single expression or a comma-separated list, the latter
yielding a tuple) and assigns the single resulting object to each of
the target lists, from left to right.

Assignment is defined recursively depending on the form of the target
(list). When a target is part of a mutable object (an attribute
reference, subscription or slicing), the mutable object must
ultimately perform the assignment and decide about its validity, and
may raise an exception if the assignment is unacceptable.  The rules
observed by various types and the exceptions raised are given with the
definition of the object types (see section The standard type
hierarchy).

Assignment of an object to a target list, optionally enclosed in
parentheses or square brackets, is recursively defined as follows.

* If the target list is empty: The object must also be an empty
  iterable.

* If the target list is a single target in parentheses: The object
  is assigned to that target.

* If the target list is a comma-separated list of targets, or a
  single target in square brackets: The object must be an iterable
  with the same number of items as there are targets in the target
  list, and the items are assigned, from left to right, to the
  corresponding targets.

* If the target list contains one target prefixed with an
    asterisk, called a “starred” target: The object must be an
    iterable with at least as many items as there are targets in the
    target list, minus one.  The first items of the iterable are
    assigned, from left to right, to the targets before the starred
    target.  The final items of the iterable are assigned to the
    targets after the starred target.  A list of the remaining items
    in the iterable is then assigned to the starred target (the list
    can be empty).

* Else: The object must be an iterable with the same number of
    items as there are targets in the target list, and the items are
    assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as
follows.

* If the target is an identifier (name):

* If the name does not occur in a "global" or "nonlocal" statement
    in the current code block: the name is bound to the object in the
    current local namespace.

* Otherwise: the name is bound to the object in the global
    namespace or the outer namespace determined by "nonlocal",
    respectively.

The name is rebound if it was already bound.  This may cause the
  reference count for the object previously bound to the name to reach
  zero, causing the object to be deallocated and its destructor (if it
  has one) to be called.

* If the target is an attribute reference: The primary expression in
  the reference is evaluated.  It should yield an object with
  assignable attributes; if this is not the case, "TypeError" is
  raised.  That object is then asked to assign the assigned object to
  the given attribute; if it cannot perform the assignment, it raises
  an exception (usually but not necessarily "AttributeError").

Note: If the object is a class instance and the attribute reference
  occurs on both sides of the assignment operator, the RHS expression,
  "a.x" can access either an instance attribute or (if no instance
  attribute exists) a class attribute.  The LHS target "a.x" is always
  set as an instance attribute, creating it if necessary.  Thus, the
  two occurrences of "a.x" do not necessarily refer to the same
  attribute: if the RHS expression refers to a class attribute, the
  LHS creates a new instance attribute as the target of the
  assignment:

class Cls:
         x = 3             # class variable
     inst = Cls()
     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3

This description does not necessarily apply to descriptor
  attributes, such as properties created with "property()".

* If the target is a subscription: The primary expression in the
  reference is evaluated.  It should yield either a mutable sequence
  object (such as a list) or a mapping object (such as a dictionary).
  Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the
  subscript must yield an integer.  If it is negative, the sequence’s
  length is added to it.  The resulting value must be a nonnegative
  integer less than the sequence’s length, and the sequence is asked
  to assign the assigned object to its item with that index.  If the
  index is out of range, "IndexError" is raised (assignment to a
  subscripted sequence cannot add new items to a list).

If the primary is a mapping object (such as a dictionary), the
  subscript must have a type compatible with the mapping’s key type,
  and the mapping is then asked to create a key/datum pair which maps
  the subscript to the assigned object.  This can either replace an
  existing key/value pair with the same key value, or insert a new
  key/value pair (if no key with the same value existed).

For user-defined objects, the "__setitem__()" method is called with
  appropriate arguments.

* If the target is a slicing: The primary expression in the
  reference is evaluated.  It should yield a mutable sequence object
  (such as a list).  The assigned object should be a sequence object
  of the same type.  Next, the lower and upper bound expressions are
  evaluated, insofar they are present; defaults are zero and the
  sequence’s length.  The bounds should evaluate to integers. If
  either bound is negative, the sequence’s length is added to it.  The
  resulting bounds are clipped to lie between zero and the sequence’s
  length, inclusive.  Finally, the sequence object is asked to replace
  the slice with the items of the assigned sequence.  The length of
  the slice may be different from the length of the assigned sequence,
  thus changing the length of the target sequence, if the target
  sequence allows it.

**CPython implementation detail:** In the current implementation, the
syntax for targets is taken to be the same as for expressions, and
invalid syntax is rejected during the code generation phase, causing
less detailed error messages.

Although the definition of assignment implies that overlaps between
the left-hand side and the right-hand side are ‘simultaneous’ (for
example "a, b = b, a" swaps two variables), overlaps *within* the
collection of assigned-to variables occur left-to-right, sometimes
resulting in confusion.  For instance, the following program prints
"[0, 2]":

x = [0, 1]
   i = 0
   i, x[i] = 1, 2         # i is updated, then x[i] is updated
   print(x)

See also:

**PEP 3132** - Extended Iterable Unpacking
     The specification for the "*target" feature.

Augmented assignment statements
===============================

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
             | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions of the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side.  For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
zatom-identifiersa���Identifiers (Names)
*******************

An identifier occurring as an atom is a name.  See section Identifiers
and keywords for lexical definition and section Naming and binding for
documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields
that object. When a name is not bound, an attempt to evaluate it
raises a "NameError" exception.

**Private name mangling:** When an identifier that textually occurs in
a class definition begins with two or more underscore characters and
does not end in two or more underscores, it is considered a *private
name* of that class. Private names are transformed to a longer form
before code is generated for them.  The transformation inserts the
class name, with leading underscores removed and a single underscore
inserted, in front of the name.  For example, the identifier "__spam"
occurring in a class named "Ham" will be transformed to "_Ham__spam".
This transformation is independent of the syntactical context in which
the identifier is used.  If the transformed name is extremely long
(longer than 255 characters), implementation defined truncation may
happen. If the class name consists only of underscores, no
transformation is done.
z
atom-literalsu
��Literals
********

Python supports string and bytes literals and various numeric
literals:

literal ::= stringliteral | bytesliteral
               | integer | floatnumber | imagnumber

Evaluation of a literal yields an object of the given type (string,
bytes, integer, floating point number, complex number) with the given
value.  The value may be approximated in the case of floating point
and imaginary (complex) literals.  See section Literals for details.

All literals correspond to immutable data types, and hence the
object’s identity is less important than its value.  Multiple
evaluations of literals with the same value (either the same
occurrence in the program text or a different occurrence) may obtain
the same object or a different object with the same value.
zattribute-accessuX(��Customizing attribute access
****************************

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

object.__getattr__(self, name)

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

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

object.__getattribute__(self, name)

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

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

object.__setattr__(self, name, value)

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

If "__setattr__()" wants to assign to an instance attribute, it
   should call the base class method with the same name, for example,
   "object.__setattr__(self, name, value)".

object.__delattr__(self, name)

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

object.__dir__(self)

Called when "dir()" is called on the object. A sequence must be
   returned. "dir()" converts the returned sequence to a list and
   sorts it.

Implementing Descriptors
========================

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

object.__get__(self, instance, owner)

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

object.__set__(self, instance, value)

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

object.__delete__(self, instance)

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

The attribute "__objclass__" is interpreted by the "inspect" module as
specifying the class where this object was defined (setting this
appropriately can assist in runtime introspection of dynamic class
attributes). For callables, it may indicate that an instance of the
given type (or a subclass) is expected or required as the first
positional argument (for example, CPython sets this attribute for
unbound methods that are implemented in C).

Invoking Descriptors
====================

In general, a descriptor is an object attribute with “binding
behavior”, one whose attribute access has been overridden by methods
in the descriptor protocol:  "__get__()", "__set__()", and
"__delete__()". If any of those methods are defined for an object, it
is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete
the attribute from an object’s dictionary. For instance, "a.x" has a
lookup chain starting with "a.__dict__['x']", then
"type(a).__dict__['x']", and continuing through the base classes of
"type(a)" excluding metaclasses.

However, if the looked-up value is an object defining one of the
descriptor methods, then Python may override the default behavior and
invoke the descriptor method instead.  Where this occurs in the
precedence chain depends on which descriptor methods were defined and
how they were called.

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

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

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

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

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

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

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

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

__slots__
=========

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

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

object.__slots__

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

Notes on using *__slots__*
--------------------------

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

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

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

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

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

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

* Nonempty *__slots__* does not work for classes derived from
  “variable-length” built-in types such as "int", "bytes" and "tuple".

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

* *__class__* assignment works only if both classes have the same
  *__slots__*.
zattribute-referencesa���Attribute references
********************

An attribute reference is a primary followed by a period and a name:

attributeref ::= primary "." identifier

The primary must evaluate to an object of a type that supports
attribute references, which most objects do.  This object is then
asked to produce the attribute whose name is the identifier.  This
production can be customized by overriding the "__getattr__()" method.
If this attribute is not available, the exception "AttributeError" is
raised.  Otherwise, the type and value of the object produced is
determined by the object.  Multiple evaluations of the same attribute
reference may yield different objects.
Z	augassigna���Augmented assignment statements
*******************************

Augmented assignment is the combination, in a single statement, of a
binary operation and an assignment statement:

augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)
   augtarget                 ::= identifier | attributeref | subscription | slicing
   augop                     ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
             | ">>=" | "<<=" | "&=" | "^=" | "|="

(See section Primaries for the syntax definitions of the last three
symbols.)

An augmented assignment evaluates the target (which, unlike normal
assignment statements, cannot be an unpacking) and the expression
list, performs the binary operation specific to the type of assignment
on the two operands, and assigns the result to the original target.
The target is only evaluated once.

An augmented assignment expression like "x += 1" can be rewritten as
"x = x + 1" to achieve a similar, but not exactly equal effect. In the
augmented version, "x" is only evaluated once. Also, when possible,
the actual operation is performed *in-place*, meaning that rather than
creating a new object and assigning that to the target, the old object
is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-
hand side *before* evaluating the right-hand side.  For example, "a[i]
+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs
the addition, and lastly, it writes the result back to "a[i]".

With the exception of assigning to tuples and multiple targets in a
single statement, the assignment done by augmented assignment
statements is handled the same way as normal assignments. Similarly,
with the exception of the possible *in-place* behavior, the binary
operation performed by augmented assignment is the same as the normal
binary operations.

For targets which are attribute references, the same caveat about
class and instance attributes applies as for regular assignments.
Zbinaryui��Binary arithmetic operations
****************************

The binary arithmetic operations have the conventional priority
levels.  Note that some of these operations also apply to certain non-
numeric types.  Apart from the power operator, there are only two
levels, one for multiplicative operators and one for additive
operators:

The "*" (multiplication) operator yields the product of its arguments.
The arguments must either both be numbers, or one argument must be an
integer and the other must be a sequence. In the former case, the
numbers are converted to a common type and then multiplied together.
In the latter case, sequence repetition is performed; a negative
repetition factor yields an empty sequence.

The "@" (at) operator is intended to be used for matrix
multiplication.  No builtin Python types implement this operator.

New in version 3.5.

The "/" (division) and "//" (floor division) operators yield the
quotient of their arguments.  The numeric arguments are first
converted to a common type. Division of integers yields a float, while
floor division of integers results in an integer; the result is that
of mathematical division with the ‘floor’ function applied to the
result.  Division by zero raises the "ZeroDivisionError" exception.

The "%" (modulo) operator yields the remainder from the division of
the first argument by the second.  The numeric arguments are first
converted to a common type.  A zero right argument raises the
"ZeroDivisionError" exception.  The arguments may be floating point
numbers, e.g., "3.14%0.7" equals "0.34" (since "3.14" equals "4*0.7 +
0.34".)  The modulo operator always yields a result with the same sign
as its second operand (or zero); the absolute value of the result is
strictly smaller than the absolute value of the second operand [1].

The floor division and modulo operators are connected by the following
identity: "x == (x//y)*y + (x%y)".  Floor division and modulo are also
connected with the built-in function "divmod()": "divmod(x, y) ==
(x//y, x%y)". [2].

In addition to performing the modulo operation on numbers, the "%"
operator is also overloaded by string objects to perform old-style
string formatting (also known as interpolation).  The syntax for
string formatting is described in the Python Library Reference,
section printf-style String Formatting.

The floor division operator, the modulo operator, and the "divmod()"
function are not defined for complex numbers.  Instead, convert to a
floating point number using the "abs()" function if appropriate.

The "+" (addition) operator yields the sum of its arguments.  The
arguments must either both be numbers or both be sequences of the same
type.  In the former case, the numbers are converted to a common type
and then added together. In the latter case, the sequences are
concatenated.

The "-" (subtraction) operator yields the difference of its arguments.
The numeric arguments are first converted to a common type.
Zbitwisea$��Binary bitwise operations
*************************

Each of the three bitwise operations has a different priority level:

and_expr ::= shift_expr | and_expr "&" shift_expr
   xor_expr ::= and_expr | xor_expr "^" and_expr
   or_expr  ::= xor_expr | or_expr "|" xor_expr

The "&" operator yields the bitwise AND of its arguments, which must
be integers.

The "^" operator yields the bitwise XOR (exclusive OR) of its
arguments, which must be integers.

The "|" operator yields the bitwise (inclusive) OR of its arguments,
which must be integers.
zbltin-code-objectsux��Code Objects
************

Code objects are used by the implementation to represent “pseudo-
compiled” executable Python code such as a function body. They differ
from function objects because they don’t contain a reference to their
global execution environment.  Code objects are returned by the built-
in "compile()" function and can be extracted from function objects
through their "__code__" attribute. See also the "code" module.

A code object can be executed or evaluated by passing it (instead of a
source string) to the "exec()" or "eval()"  built-in functions.

See The standard type hierarchy for more information.
zbltin-ellipsis-objecta.��The Ellipsis Object
*******************

This object is commonly used by slicing (see Slicings).  It supports
no special operations.  There is exactly one ellipsis object, named
"Ellipsis" (a built-in name).  "type(Ellipsis)()" produces the
"Ellipsis" singleton.

It is written as "Ellipsis" or "...".
zbltin-null-objectu��The Null Object
***************

This object is returned by functions that don’t explicitly return a
value.  It supports no special operations.  There is exactly one null
object, named "None" (a built-in name).  "type(None)()" produces the
same singleton.

It is written as "None".
zbltin-type-objectsu5��Type Objects
************

Type objects represent the various object types.  An object’s type is
accessed by the built-in function "type()".  There are no special
operations on types.  The standard module "types" defines names for
all standard built-in types.

Types are written like this: "<class 'int'>".
Zbooleansa���Boolean operations
******************

or_test  ::= and_test | or_test "or" and_test
   and_test ::= not_test | and_test "and" not_test
   not_test ::= comparison | "not" not_test

In the context of Boolean operations, and also when expressions are
used by control flow statements, the following values are interpreted
as false: "False", "None", numeric zero of all types, and empty
strings and containers (including strings, tuples, lists,
dictionaries, sets and frozensets).  All other values are interpreted
as true.  User-defined objects can customize their truth value by
providing a "__bool__()" method.

The operator "not" yields "True" if its argument is false, "False"
otherwise.

The expression "x and y" first evaluates *x*; if *x* is false, its
value is returned; otherwise, *y* is evaluated and the resulting value
is returned.

The expression "x or y" first evaluates *x*; if *x* is true, its value
is returned; otherwise, *y* is evaluated and the resulting value is
returned.

(Note that neither "and" nor "or" restrict the value and type they
return to "False" and "True", but rather return the last evaluated
argument.  This is sometimes useful, e.g., if "s" is a string that
should be replaced by a default value if it is empty, the expression
"s or 'foo'" yields the desired value.  Because "not" has to create a
new value, it returns a boolean value regardless of the type of its
argument (for example, "not 'foo'" produces "False" rather than "''".)
�breaka$��The "break" statement
*********************

break_stmt ::= "break"

"break" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition within that
loop.

It terminates the nearest enclosing loop, skipping the optional "else"
clause if the loop has one.

If a "for" loop is terminated by "break", the loop control target
keeps its current value.

When "break" passes control out of a "try" statement with a "finally"
clause, that "finally" clause is executed before really leaving the
loop.
zcallable-typesu����Emulating callable objects
**************************

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

Called when the instance is “called” as a function; if this method
   is defined, "x(arg1, arg2, ...)" is a shorthand for
   "x.__call__(arg1, arg2, ...)".
ZcallsuC��Calls
*****

A call calls a callable object (e.g., a *function*) with a possibly
empty series of *arguments*:

call                 ::= primary "(" [argument_list [","] | comprehension] ")"
   argument_list        ::= positional_arguments ["," starred_and_keywords]
                       ["," keywords_arguments]
                     | starred_and_keywords ["," keywords_arguments]
                     | keywords_arguments
   positional_arguments ::= ["*"] expression ("," ["*"] expression)*
   starred_and_keywords ::= ("*" expression | keyword_item)
                            ("," "*" expression | "," keyword_item)*
   keywords_arguments   ::= (keyword_item | "**" expression)
                          ("," keyword_item | "," "**" expression)*
   keyword_item         ::= identifier "=" expression

An optional trailing comma may be present after the positional and
keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined
functions, built-in functions, methods of built-in objects, class
objects, methods of class instances, and all objects having a
"__call__()" method are callable).  All argument expressions are
evaluated before the call is attempted.  Please refer to section
Function definitions for the syntax of formal *parameter* lists.

If keyword arguments are present, they are first converted to
positional arguments, as follows.  First, a list of unfilled slots is
created for the formal parameters.  If there are N positional
arguments, they are placed in the first N slots.  Next, for each
keyword argument, the identifier is used to determine the
corresponding slot (if the identifier is the same as the first formal
parameter name, the first slot is used, and so on).  If the slot is
already filled, a "TypeError" exception is raised. Otherwise, the
value of the argument is placed in the slot, filling it (even if the
expression is "None", it fills the slot).  When all arguments have
been processed, the slots that are still unfilled are filled with the
corresponding default value from the function definition.  (Default
values are calculated, once, when the function is defined; thus, a
mutable object such as a list or dictionary used as default value will
be shared by all calls that don’t specify an argument value for the
corresponding slot; this should usually be avoided.)  If there are any
unfilled slots for which no default value is specified, a "TypeError"
exception is raised.  Otherwise, the list of filled slots is used as
the argument list for the call.

**CPython implementation detail:** An implementation may provide
built-in functions whose positional parameters do not have names, even
if they are ‘named’ for the purpose of documentation, and which
therefore cannot be supplied by keyword.  In CPython, this is the case
for functions implemented in C that use "PyArg_ParseTuple()" to parse
their arguments.

If there are more positional arguments than there are formal parameter
slots, a "TypeError" exception is raised, unless a formal parameter
using the syntax "*identifier" is present; in this case, that formal
parameter receives a tuple containing the excess positional arguments
(or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter
name, a "TypeError" exception is raised, unless a formal parameter
using the syntax "**identifier" is present; in this case, that formal
parameter receives a dictionary containing the excess keyword
arguments (using the keywords as keys and the argument values as
corresponding values), or a (new) empty dictionary if there were no
excess keyword arguments.

If the syntax "*expression" appears in the function call, "expression"
must evaluate to an *iterable*.  Elements from these iterables are
treated as if they were additional positional arguments.  For the call
"f(x1, x2, *y, x3, x4)", if *y* evaluates to a sequence *y1*, …, *yM*,
this is equivalent to a call with M+4 positional arguments *x1*, *x2*,
*y1*, …, *yM*, *x3*, *x4*.

A consequence of this is that although the "*expression" syntax may
appear *after* explicit keyword arguments, it is processed *before*
the keyword arguments (and any "**expression" arguments – see below).
So:

>>> def f(a, b):
   ...     print(a, b)
   ...
   >>> f(b=1, *(2,))
   2 1
   >>> f(a=1, *(2,))
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: f() got multiple values for keyword argument 'a'
   >>> f(1, *(2,))
   1 2

It is unusual for both keyword arguments and the "*expression" syntax
to be used in the same call, so in practice this confusion does not
arise.

If the syntax "**expression" appears in the function call,
"expression" must evaluate to a *mapping*, the contents of which are
treated as additional keyword arguments.  If a keyword is already
present (as an explicit keyword argument, or from another unpacking),
a "TypeError" exception is raised.

Formal parameters using the syntax "*identifier" or "**identifier"
cannot be used as positional argument slots or as keyword argument
names.

Changed in version 3.5: Function calls accept any number of "*" and
"**" unpackings, positional arguments may follow iterable unpackings
("*"), and keyword arguments may follow dictionary unpackings ("**").
Originally proposed by **PEP 448**.

A call always returns some value, possibly "None", unless it raises an
exception.  How this value is computed depends on the type of the
callable object.

If it is—

a user-defined function:
   The code block for the function is executed, passing it the
   argument list.  The first thing the code block will do is bind the
   formal parameters to the arguments; this is described in section
   Function definitions.  When the code block executes a "return"
   statement, this specifies the return value of the function call.

a built-in function or method:
   The result is up to the interpreter; see Built-in Functions for the
   descriptions of built-in functions and methods.

a class object:
   A new instance of that class is returned.

a class instance method:
   The corresponding user-defined function is called, with an argument
   list that is one longer than the argument list of the call: the
   instance becomes the first argument.

a class instance:
   The class must define a "__call__()" method; the effect is then the
   same as if that method was called.
�classu	��Class definitions
*****************

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= [decorators] "class" classname [inheritance] ":" suite
   inheritance ::= "(" [argument_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  The inheritance list
usually gives a list of base classes (see Customizing class creation
for more advanced uses), so each item in the list should evaluate to a
class object which allows subclassing.  Classes without an inheritance
list inherit, by default, from the base class "object"; hence,

class Foo:
       pass

is equivalent to

class Foo(object):
       pass

The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.)  When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [4] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

@f1(arg)
   @f2
   class Foo: pass

is roughly equivalent to

class Foo: pass
   Foo = f1(arg)(f2(Foo))

The evaluation rules for the decorator expressions are the same as for
function decorators.  The result is then bound to the class name.

**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances.  Instance attributes
can be set in a method with "self.name = value".  Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way.  Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results.  Descriptors can be used to create instance
variables with different implementation details.

See also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -
  Class Decorators
Zcomparisonsu6)��Comparisons
***********

Unlike C, all comparison operations in Python have the same priority,
which is lower than that of any arithmetic, shifting or bitwise
operation.  Also unlike C, expressions like "a < b < c" have the
interpretation that is conventional in mathematics:

comparison    ::= or_expr ( comp_operator or_expr )*
   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="
                     | "is" ["not"] | ["not"] "in"

Comparisons yield boolean values: "True" or "False".

Comparisons can be chained arbitrarily, e.g., "x < y <= z" is
equivalent to "x < y and y <= z", except that "y" is evaluated only
once (but in both cases "z" is not evaluated at all when "x < y" is
found to be false).

Formally, if *a*, *b*, *c*, …, *y*, *z* are expressions and *op1*,
*op2*, …, *opN* are comparison operators, then "a op1 b op2 c ... y
opN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except
that each expression is evaluated at most once.

Note that "a op1 b op2 c" doesn’t imply any kind of comparison between
*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though
perhaps not pretty).

Value comparisons
=================

The operators "<", ">", "==", ">=", "<=", and "!=" compare the values
of two objects.  The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in
addition to type and identity).  The value of an object is a rather
abstract notion in Python: For example, there is no canonical access
method for an object’s value.  Also, there is no requirement that the
value of an object should be constructed in a particular way, e.g.
comprised of all its data attributes. Comparison operators implement a
particular notion of what the value of an object is.  One can think of
them as defining the value of an object indirectly, by means of their
comparison implementation.

Because all types are (direct or indirect) subtypes of "object", they
inherit the default comparison behavior from "object".  Types can
customize their comparison behavior by implementing *rich comparison
methods* like "__lt__()", described in Basic customization.

The default behavior for equality comparison ("==" and "!=") is based
on the identity of the objects.  Hence, equality comparison of
instances with the same identity results in equality, and equality
comparison of instances with different identities results in
inequality.  A motivation for this default behavior is the desire that
all objects should be reflexive (i.e. "x is y" implies "x == y").

A default order comparison ("<", ">", "<=", and ">=") is not provided;
an attempt raises "TypeError".  A motivation for this default behavior
is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with
different identities are always unequal, may be in contrast to what
types will need that have a sensible definition of object value and
value-based equality.  Such types will need to customize their
comparison behavior, and in fact, a number of built-in types have done
that.

The following list describes the comparison behavior of the most
important built-in types.

* Numbers of built-in numeric types (Numeric Types — int, float,
  complex) and of the standard library types "fractions.Fraction" and
  "decimal.Decimal" can be compared within and across their types,
  with the restriction that complex numbers do not support order
  comparison.  Within the limits of the types involved, they compare
  mathematically (algorithmically) correct without loss of precision.

The not-a-number values "float('NaN')" and "Decimal('NaN')" are
  special.  They are identical to themselves ("x is x" is true) but
  are not equal to themselves ("x == x" is false).  Additionally,
  comparing any number to a not-a-number value will return "False".
  For example, both "3 < float('NaN')" and "float('NaN') < 3" will
  return "False".

* Binary sequences (instances of "bytes" or "bytearray") can be
  compared within and across their types.  They compare
  lexicographically using the numeric values of their elements.

* Strings (instances of "str") compare lexicographically using the
  numerical Unicode code points (the result of the built-in function
  "ord()") of their characters. [3]

Strings and binary sequences cannot be directly compared.

* Sequences (instances of "tuple", "list", or "range") can be
  compared only within each of their types, with the restriction that
  ranges do not support order comparison.  Equality comparison across
  these types results in unequality, and ordering comparison across
  these types raises "TypeError".

Sequences compare lexicographically using comparison of
  corresponding elements, whereby reflexivity of the elements is
  enforced.

In enforcing reflexivity of elements, the comparison of collections
  assumes that for a collection element "x", "x == x" is always true.
  Based on that assumption, element identity is compared first, and
  element comparison is performed only for distinct elements.  This
  approach yields the same result as a strict element comparison
  would, if the compared elements are reflexive.  For non-reflexive
  elements, the result is different than for strict element
  comparison, and may be surprising:  The non-reflexive not-a-number
  values for example result in the following comparison behavior when
  used in a list:

>>> nan = float('NaN')
     >>> nan is nan
     True
     >>> nan == nan
     False                 <-- the defined non-reflexive behavior of NaN
     >>> [nan] == [nan]
     True                  <-- list enforces reflexivity and tests identity first

Lexicographical comparison between built-in collections works as
  follows:

* For two collections to compare equal, they must be of the same
    type, have the same length, and each pair of corresponding
    elements must compare equal (for example, "[1,2] == (1,2)" is
    false because the type is not the same).

* Collections that support order comparison are ordered the same
    as their first unequal elements (for example, "[1,2,x] <= [1,2,y]"
    has the same value as "x <= y").  If a corresponding element does
    not exist, the shorter collection is ordered first (for example,
    "[1,2] < [1,2,3]" is true).

* Mappings (instances of "dict") compare equal if and only if they
  have equal *(key, value)* pairs. Equality comparison of the keys and
  values enforces reflexivity.

Order comparisons ("<", ">", "<=", and ">=") raise "TypeError".

* Sets (instances of "set" or "frozenset") can be compared within
  and across their types.

They define order comparison operators to mean subset and superset
  tests.  Those relations do not define total orderings (for example,
  the two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one
  another, nor supersets of one another).  Accordingly, sets are not
  appropriate arguments for functions which depend on total ordering
  (for example, "min()", "max()", and "sorted()" produce undefined
  results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

* Most other built-in types have no comparison methods implemented,
  so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should
follow some consistency rules, if possible:

* Equality comparison should be reflexive. In other words, identical
  objects should compare equal:

"x is y" implies "x == y"

* Comparison should be symmetric. In other words, the following
  expressions should have the same result:

"x == y" and "y == x"

"x != y" and "y != x"

"x < y" and "y > x"

"x <= y" and "y >= x"

* Comparison should be transitive. The following (non-exhaustive)
  examples illustrate that:

"x > y and y > z" implies "x > z"

"x < y and y <= z" implies "x < z"

* Inverse comparison should result in the boolean negation. In other
  words, the following expressions should have the same result:

"x == y" and "not x != y"

"x < y" and "not x >= y" (for total ordering)

"x > y" and "not x <= y" (for total ordering)

The last two expressions apply to totally ordered collections (e.g.
  to sequences, but not to sets or mappings). See also the
  "total_ordering()" decorator.

* The "hash()" result should be consistent with equality. Objects
  that are equal should either have the same hash value, or be marked
  as unhashable.

Python does not enforce these consistency rules. In fact, the
not-a-number values are an example for not following these rules.

Membership test operations
==========================

The operators "in" and "not in" test for membership.  "x in s"
evaluates to "True" if *x* is a member of *s*, and "False" otherwise.
"x not in s" returns the negation of "x in s".  All built-in sequences
and set types support this as well as dictionary, for which "in" tests
whether the dictionary has a given key. For container types such as
list, tuple, set, frozenset, dict, or collections.deque, the
expression "x in y" is equivalent to "any(x is e or x == e for e in
y)".

For the string and bytes types, "x in y" is "True" if and only if *x*
is a substring of *y*.  An equivalent test is "y.find(x) != -1".
Empty strings are always considered to be a substring of any other
string, so """ in "abc"" will return "True".

For user-defined classes which define the "__contains__()" method, "x
in y" returns "True" if "y.__contains__(x)" returns a true value, and
"False" otherwise.

For user-defined classes which do not define "__contains__()" but do
define "__iter__()", "x in y" is "True" if some value "z" with "x ==
z" is produced while iterating over "y".  If an exception is raised
during the iteration, it is as if "in" raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines
"__getitem__()", "x in y" is "True" if and only if there is a non-
negative integer index *i* such that "x == y[i]", and all lower
integer indices do not raise "IndexError" exception.  (If any other
exception is raised, it is as if "in" raised that exception).

The operator "not in" is defined to have the inverse true value of
"in".

Identity comparisons
====================

The operators "is" and "is not" test for object identity: "x is y" is
true if and only if *x* and *y* are the same object.  Object identity
is determined using the "id()" function.  "x is not y" yields the
inverse truth value. [4]
Zcompoundu�b��Compound statements
*******************

Compound statements contain (groups of) other statements; they affect
or control the execution of those other statements in some way.  In
general, compound statements span multiple lines, although in simple
incarnations a whole compound statement may be contained in one line.

The "if", "while" and "for" statements implement traditional control
flow constructs.  "try" specifies exception handlers and/or cleanup
code for a group of statements, while the "with" statement allows the
execution of initialization and finalization code around a block of
code.  Function and class definitions are also syntactically compound
statements.

A compound statement consists of one or more ‘clauses.’  A clause
consists of a header and a ‘suite.’  The clause headers of a
particular compound statement are all at the same indentation level.
Each clause header begins with a uniquely identifying keyword and ends
with a colon.  A suite is a group of statements controlled by a
clause.  A suite can be one or more semicolon-separated simple
statements on the same line as the header, following the header’s
colon, or it can be one or more indented statements on subsequent
lines.  Only the latter form of a suite can contain nested compound
statements; the following is illegal, mostly because it wouldn’t be
clear to which "if" clause a following "else" clause would belong:

if test1: if test2: print(x)

Also note that the semicolon binds tighter than the colon in this
context, so that in the following example, either all or none of the
"print()" calls are executed:

if x < y < z: print(x); print(y); print(z)

Summarizing:

Note that statements always end in a "NEWLINE" possibly followed by a
"DEDENT".  Also note that optional continuation clauses always begin
with a keyword that cannot start a statement, thus there are no
ambiguities (the ‘dangling "else"’ problem is solved in Python by
requiring nested "if" statements to be indented).

The formatting of the grammar rules in the following sections places
each clause on a separate line for clarity.

The "if" statement
==================

The "if" statement is used for conditional execution:

if_stmt ::= "if" expression ":" suite
               ( "elif" expression ":" suite )*
               ["else" ":" suite]

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.

The "while" statement
=====================

The "while" statement is used for repeated execution as long as an
expression is true:

while_stmt ::= "while" expression ":" suite
                  ["else" ":" suite]

This repeatedly tests the expression and, if it is true, executes the
first suite; if the expression is false (which may be the first time
it is tested) the suite of the "else" clause, if present, is executed
and the loop terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and goes back
to testing the expression.

The "for" statement
===================

The "for" statement is used to iterate over the elements of a sequence
(such as a string, tuple or list) or other iterable object:

for_stmt ::= "for" target_list "in" expression_list ":" suite
                ["else" ":" suite]

The expression list is evaluated once; it should yield an iterable
object.  An iterator is created for the result of the
"expression_list".  The suite is then executed once for each item
provided by the iterator, in the order returned by the iterator.  Each
item in turn is assigned to the target list using the standard rules
for assignments (see Assignment statements), and then the suite is
executed.  When the items are exhausted (which is immediately when the
sequence is empty or an iterator raises a "StopIteration" exception),
the suite in the "else" clause, if present, is executed, and the loop
terminates.

A "break" statement executed in the first suite terminates the loop
without executing the "else" clause’s suite.  A "continue" statement
executed in the first suite skips the rest of the suite and continues
with the next item, or with the "else" clause if there is no next
item.

The for-loop makes assignments to the variables(s) in the target list.
This overwrites all previous assignments to those variables including
those made in the suite of the for-loop:

for i in range(10):
       print(i)
       i = 5             # this will not affect the for-loop
                         # because i will be overwritten with the next
                         # index in the range

Names in the target list are not deleted when the loop is finished,
but if the sequence is empty, they will not have been assigned to at
all by the loop.  Hint: the built-in function "range()" returns an
iterator of integers suitable to emulate the effect of Pascal’s "for i
:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".

Note: There is a subtlety when the sequence is being modified by the
  loop (this can only occur for mutable sequences, i.e. lists).  An
  internal counter is used to keep track of which item is used next,
  and this is incremented on each iteration.  When this counter has
  reached the length of the sequence the loop terminates.  This means
  that if the suite deletes the current (or a previous) item from the
  sequence, the next item will be skipped (since it gets the index of
  the current item which has already been treated).  Likewise, if the
  suite inserts an item in the sequence before the current item, the
  current item will be treated again the next time through the loop.
  This can lead to nasty bugs that can be avoided by making a
  temporary copy using a slice of the whole sequence, e.g.,

for x in a[:]:
         if x < 0: a.remove(x)

The "try" statement
===================

The "try" statement specifies exception handlers and/or cleanup code
for a group of statements:

try_stmt  ::= try1_stmt | try2_stmt
   try1_stmt ::= "try" ":" suite
                 ("except" [expression ["as" identifier]] ":" suite)+
                 ["else" ":" suite]
                 ["finally" ":" suite]
   try2_stmt ::= "try" ":" suite
                 "finally" ":" suite

The "except" clause(s) specify one or more exception handlers. When no
exception occurs in the "try" clause, no exception handler is
executed. When an exception occurs in the "try" suite, a search for an
exception handler is started.  This search inspects the except clauses
in turn until one is found that matches the exception.  An expression-
less except clause, if present, must be last; it matches any
exception.  For an except clause with an expression, that expression
is evaluated, and the clause matches the exception if the resulting
object is “compatible” with the exception.  An object is compatible
with an exception if it is the class or a base class of the exception
object or a tuple containing an item compatible with the exception.

If no except clause matches the exception, the search for an exception
handler continues in the surrounding code and on the invocation stack.
[1]

If the evaluation of an expression in the header of an except clause
raises an exception, the original search for a handler is canceled and
a search starts for the new exception in the surrounding code and on
the call stack (it is treated as if the entire "try" statement raised
the exception).

When a matching except clause is found, the exception is assigned to
the target specified after the "as" keyword in that except clause, if
present, and the except clause’s suite is executed.  All except
clauses must have an executable block.  When the end of this block is
reached, execution continues normally after the entire try statement.
(This means that if two nested handlers exist for the same exception,
and the exception occurs in the try clause of the inner handler, the
outer handler will not handle the exception.)

When an exception has been assigned using "as target", it is cleared
at the end of the except clause.  This is as if

except E as N:
       foo

was translated to

except E as N:
       try:
           foo
       finally:
           del N

This means the exception must be assigned to a different name to be
able to refer to it after the except clause.  Exceptions are cleared
because with the traceback attached to them, they form a reference
cycle with the stack frame, keeping all locals in that frame alive
until the next garbage collection occurs.

Before an except clause’s suite is executed, details about the
exception are stored in the "sys" module and can be accessed via
"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the
exception class, the exception instance and a traceback object (see
section The standard type hierarchy) identifying the point in the
program where the exception occurred.  "sys.exc_info()" values are
restored to their previous values (before the call) when returning
from a function that handled an exception.

The optional "else" clause is executed if and when control flows off
the end of the "try" clause. [2] Exceptions in the "else" clause are
not handled by the preceding "except" clauses.

If "finally" is present, it specifies a ‘cleanup’ handler.  The "try"
clause is executed, including any "except" and "else" clauses.  If an
exception occurs in any of the clauses and is not handled, the
exception is temporarily saved. The "finally" clause is executed.  If
there is a saved exception it is re-raised at the end of the "finally"
clause.  If the "finally" clause raises another exception, the saved
exception is set as the context of the new exception. If the "finally"
clause executes a "return" or "break" statement, the saved exception
is discarded:

>>> def f():
   ...     try:
   ...         1/0
   ...     finally:
   ...         return 42
   ...
   >>> f()
   42

The exception information is not available to the program during
execution of the "finally" clause.

When a "return", "break" or "continue" statement is executed in the
"try" suite of a "try"…"finally" statement, the "finally" clause is
also executed ‘on the way out.’ A "continue" statement is illegal in
the "finally" clause. (The reason is a problem with the current
implementation — this restriction may be lifted in the future).

The return value of a function is determined by the last "return"
statement executed.  Since the "finally" clause always executes, a
"return" statement executed in the "finally" clause will always be the
last one executed:

>>> def foo():
   ...     try:
   ...         return 'try'
   ...     finally:
   ...         return 'finally'
   ...
   >>> foo()
   'finally'

Additional information on exceptions can be found in section
Exceptions, and information on using the "raise" statement to generate
exceptions may be found in section The raise statement.

The "with" statement
====================

The "with" statement is used to wrap the execution of a block with
methods defined by a context manager (see section With Statement
Context Managers). This allows common "try"…"except"…"finally" usage
patterns to be encapsulated for convenient reuse.

with_stmt ::= "with" with_item ("," with_item)* ":" suite
   with_item ::= expression ["as" target]

The execution of the "with" statement with one “item” proceeds as
follows:

1. The context expression (the expression given in the "with_item")
   is evaluated to obtain a context manager.

2. The context manager’s "__exit__()" is loaded for later use.

3. The context manager’s "__enter__()" method is invoked.

4. If a target was included in the "with" statement, the return
   value from "__enter__()" is assigned to it.

Note: The "with" statement guarantees that if the "__enter__()"
     method returns without an error, then "__exit__()" will always be
     called. Thus, if an error occurs during the assignment to the
     target list, it will be treated the same as an error occurring
     within the suite would be. See step 6 below.

5. The suite is executed.

6. The context manager’s "__exit__()" method is invoked.  If an
   exception caused the suite to be exited, its type, value, and
   traceback are passed as arguments to "__exit__()". Otherwise, three
   "None" arguments are supplied.

If the suite was exited due to an exception, and the return value
   from the "__exit__()" method was false, the exception is reraised.
   If the return value was true, the exception is suppressed, and
   execution continues with the statement following the "with"
   statement.

If the suite was exited for any reason other than an exception, the
   return value from "__exit__()" is ignored, and execution proceeds
   at the normal location for the kind of exit that was taken.

With more than one item, the context managers are processed as if
multiple "with" statements were nested:

with A() as a, B() as b:
       suite

is equivalent to

with A() as a:
       with B() as b:
           suite

Changed in version 3.1: Support for multiple context expressions.

See also:

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

Function definitions
====================

A function definition defines a user-defined function object (see
section The standard type hierarchy):

funcdef        ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite
   decorators     ::= decorator+
   decorator      ::= "@" dotted_name ["(" [argument_list [","]] ")"] NEWLINE
   dotted_name    ::= identifier ("." identifier)*
   parameter_list ::= (defparameter ",")*
                      | "*" [parameter] ("," defparameter)* ["," "**" parameter]
                      | "**" parameter
                      | defparameter [","] )
   parameter      ::= identifier [":" expression]
   defparameter   ::= parameter ["=" expression]
   funcname       ::= identifier

A function definition is an executable statement.  Its execution binds
the function name in the current local namespace to a function object
(a wrapper around the executable code for the function).  This
function object contains a reference to the current global namespace
as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets
executed only when the function is called. [3]

A function definition may be wrapped by one or more *decorator*
expressions. Decorator expressions are evaluated when the function is
defined, in the scope that contains the function definition.  The
result must be a callable, which is invoked with the function object
as the only argument. The returned value is bound to the function name
instead of the function object.  Multiple decorators are applied in
nested fashion. For example, the following code

@f1(arg)
   @f2
   def func(): pass

is roughly equivalent to

def func(): pass
   func = f1(arg)(f2(func))

except that the original function is not temporarily bound to the name
"func".

When one or more *parameters* have the form *parameter* "="
*expression*, the function is said to have “default parameter values.”
For a parameter with a default value, the corresponding *argument* may
be omitted from a call, in which case the parameter’s default value is
substituted.  If a parameter has a default value, all following
parameters up until the “"*"” must also have a default value — this is
a syntactic restriction that is not expressed by the grammar.

**Default parameter values are evaluated from left to right when the
function definition is executed.** This means that the expression is
evaluated once, when the function is defined, and that the same “pre-
computed” value is used for each call.  This is especially important
to understand when a default parameter is a mutable object, such as a
list or a dictionary: if the function modifies the object (e.g. by
appending an item to a list), the default value is in effect modified.
This is generally not what was intended.  A way around this is to use
"None" as the default, and explicitly test for it in the body of the
function, e.g.:

def whats_on_the_telly(penguin=None):
       if penguin is None:
           penguin = []
       penguin.append("property of the zoo")
       return penguin

Function call semantics are described in more detail in section Calls.
A function call always assigns values to all parameters mentioned in
the parameter list, either from position arguments, from keyword
arguments, or from default values.  If the form “"*identifier"” is
present, it is initialized to a tuple receiving any excess positional
parameters, defaulting to the empty tuple.  If the form
“"**identifier"” is present, it is initialized to a new dictionary
receiving any excess keyword arguments, defaulting to a new empty
dictionary. Parameters after “"*"” or “"*identifier"” are keyword-only
parameters and may only be passed used keyword arguments.

Parameters may have annotations of the form “": expression"” following
the parameter name.  Any parameter may have an annotation even those
of the form "*identifier" or "**identifier".  Functions may have
“return” annotation of the form “"-> expression"” after the parameter
list.  These annotations can be any valid Python expression and are
evaluated when the function definition is executed.  Annotations may
be evaluated in a different order than they appear in the source code.
The presence of annotations does not change the semantics of a
function.  The annotation values are available as values of a
dictionary keyed by the parameters’ names in the "__annotations__"
attribute of the function object.

It is also possible to create anonymous functions (functions not bound
to a name), for immediate use in expressions.  This uses lambda
expressions, described in section Lambdas.  Note that the lambda
expression is merely a shorthand for a simplified function definition;
a function defined in a “"def"” statement can be passed around or
assigned to another name just like a function defined by a lambda
expression.  The “"def"” form is actually more powerful since it
allows the execution of multiple statements and annotations.

**Programmer’s note:** Functions are first-class objects.  A “"def"”
statement executed inside a function definition defines a local
function that can be returned or passed around.  Free variables used
in the nested function can access the local variables of the function
containing the def.  See section Naming and binding for details.

See also:

**PEP 3107** - Function Annotations
     The original specification for function annotations.

Class definitions
=================

A class definition defines a class object (see section The standard
type hierarchy):

classdef    ::= [decorators] "class" classname [inheritance] ":" suite
   inheritance ::= "(" [argument_list] ")"
   classname   ::= identifier

A class definition is an executable statement.  The inheritance list
usually gives a list of base classes (see Customizing class creation
for more advanced uses), so each item in the list should evaluate to a
class object which allows subclassing.  Classes without an inheritance
list inherit, by default, from the base class "object"; hence,

class Foo:
       pass

is equivalent to

class Foo(object):
       pass

The class’s suite is then executed in a new execution frame (see
Naming and binding), using a newly created local namespace and the
original global namespace. (Usually, the suite contains mostly
function definitions.)  When the class’s suite finishes execution, its
execution frame is discarded but its local namespace is saved. [4] A
class object is then created using the inheritance list for the base
classes and the saved local namespace for the attribute dictionary.
The class name is bound to this class object in the original local
namespace.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

@f1(arg)
   @f2
   class Foo: pass

is roughly equivalent to

class Foo: pass
   Foo = f1(arg)(f2(Foo))

The evaluation rules for the decorator expressions are the same as for
function decorators.  The result is then bound to the class name.

**Programmer’s note:** Variables defined in the class definition are
class attributes; they are shared by instances.  Instance attributes
can be set in a method with "self.name = value".  Both class and
instance attributes are accessible through the notation “"self.name"”,
and an instance attribute hides a class attribute with the same name
when accessed in this way.  Class attributes can be used as defaults
for instance attributes, but using mutable values there can lead to
unexpected results.  Descriptors can be used to create instance
variables with different implementation details.

See also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -
  Class Decorators

Coroutines
==========

New in version 3.5.

Coroutine function definition
-----------------------------

async_funcdef ::= [decorators] "async" "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite

Execution of Python coroutines can be suspended and resumed at many
points (see *coroutine*).  In the body of a coroutine, any "await" and
"async" identifiers become reserved keywords; "await" expressions,
"async for" and "async with" can only be used in coroutine bodies.

Functions defined with "async def" syntax are always coroutine
functions, even if they do not contain "await" or "async" keywords.

It is a "SyntaxError" to use "yield" expressions in "async def"
coroutines.

An example of a coroutine function:

async def func(param1, param2):
       do_stuff()
       await some_coroutine()

The "async for" statement
-------------------------

async_for_stmt ::= "async" for_stmt

An *asynchronous iterable* is able to call asynchronous code in its
*iter* implementation, and *asynchronous iterator* can call
asynchronous code in its *next* method.

The "async for" statement allows convenient iteration over
asynchronous iterators.

The following code:

async for TARGET in ITER:
       BLOCK
   else:
       BLOCK2

Is semantically equivalent to:

iter = (ITER)
   iter = type(iter).__aiter__(iter)
   running = True
   while running:
       try:
           TARGET = await type(iter).__anext__(iter)
       except StopAsyncIteration:
           running = False
       else:
           BLOCK
   else:
       BLOCK2

See also "__aiter__()" and "__anext__()" for details.

It is a "SyntaxError" to use "async for" statement outside of an
"async def" function.

The "async with" statement
--------------------------

async_with_stmt ::= "async" with_stmt

An *asynchronous context manager* is a *context manager* that is able
to suspend execution in its *enter* and *exit* methods.

The following code:

async with EXPR as VAR:
       BLOCK

Is semantically equivalent to:

mgr = (EXPR)
   aexit = type(mgr).__aexit__
   aenter = type(mgr).__aenter__(mgr)
   exc = True

VAR = await aenter
   try:
       BLOCK
   except:
       if not await aexit(mgr, *sys.exc_info()):
           raise
   else:
       await aexit(mgr, None, None, None)

See also "__aenter__()" and "__aexit__()" for details.

It is a "SyntaxError" to use "async with" statement outside of an
"async def" function.

See also: **PEP 492** - Coroutines with async and await syntax

-[ Footnotes ]-

[1] The exception is propagated to the invocation stack unless
    there is a "finally" clause which happens to raise another
    exception. That new exception causes the old one to be lost.

[2] Currently, control “flows off the end” except in the case of
    an exception or the execution of a "return", "continue", or
    "break" statement.

[3] A string literal appearing as the first statement in the
    function body is transformed into the function’s "__doc__"
    attribute and therefore the function’s *docstring*.

[4] A string literal appearing as the first statement in the class
    body is transformed into the namespace’s "__doc__" item and
    therefore the class’s *docstring*.
zcontext-managersu���With Statement Context Managers
*******************************

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

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

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

object.__enter__(self)

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

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

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

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

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

See also:

**PEP 343** - The “with” statement
     The specification, background, and examples for the Python "with"
     statement.
�continuea���The "continue" statement
************************

continue_stmt ::= "continue"

"continue" may only occur syntactically nested in a "for" or "while"
loop, but not nested in a function or class definition or "finally"
clause within that loop.  It continues with the next cycle of the
nearest enclosing loop.

When "continue" passes control out of a "try" statement with a
"finally" clause, that "finally" clause is executed before really
starting the next loop cycle.
Zconversionsu���Arithmetic conversions
**********************

When a description of an arithmetic operator below uses the phrase
“the numeric arguments are converted to a common type,” this means
that the operator implementation for built-in types works as follows:

* If either argument is a complex number, the other is converted to
  complex;

* otherwise, if either argument is a floating point number, the
  other is converted to floating point;

* otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a
left argument to the ‘%’ operator).  Extensions must define their own
conversion behavior.
Z
customizationu�4��Basic customization
*******************

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

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

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

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

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

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

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

Called after the instance has been created (by "__new__()"), but
   before it is returned to the caller.  The arguments are those
   passed to the class constructor expression.  If a base class has an
   "__init__()" method, the derived class’s "__init__()" method, if
   any, must explicitly call it to ensure proper initialization of the
   base class part of the instance; for example:
   "super().__init__([args...])".

Because "__new__()" and "__init__()" work together in constructing
   objects ("__new__()" to create it, and "__init__()" to customize
   it), no non-"None" value may be returned by "__init__()"; doing so
   will cause a "TypeError" to be raised at runtime.

object.__del__(self)

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

Note: "del x" doesn’t directly call "x.__del__()" — the former
     decrements the reference count for "x" by one, and the latter is
     only called when "x"’s reference count reaches zero.  Some common
     situations that may prevent the reference count of an object from
     going to zero include: circular references between objects (e.g.,
     a doubly-linked list or a tree data structure with parent and
     child pointers); a reference to the object on the stack frame of
     a function that caught an exception (the traceback stored in
     "sys.exc_info()[2]" keeps the stack frame alive); or a reference
     to the object on the stack frame that raised an unhandled
     exception in interactive mode (the traceback stored in
     "sys.last_traceback" keeps the stack frame alive).  The first
     situation can only be remedied by explicitly breaking the cycles;
     the second can be resolved by freeing the reference to the
     traceback object when it is no longer useful, and the third can
     be resolved by storing "None" in "sys.last_traceback". Circular
     references which are garbage are detected and cleaned up when the
     cyclic garbage collector is enabled (it’s on by default). Refer
     to the documentation for the "gc" module for more information
     about this topic.

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

object.__repr__(self)

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

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

object.__str__(self)

Called by "str(object)" and the built-in functions "format()" and
   "print()" to compute the “informal” or nicely printable string
   representation of an object.  The return value must be a string
   object.

This method differs from "object.__repr__()" in that there is no
   expectation that "__str__()" return a valid Python expression: a
   more convenient or concise representation can be used.

The default implementation defined by the built-in type "object"
   calls "object.__repr__()".

object.__bytes__(self)

Called by "bytes()" to compute a byte-string representation of an
   object. This should return a "bytes" object.

object.__format__(self, format_spec)

Called by the "format()" built-in function (and by extension, the
   "str.format()" method of class "str") to produce a “formatted”
   string representation of an object. The "format_spec" argument is a
   string that contains a description of the formatting options
   desired. The interpretation of the "format_spec" argument is up to
   the type implementing "__format__()", however most classes will
   either delegate formatting to one of the built-in types, or use a
   similar formatting option syntax.

See Format Specification Mini-Language for a description of the
   standard formatting syntax.

The return value must be a string object.

Changed in version 3.4: The __format__ method of "object" itself
   raises a "TypeError" if passed any non-empty string.

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

These are the so-called “rich comparison” methods. The
   correspondence between operator symbols and method names is as
   follows: "x<y" calls "x.__lt__(y)", "x<=y" calls "x.__le__(y)",
   "x==y" calls "x.__eq__(y)", "x!=y" calls "x.__ne__(y)", "x>y" calls
   "x.__gt__(y)", and "x>=y" calls "x.__ge__(y)".

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

By default, "__ne__()" delegates to "__eq__()" and inverts the
   result unless it is "NotImplemented".  There are no other implied
   relationships among the comparison operators, for example, the
   truth of "(x<y or x==y)" does not imply "x<=y". To automatically
   generate ordering operations from a single root operation, see
   "functools.total_ordering()".

See the paragraph on "__hash__()" for some important notes on
   creating *hashable* objects which support custom comparison
   operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used
   when the left argument does not support the operation but the right
   argument does); rather, "__lt__()" and "__gt__()" are each other’s
   reflection, "__le__()" and "__ge__()" are each other’s reflection,
   and "__eq__()" and "__ne__()" are their own reflection. If the
   operands are of different types, and right operand’s type is a
   direct or indirect subclass of the left operand’s type, the
   reflected method of the right operand has priority, otherwise the
   left operand’s method has priority.  Virtual subclassing is not
   considered.

object.__hash__(self)

Called by built-in function "hash()" and for operations on members
   of hashed collections including "set", "frozenset", and "dict".
   "__hash__()" should return an integer. The only required property
   is that objects which compare equal have the same hash value; it is
   advised to mix together the hash values of the components of the
   object that also play a part in comparison of objects by packing
   them into a tuple and hashing the tuple. Example:

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

Note: "hash()" truncates the value returned from an object’s
     custom "__hash__()" method to the size of a "Py_ssize_t".  This
     is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit
     builds. If an object’s   "__hash__()" must interoperate on builds
     of different bit sizes, be sure to check the width on all
     supported builds.  An easy way to do this is with "python -c
     "import sys; print(sys.hash_info.width)"".

If a class does not define an "__eq__()" method it should not
   define a "__hash__()" operation either; if it defines "__eq__()"
   but not "__hash__()", its instances will not be usable as items in
   hashable collections.  If a class defines mutable objects and
   implements an "__eq__()" method, it should not implement
   "__hash__()", since the implementation of hashable collections
   requires that a key’s hash value is immutable (if the object’s hash
   value changes, it will be in the wrong hash bucket).

User-defined classes have "__eq__()" and "__hash__()" methods by
   default; with them, all objects compare unequal (except with
   themselves) and "x.__hash__()" returns an appropriate value such
   that "x == y" implies both that "x is y" and "hash(x) == hash(y)".

A class that overrides "__eq__()" and does not define "__hash__()"
   will have its "__hash__()" implicitly set to "None".  When the
   "__hash__()" method of a class is "None", instances of the class
   will raise an appropriate "TypeError" when a program attempts to
   retrieve their hash value, and will also be correctly identified as
   unhashable when checking "isinstance(obj, collections.Hashable)".

If a class that overrides "__eq__()" needs to retain the
   implementation of "__hash__()" from a parent class, the interpreter
   must be told this explicitly by setting "__hash__ =
   <ParentClass>.__hash__".

If a class that does not override "__eq__()" wishes to suppress
   hash support, it should include "__hash__ = None" in the class
   definition. A class which defines its own "__hash__()" that
   explicitly raises a "TypeError" would be incorrectly identified as
   hashable by an "isinstance(obj, collections.Hashable)" call.

Note: By default, the "__hash__()" values of str, bytes and
     datetime objects are “salted” with an unpredictable random value.
     Although they remain constant within an individual Python
     process, they are not predictable between repeated invocations of
     Python.This is intended to provide protection against a denial-
     of-service caused by carefully-chosen inputs that exploit the
     worst case performance of a dict insertion, O(n^2) complexity.
     See http://www.ocert.org/advisories/ocert-2011-003.html for
     details.Changing hash values affects the iteration order of
     dicts, sets and other mappings.  Python has never made guarantees
     about this ordering (and it typically varies between 32-bit and
     64-bit builds).See also "PYTHONHASHSEED".

Changed in version 3.3: Hash randomization is enabled by default.

object.__bool__(self)

Called to implement truth value testing and the built-in operation
   "bool()"; should return "False" or "True".  When this method is not
   defined, "__len__()" is called, if it is defined, and the object is
   considered true if its result is nonzero.  If a class defines
   neither "__len__()" nor "__bool__()", all its instances are
   considered true.
Zdebuggeru�E��"pdb" — The Python Debugger
***************************

**Source code:** Lib/pdb.py

======================================================================

The module "pdb" defines an interactive source code debugger for
Python programs.  It supports setting (conditional) breakpoints and
single stepping at the source line level, inspection of stack frames,
source code listing, and evaluation of arbitrary Python code in the
context of any stack frame.  It also supports post-mortem debugging
and can be called under program control.

The debugger is extensible – it is actually defined as the class
"Pdb". This is currently undocumented but easily understood by reading
the source.  The extension interface uses the modules "bdb" and "cmd".

The debugger’s prompt is "(Pdb)". Typical usage to run a program under
control of the debugger is:

>>> import pdb
   >>> import mymodule
   >>> pdb.run('mymodule.test()')
   > <string>(0)?()
   (Pdb) continue
   > <string>(1)?()
   (Pdb) continue
   NameError: 'spam'
   > <string>(1)?()
   (Pdb)

Changed in version 3.3: Tab-completion via the "readline" module is
available for commands and command arguments, e.g. the current global
and local names are offered as arguments of the "p" command.

"pdb.py" can also be invoked as a script to debug other scripts.  For
example:

python3 -m pdb myscript.py

When invoked as a script, pdb will automatically enter post-mortem
debugging if the program being debugged exits abnormally.  After post-
mortem debugging (or after normal exit of the program), pdb will
restart the program.  Automatic restarting preserves pdb’s state (such
as breakpoints) and in most cases is more useful than quitting the
debugger upon program’s exit.

New in version 3.2: "pdb.py" now accepts a "-c" option that executes
commands as if given in a ".pdbrc" file, see Debugger Commands.

The typical usage to break into the debugger from a running program is
to insert

import pdb; pdb.set_trace()

at the location you want to break into the debugger.  You can then
step through the code following this statement, and continue running
without the debugger using the "continue" command.

The typical usage to inspect a crashed program is:

>>> import pdb
   >>> import mymodule
   >>> mymodule.test()
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
     File "./mymodule.py", line 4, in test
       test2()
     File "./mymodule.py", line 3, in test2
       print(spam)
   NameError: spam
   >>> pdb.pm()
   > ./mymodule.py(3)test2()
   -> print(spam)
   (Pdb)

The module defines the following functions; each enters the debugger
in a slightly different way:

pdb.run(statement, globals=None, locals=None)

Execute the *statement* (given as a string or a code object) under
   debugger control.  The debugger prompt appears before any code is
   executed; you can set breakpoints and type "continue", or you can
   step through the statement using "step" or "next" (all these
   commands are explained below).  The optional *globals* and *locals*
   arguments specify the environment in which the code is executed; by
   default the dictionary of the module "__main__" is used.  (See the
   explanation of the built-in "exec()" or "eval()" functions.)

pdb.runeval(expression, globals=None, locals=None)

Evaluate the *expression* (given as a string or a code object)
   under debugger control.  When "runeval()" returns, it returns the
   value of the expression.  Otherwise this function is similar to
   "run()".

pdb.runcall(function, *args, **kwds)

Call the *function* (a function or method object, not a string)
   with the given arguments.  When "runcall()" returns, it returns
   whatever the function call returned.  The debugger prompt appears
   as soon as the function is entered.

pdb.set_trace()

Enter the debugger at the calling stack frame.  This is useful to
   hard-code a breakpoint at a given point in a program, even if the
   code is not otherwise being debugged (e.g. when an assertion
   fails).

pdb.post_mortem(traceback=None)

Enter post-mortem debugging of the given *traceback* object.  If no
   *traceback* is given, it uses the one of the exception that is
   currently being handled (an exception must be being handled if the
   default is to be used).

pdb.pm()

Enter post-mortem debugging of the traceback found in
   "sys.last_traceback".

The "run*" functions and "set_trace()" are aliases for instantiating
the "Pdb" class and calling the method of the same name.  If you want
to access further features, you have to do this yourself:

class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False)

"Pdb" is the debugger class.

The *completekey*, *stdin* and *stdout* arguments are passed to the
   underlying "cmd.Cmd" class; see the description there.

The *skip* argument, if given, must be an iterable of glob-style
   module name patterns.  The debugger will not step into frames that
   originate in a module that matches one of these patterns. [1]

By default, Pdb sets a handler for the SIGINT signal (which is sent
   when the user presses "Ctrl-C" on the console) when you give a
   "continue" command. This allows you to break into the debugger
   again by pressing "Ctrl-C".  If you want Pdb not to touch the
   SIGINT handler, set *nosigint* to true.

Example call to enable tracing with *skip*:

import pdb; pdb.Pdb(skip=['django.*']).set_trace()

New in version 3.1: The *skip* argument.

New in version 3.2: The *nosigint* argument.  Previously, a SIGINT
   handler was never set by Pdb.

run(statement, globals=None, locals=None)
   runeval(expression, globals=None, locals=None)
   runcall(function, *args, **kwds)
   set_trace()

See the documentation for the functions explained above.

Debugger Commands
=================

The commands recognized by the debugger are listed below.  Most
commands can be abbreviated to one or two letters as indicated; e.g.
"h(elp)" means that either "h" or "help" can be used to enter the help
command (but not "he" or "hel", nor "H" or "Help" or "HELP").
Arguments to commands must be separated by whitespace (spaces or
tabs).  Optional arguments are enclosed in square brackets ("[]") in
the command syntax; the square brackets must not be typed.
Alternatives in the command syntax are separated by a vertical bar
("|").

Entering a blank line repeats the last command entered.  Exception: if
the last command was a "list" command, the next 11 lines are listed.

Commands that the debugger doesn’t recognize are assumed to be Python
statements and are executed in the context of the program being
debugged.  Python statements can also be prefixed with an exclamation
point ("!").  This is a powerful way to inspect the program being
debugged; it is even possible to change a variable or call a function.
When an exception occurs in such a statement, the exception name is
printed but the debugger’s state is not changed.

The debugger supports aliases.  Aliases can have parameters which
allows one a certain level of adaptability to the context under
examination.

Multiple commands may be entered on a single line, separated by ";;".
(A single ";" is not used as it is the separator for multiple commands
in a line that is passed to the Python parser.)  No intelligence is
applied to separating the commands; the input is split at the first
";;" pair, even if it is in the middle of a quoted string.

If a file ".pdbrc" exists in the user’s home directory or in the
current directory, it is read in and executed as if it had been typed
at the debugger prompt.  This is particularly useful for aliases.  If
both files exist, the one in the home directory is read first and
aliases defined there can be overridden by the local file.

Changed in version 3.2: ".pdbrc" can now contain commands that
continue debugging, such as "continue" or "next".  Previously, these
commands had no effect.

h(elp) [command]

Without argument, print the list of available commands.  With a
   *command* as argument, print help about that command.  "help pdb"
   displays the full documentation (the docstring of the "pdb"
   module).  Since the *command* argument must be an identifier, "help
   exec" must be entered to get help on the "!" command.

w(here)

Print a stack trace, with the most recent frame at the bottom.  An
   arrow indicates the current frame, which determines the context of
   most commands.

d(own) [count]

Move the current frame *count* (default one) levels down in the
   stack trace (to a newer frame).

u(p) [count]

Move the current frame *count* (default one) levels up in the stack
   trace (to an older frame).

b(reak) [([filename:]lineno | function) [, condition]]

With a *lineno* argument, set a break there in the current file.
   With a *function* argument, set a break at the first executable
   statement within that function.  The line number may be prefixed
   with a filename and a colon, to specify a breakpoint in another
   file (probably one that hasn’t been loaded yet).  The file is
   searched on "sys.path".  Note that each breakpoint is assigned a
   number to which all the other breakpoint commands refer.

If a second argument is present, it is an expression which must
   evaluate to true before the breakpoint is honored.

Without argument, list all breaks, including for each breakpoint,
   the number of times that breakpoint has been hit, the current
   ignore count, and the associated condition if any.

tbreak [([filename:]lineno | function) [, condition]]

Temporary breakpoint, which is removed automatically when it is
   first hit. The arguments are the same as for "break".

cl(ear) [filename:lineno | bpnumber [bpnumber ...]]

With a *filename:lineno* argument, clear all the breakpoints at
   this line. With a space separated list of breakpoint numbers, clear
   those breakpoints. Without argument, clear all breaks (but first
   ask confirmation).

disable [bpnumber [bpnumber ...]]

Disable the breakpoints given as a space separated list of
   breakpoint numbers.  Disabling a breakpoint means it cannot cause
   the program to stop execution, but unlike clearing a breakpoint, it
   remains in the list of breakpoints and can be (re-)enabled.

enable [bpnumber [bpnumber ...]]

Enable the breakpoints specified.

ignore bpnumber [count]

Set the ignore count for the given breakpoint number.  If count is
   omitted, the ignore count is set to 0.  A breakpoint becomes active
   when the ignore count is zero.  When non-zero, the count is
   decremented each time the breakpoint is reached and the breakpoint
   is not disabled and any associated condition evaluates to true.

condition bpnumber [condition]

Set a new *condition* for the breakpoint, an expression which must
   evaluate to true before the breakpoint is honored.  If *condition*
   is absent, any existing condition is removed; i.e., the breakpoint
   is made unconditional.

commands [bpnumber]

Specify a list of commands for breakpoint number *bpnumber*.  The
   commands themselves appear on the following lines.  Type a line
   containing just "end" to terminate the commands. An example:

(Pdb) commands 1
      (com) p some_variable
      (com) end
      (Pdb)

To remove all commands from a breakpoint, type commands and follow
   it immediately with "end"; that is, give no commands.

With no *bpnumber* argument, commands refers to the last breakpoint
   set.

You can use breakpoint commands to start your program up again.
   Simply use the continue command, or step, or any other command that
   resumes execution.

Specifying any command resuming execution (currently continue,
   step, next, return, jump, quit and their abbreviations) terminates
   the command list (as if that command was immediately followed by
   end). This is because any time you resume execution (even with a
   simple next or step), you may encounter another breakpoint—which
   could have its own command list, leading to ambiguities about which
   list to execute.

If you use the ‘silent’ command in the command list, the usual
   message about stopping at a breakpoint is not printed.  This may be
   desirable for breakpoints that are to print a specific message and
   then continue.  If none of the other commands print anything, you
   see no sign that the breakpoint was reached.

s(tep)

Execute the current line, stop at the first possible occasion
   (either in a function that is called or on the next line in the
   current function).

n(ext)

Continue execution until the next line in the current function is
   reached or it returns.  (The difference between "next" and "step"
   is that "step" stops inside a called function, while "next"
   executes called functions at (nearly) full speed, only stopping at
   the next line in the current function.)

unt(il) [lineno]

Without argument, continue execution until the line with a number
   greater than the current one is reached.

With a line number, continue execution until a line with a number
   greater or equal to that is reached.  In both cases, also stop when
   the current frame returns.

Changed in version 3.2: Allow giving an explicit line number.

r(eturn)

Continue execution until the current function returns.

c(ont(inue))

Continue execution, only stop when a breakpoint is encountered.

j(ump) lineno

Set the next line that will be executed.  Only available in the
   bottom-most frame.  This lets you jump back and execute code again,
   or jump forward to skip code that you don’t want to run.

It should be noted that not all jumps are allowed – for instance it
   is not possible to jump into the middle of a "for" loop or out of a
   "finally" clause.

l(ist) [first[, last]]

List source code for the current file.  Without arguments, list 11
   lines around the current line or continue the previous listing.
   With "." as argument, list 11 lines around the current line.  With
   one argument, list 11 lines around at that line.  With two
   arguments, list the given range; if the second argument is less
   than the first, it is interpreted as a count.

The current line in the current frame is indicated by "->".  If an
   exception is being debugged, the line where the exception was
   originally raised or propagated is indicated by ">>", if it differs
   from the current line.

New in version 3.2: The ">>" marker.

ll | longlist

List all source code for the current function or frame.
   Interesting lines are marked as for "list".

New in version 3.2.

a(rgs)

Print the argument list of the current function.

p expression

Evaluate the *expression* in the current context and print its
   value.

Note: "print()" can also be used, but is not a debugger command —
     this executes the Python "print()" function.

pp expression

Like the "p" command, except the value of the expression is pretty-
   printed using the "pprint" module.

whatis expression

Print the type of the *expression*.

source expression

Try to get source code for the given object and display it.

New in version 3.2.

display [expression]

Display the value of the expression if it changed, each time
   execution stops in the current frame.

Without expression, list all display expressions for the current
   frame.

New in version 3.2.

undisplay [expression]

Do not display the expression any more in the current frame.
   Without expression, clear all display expressions for the current
   frame.

New in version 3.2.

interact

Start an interactive interpreter (using the "code" module) whose
   global namespace contains all the (global and local) names found in
   the current scope.

New in version 3.2.

alias [name [command]]

Create an alias called *name* that executes *command*.  The command
   must *not* be enclosed in quotes.  Replaceable parameters can be
   indicated by "%1", "%2", and so on, while "%*" is replaced by all
   the parameters. If no command is given, the current alias for
   *name* is shown. If no arguments are given, all aliases are listed.

Aliases may be nested and can contain anything that can be legally
   typed at the pdb prompt.  Note that internal pdb commands *can* be
   overridden by aliases.  Such a command is then hidden until the
   alias is removed.  Aliasing is recursively applied to the first
   word of the command line; all other words in the line are left
   alone.

As an example, here are two useful aliases (especially when placed
   in the ".pdbrc" file):

# Print instance variables (usage "pi classInst")
      alias pi for k in %1.__dict__.keys(): print("%1.",k,"=",%1.__dict__[k])
      # Print instance variables in self
      alias ps pi self

unalias name

Delete the specified alias.

! statement

Execute the (one-line) *statement* in the context of the current
   stack frame. The exclamation point can be omitted unless the first
   word of the statement resembles a debugger command.  To set a
   global variable, you can prefix the assignment command with a
   "global" statement on the same line, e.g.:

(Pdb) global list_options; list_options = ['-l']
      (Pdb)

run [args ...]
restart [args ...]

Restart the debugged Python program.  If an argument is supplied,
   it is split with "shlex" and the result is used as the new
   "sys.argv". History, breakpoints, actions and debugger options are
   preserved. "restart" is an alias for "run".

q(uit)

Quit from the debugger.  The program being executed is aborted.

-[ Footnotes ]-

[1] Whether a frame is considered to originate in a certain module
    is determined by the "__name__" in the frame globals.
�dela���The "del" statement
*******************

del_stmt ::= "del" target_list

Deletion is recursively defined very similar to the way assignment is
defined. Rather than spelling it out in full details, here are some
hints.

Deletion of a target list recursively deletes each target, from left
to right.

Deletion of a name removes the binding of that name from the local or
global namespace, depending on whether the name occurs in a "global"
statement in the same code block.  If the name is unbound, a
"NameError" exception will be raised.

Deletion of attribute references, subscriptions and slicings is passed
to the primary object involved; deletion of a slicing is in general
equivalent to assignment of an empty slice of the right type (but even
this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name
from the local namespace if it occurs as a free variable in a nested
block.
�dictu��Dictionary displays
*******************

A dictionary display is a possibly empty series of key/datum pairs
enclosed in curly braces:

dict_display       ::= "{" [key_datum_list | dict_comprehension] "}"
   key_datum_list     ::= key_datum ("," key_datum)* [","]
   key_datum          ::= expression ":" expression | "**" or_expr
   dict_comprehension ::= expression ":" expression comp_for

A dictionary display yields a new dictionary object.

If a comma-separated sequence of key/datum pairs is given, they are
evaluated from left to right to define the entries of the dictionary:
each key object is used as a key into the dictionary to store the
corresponding datum.  This means that you can specify the same key
multiple times in the key/datum list, and the final dictionary’s value
for that key will be the last one given.

A double asterisk "**" denotes *dictionary unpacking*. Its operand
must be a *mapping*.  Each mapping item is added to the new
dictionary.  Later values replace values already set by earlier
key/datum pairs and earlier dictionary unpackings.

New in version 3.5: Unpacking into dictionary displays, originally
proposed by **PEP 448**.

A dict comprehension, in contrast to list and set comprehensions,
needs two expressions separated with a colon followed by the usual
“for” and “if” clauses. When the comprehension is run, the resulting
key and value elements are inserted in the new dictionary in the order
they are produced.

Restrictions on the types of the key values are listed earlier in
section The standard type hierarchy.  (To summarize, the key type
should be *hashable*, which excludes all mutable objects.)  Clashes
between duplicate keys are not detected; the last datum (textually
rightmost in the display) stored for a given key value prevails.
zdynamic-featuresa���Interaction with dynamic features
*********************************

Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:

i = 10
   def f():
       print(i)
   i = 42
   f()

The "eval()" and "exec()" functions do not have access to the full
environment for resolving names.  Names may be resolved in the local
and global namespaces of the caller.  Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace.  [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace.  If only one namespace is
specified, it is used for both.
�elseaD��The "if" statement
******************

The "if" statement is used for conditional execution:

if_stmt ::= "if" expression ":" suite
               ( "elif" expression ":" suite )*
               ["else" ":" suite]

It selects exactly one of the suites by evaluating the expressions one
by one until one is found to be true (see section Boolean operations
for the definition of true and false); then that suite is executed
(and no other part of the "if" statement is executed or evaluated).
If all expressions are false, the suite of the "else" clause, if
present, is executed.
�
exceptionsu���Exceptions
**********

Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions.  An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.

The Python interpreter raises an exception when it detects a run-time
error (such as division by zero).  A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement.  The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.

Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).

When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop.  In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".

Exceptions are identified by class instances.  The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof.  The instance can be
received by the handler and can carry additional information about the
exceptional condition.

Note: Exception messages are not part of the Python API.  Their
  contents may change from one version of Python to the next without
  warning and should not be relied on by code which will run under
  multiple versions of the interpreter.

See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.

-[ Footnotes ]-

[1] This limitation occurs because the code that is executed by
    these operations is not available at the time the module is
    compiled.
Z	execmodeluq$��Execution model
***************

Structure of a program
======================

A Python program is constructed from code blocks. A *block* is a piece
of Python program text that is executed as a unit. The following are
blocks: a module, a function body, and a class definition. Each
command typed interactively is a block.  A script file (a file given
as standard input to the interpreter or specified as a command line
argument to the interpreter) is a code block.  A script command (a
command specified on the interpreter command line with the ‘**-c**’
option) is a code block.  The string argument passed to the built-in
functions "eval()" and "exec()" is a code block.

A code block is executed in an *execution frame*.  A frame contains
some administrative information (used for debugging) and determines
where and how execution continues after the code block’s execution has
completed.

Naming and binding
==================

Binding of names
----------------

*Names* refer to objects.  Names are introduced by name binding
operations.

The following constructs bind names: formal parameters to functions,
"import" statements, class and function definitions (these bind the
class or function name in the defining block), and targets that are
identifiers if occurring in an assignment, "for" loop header, or after
"as" in a "with" statement or "except" clause. The "import" statement
of the form "from ... import *" binds all names defined in the
imported module, except those beginning with an underscore.  This form
may only be used at the module level.

A target occurring in a "del" statement is also considered bound for
this purpose (though the actual semantics are to unbind the name).

Each assignment or import statement occurs within a block defined by a
class or function definition or at the module level (the top-level
code block).

If a name is bound in a block, it is a local variable of that block,
unless declared as "nonlocal" or "global".  If a name is bound at the
module level, it is a global variable.  (The variables of the module
code block are local and global.)  If a variable is used in a code
block but not defined there, it is a *free variable*.

Each occurrence of a name in the program text refers to the *binding*
of that name established by the following name resolution rules.

Resolution of names
-------------------

A *scope* defines the visibility of a name within a block.  If a local
variable is defined in a block, its scope includes that block.  If the
definition occurs in a function block, the scope extends to any blocks
contained within the defining one, unless a contained block introduces
a different binding for the name.

When a name is used in a code block, it is resolved using the nearest
enclosing scope.  The set of all such scopes visible to a code block
is called the block’s *environment*.

When a name is not found at all, a "NameError" exception is raised. If
the current scope is a function scope, and the name refers to a local
variable that has not yet been bound to a value at the point where the
name is used, an "UnboundLocalError" exception is raised.
"UnboundLocalError" is a subclass of "NameError".

If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block.  This can lead to errors when a name is used within a
block before it is bound.  This rule is subtle.  Python lacks
declarations and allows name binding operations to occur anywhere
within a code block.  The local variables of a code block can be
determined by scanning the entire text of the block for name binding
operations.

If the "global" statement occurs within a block, all uses of the name
specified in the statement refer to the binding of that name in the
top-level namespace.  Names are resolved in the top-level namespace by
searching the global namespace, i.e. the namespace of the module
containing the code block, and the builtins namespace, the namespace
of the module "builtins".  The global namespace is searched first.  If
the name is not found there, the builtins namespace is searched.  The
"global" statement must precede all uses of the name.

The "global" statement has the same scope as a name binding operation
in the same block.  If the nearest enclosing scope for a free variable
contains a global statement, the free variable is treated as a global.

The "nonlocal" statement causes corresponding names to refer to
previously bound variables in the nearest enclosing function scope.
"SyntaxError" is raised at compile time if the given name does not
exist in any enclosing function scope.

The namespace for a module is automatically created the first time a
module is imported.  The main module for a script is always called
"__main__".

Class definition blocks and arguments to "exec()" and "eval()" are
special in the context of name resolution. A class definition is an
executable statement that may use and define names. These references
follow the normal rules for name resolution with an exception that
unbound local variables are looked up in the global namespace. The
namespace of the class definition becomes the attribute dictionary of
the class. The scope of names defined in a class block is limited to
the class block; it does not extend to the code blocks of methods –
this includes comprehensions and generator expressions since they are
implemented using a function scope.  This means that the following
will fail:

class A:
       a = 42
       b = list(a + i for i in range(10))

Builtins and restricted execution
---------------------------------

The builtins namespace associated with the execution of a code block
is actually found by looking up the name "__builtins__" in its global
namespace; this should be a dictionary or a module (in the latter case
the module’s dictionary is used).  By default, when in the "__main__"
module, "__builtins__" is the built-in module "builtins"; when in any
other module, "__builtins__" is an alias for the dictionary of the
"builtins" module itself.  "__builtins__" can be set to a user-created
dictionary to create a weak form of restricted execution.

**CPython implementation detail:** Users should not touch
"__builtins__"; it is strictly an implementation detail.  Users
wanting to override values in the builtins namespace should "import"
the "builtins" module and modify its attributes appropriately.

Interaction with dynamic features
---------------------------------

Name resolution of free variables occurs at runtime, not at compile
time. This means that the following code will print 42:

i = 10
   def f():
       print(i)
   i = 42
   f()

The "eval()" and "exec()" functions do not have access to the full
environment for resolving names.  Names may be resolved in the local
and global namespaces of the caller.  Free variables are not resolved
in the nearest enclosing namespace, but in the global namespace.  [1]
The "exec()" and "eval()" functions have optional arguments to
override the global and local namespace.  If only one namespace is
specified, it is used for both.

Exceptions
==========

Exceptions are a means of breaking out of the normal flow of control
of a code block in order to handle errors or other exceptional
conditions.  An exception is *raised* at the point where the error is
detected; it may be *handled* by the surrounding code block or by any
code block that directly or indirectly invoked the code block where
the error occurred.

The Python interpreter raises an exception when it detects a run-time
error (such as division by zero).  A Python program can also
explicitly raise an exception with the "raise" statement. Exception
handlers are specified with the "try" … "except" statement.  The
"finally" clause of such a statement can be used to specify cleanup
code which does not handle the exception, but is executed whether an
exception occurred or not in the preceding code.

Python uses the “termination” model of error handling: an exception
handler can find out what happened and continue execution at an outer
level, but it cannot repair the cause of the error and retry the
failing operation (except by re-entering the offending piece of code
from the top).

When an exception is not handled at all, the interpreter terminates
execution of the program, or returns to its interactive main loop.  In
either case, it prints a stack backtrace, except when the exception is
"SystemExit".

Exceptions are identified by class instances.  The "except" clause is
selected depending on the class of the instance: it must reference the
class of the instance or a base class thereof.  The instance can be
received by the handler and can carry additional information about the
exceptional condition.

Note: Exception messages are not part of the Python API.  Their
  contents may change from one version of Python to the next without
  warning and should not be relied on by code which will run under
  multiple versions of the interpreter.

See also the description of the "try" statement in section The try
statement and "raise" statement in section The raise statement.

-[ Footnotes ]-

[1] This limitation occurs because the code that is executed by
    these operations is not available at the time the module is
    compiled.
Z	exprlistsuu��Expression lists
****************

expression_list    ::= expression ( "," expression )* [","]
   starred_list       ::= starred_item ( "," starred_item )* [","]
   starred_expression ::= expression | ( starred_item "," )* [starred_item]
   starred_item       ::= expression | "*" or_expr

Except when part of a list or set display, an expression list
containing at least one comma yields a tuple.  The length of the tuple
is the number of expressions in the list.  The expressions are
evaluated from left to right.

An asterisk "*" denotes *iterable unpacking*.  Its operand must be an
*iterable*.  The iterable is expanded into a sequence of items, which
are included in the new tuple, list, or set, at the site of the
unpacking.

New in version 3.5: Iterable unpacking in expression lists, originally
proposed by **PEP 448**.

The trailing comma is required only to create a single tuple (a.k.a. a
*singleton*); it is optional in all other cases.  A single expression
without a trailing comma doesn’t create a tuple, but rather yields the
value of that expression. (To create an empty tuple, use an empty pair
of parentheses: "()".)
Zfloatingac��Floating point literals
***********************

Floating point literals are described by the following lexical
definitions:

floatnumber   ::= pointfloat | exponentfloat
   pointfloat    ::= [intpart] fraction | intpart "."
   exponentfloat ::= (intpart | pointfloat) exponent
   intpart       ::= digit+
   fraction      ::= "." digit+
   exponent      ::= ("e" | "E") ["+" | "-"] digit+

Note that the integer and exponent parts are always interpreted using
radix 10. For example, "077e010" is legal, and denotes the same number
as "77e10". The allowed range of floating point literals is
implementation-dependent. Some examples of floating point literals:

3.14    10.    .001    1e100    3.14e-10    0e0

Note that numeric literals do not include a sign; a phrase like "-1"
is actually an expression composed of the unary operator "-" and the
literal "1".
�foru�
��The "for" statement
*******************