26.1. "typing" --- 类型标注支持
*******************************

3.5 新版功能.

**源码：** Lib/typing.py

注解: typing模块 *暂时* 包含在标准库中。如果核心开发人员认为有必要，
  可能会 添加新功能，甚至可能会在次要版本之间改变API。

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

此模块支持 **PEP 484** 和 **PEP 526** 指定的类型提示。最基本的支持由
"Any"，"Union"，"Tuple"，"Callable"，"TypeVar" 和 "Generic" 类型组成。
有关完整的规范，请参阅 **PEP 484**。有关类型提示的简单介绍，请参阅
**PEP 483**。

函数接受并返回一个字符串，注释像下面这样:

   def greeting(name: str) -> str:
       return 'Hello ' + name

在函数 "greeting" 中，参数 "name" 预期是 "str" 类型，并且返回 "str" 类
型。子类型允许作为参数。


26.1.1. 类型别名
================

类型别名通过将类型分配给别名来定义。在这个例子中， "Vector" 和
"List[float]" 将被视为可互换的同义词:

   from typing import List
   Vector = List[float]

   def scale(scalar: float, vector: Vector) -> Vector:
       return [scalar * num for num in vector]

   # typechecks; a list of floats qualifies as a Vector.
   new_vector = scale(2.0, [1.0, -4.2, 5.4])

类型别名可用于简化复杂类型签名。例如:

   from typing import Dict, Tuple, List

   ConnectionOptions = Dict[str, str]
   Address = Tuple[str, int]
   Server = Tuple[Address, ConnectionOptions]

   def broadcast_message(message: str, servers: List[Server]) -> None:
       ...

   # The static type checker will treat the previous type signature as
   # being exactly equivalent to this one.
   def broadcast_message(
           message: str,
           servers: List[Tuple[Tuple[str, int], Dict[str, str]]]) -> None:
       ...

请注意，"None" 作为类型提示是一种特殊情况，并且由 "type(None)" 取代。


26.1.2. NewType
===============

使用 "NewType()" 辅助函数创建不同的类型:

   from typing import NewType

   UserId = NewType('UserId', int)
   some_id = UserId(524313)

静态类型检查器会将新类型视为它是原始类型的子类。这对于帮助捕捉逻辑错误
非常有用:

   def get_user_name(user_id: UserId) -> str:
       ...

   # typechecks
   user_a = get_user_name(UserId(42351))

   # does not typecheck; an int is not a UserId
   user_b = get_user_name(-1)

您仍然可以对 "UserId" 类型的变量执行所有的 "int" 支持的操作，但结果将
始终为 "int" 类型。这可以让你在需要 "int" 的地方传入 "UserId"，但会阻
止你以无效的方式无意中创建 "UserId":

   # 'output' is of type 'int', not 'UserId'
   output = UserId(23413) + UserId(54341)

请注意，这些检查仅通过静态类型检查程序强制执行。在运行时，"Derived =
NewType('Derived'，Base)" 将 "Derived" 一个函数，该函数立即返回您传递
它的任何参数。这意味着表达式 "Derived(some_value)" 不会创建一个新的类
或引入任何超出常规函数调用的开销。

更确切地说，表达式 "some_value is Derived(some_value)" 在运行时总是为
真。

这也意味着无法创建 "Derived" 的子类型，因为它是运行时的标识函数，而不
是实际的类型:

   from typing import NewType

   UserId = NewType('UserId', int)

   # Fails at runtime and does not typecheck
   class AdminUserId(UserId): pass

但是，可以基于'derived' "NewType" 创建 "NewType()"

   from typing import NewType

   UserId = NewType('UserId', int)

   ProUserId = NewType('ProUserId', UserId)

并且 "ProUserId" 的类型检查将按预期工作。

有关更多详细信息，请参阅 **PEP 484**。

注解: 回想一下，使用类型别名声明两种类型彼此 *等效* 。"Alias =
  Original" 将使静态类型检查对待所有情况下 "Alias" *完全等同于*
  "Original"。当您 想简化复杂类型签名时，这很有用。相反，"NewType" 声
  明一种类型是另一种 类型的子类型。"Derived = NewType('Derived',
  Original)" 将使静态类型 检查器将 "Derived" 当作 "Original" 的 *子类*
  ，这意味着 "Original" 类型的值不能用于 "Derived" 类型的值需要的地方
  。当您想以最小的运行时 间成本防止逻辑错误时，这非常有用。

3.5.2 新版功能.


26.1.3. Callable
================

期望特定签名的回调函数的框架可以将类型标注为 "Callable[[Arg1Type,
Arg2Type], ReturnType]"。

例如:

   from typing import Callable

   def feeder(get_next_item: Callable[[], str]) -> None:
       # Body

   def async_query(on_success: Callable[[int], None],
                   on_error: Callable[[int, Exception], None]) -> None:
       # Body

通过用文字省略号替换类型提示中的参数列表： "Callable[...，ReturnType]"
，可以声明可调用的返回类型，而无需指定调用签名。


26.1.4. 泛型(Generics)
======================

由于无法以通用方式静态推断有关保存在容器中的对象的类型信息，因此抽象基
类已扩展为支持订阅以表示容器元素的预期类型。

   from typing import Mapping, Sequence

   def notify_by_email(employees: Sequence[Employee],
                       overrides: Mapping[str, str]) -> None: ...

Generics can be parameterized by using a new factory available in
typing called "TypeVar".

   from typing import Sequence, TypeVar

   T = TypeVar('T')      # Declare type variable

   def first(l: Sequence[T]) -> T:   # Generic function
       return l[0]


26.1.5. 用户定义的泛型类型
==========================

用户定义的类可以定义为泛型类。

   from typing import TypeVar, Generic
   from logging import Logger

   T = TypeVar('T')

   class LoggedVar(Generic[T]):
       def __init__(self, value: T, name: str, logger: Logger) -> None:
           self.name = name
           self.logger = logger
           self.value = value

       def set(self, new: T) -> None:
           self.log('Set ' + repr(self.value))
           self.value = new

       def get(self) -> T:
           self.log('Get ' + repr(self.value))
           return self.value

       def log(self, message: str) -> None:
           self.logger.info('%s: %s', self.name, message)

"Generic[T]" 作为基类定义了类 "LoggedVar" 采用单个类型参数 "T"。这也使
得 "T" 作为类体内的一个类型有效。

"Generic" 基类使用定义了 "__getitem__()" 的元类，以便 "LoggedVar[t]"
作为类型有效:

   from typing import Iterable

   def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
       for var in vars:
           var.set(0)

泛型类型可以有任意数量的类型变量，并且类型变量可能会受到限制:

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')
   S = TypeVar('S', int, str)

   class StrangePair(Generic[T, S]):
       ...

"Generic" 每个参数的类型变量必须是不同的。这是无效的:

   from typing import TypeVar, Generic
   ...

   T = TypeVar('T')

   class Pair(Generic[T, T]):   # INVALID
       ...

您可以对 "Generic" 使用多重继承:

   from typing import TypeVar, Generic, Sized

   T = TypeVar('T')

   class LinkedList(Sized, Generic[T]):
       ...

从泛型类继承时，某些类型变量可能是固定的:

   from typing import TypeVar, Mapping

   T = TypeVar('T')

   class MyDict(Mapping[str, T]):
       ...

在这种情况下，"MyDict" 只有一个参数，"T"。

在不指定类型参数的情况下使用泛型类别会为每个位置假设 "Any"。在下面的例
子中，"MyIterable" 不是泛型，但是隐式继承自 "Iterable[Any]":

   from typing import Iterable

   class MyIterable(Iterable): # Same as Iterable[Any]

用户定义的通用类型别名也受支持。例子:

   from typing import TypeVar, Iterable, Tuple, Union
   S = TypeVar('S')
   Response = Union[Iterable[S], int]

   # Return type here is same as Union[Iterable[str], int]
   def response(query: str) -> Response[str]:
       ...

   T = TypeVar('T', int, float, complex)
   Vec = Iterable[Tuple[T, T]]

   def inproduct(v: Vec[T]) -> T: # Same as Iterable[Tuple[T, T]]
       return sum(x*y for x, y in v)

"Generic" 使用的元类是 "abc.ABCMeta" 的子类。泛型类可以通过包含抽象方
法或属性成为ABC，并且泛型类也可以使用ABCs作为基类而不存在元类冲突。不
支持泛型元类。参数化泛型的结果被缓存，并且typing模块中的大部分类型都是
可散列的，并且可以比较是否相等。


26.1.6. "Any" 类型
==================

A special kind of type is "Any". A static type checker will treat
every type as being compatible with "Any" and "Any" as being
compatible with every type.

This means that it is possible to perform any operation or method call
on a value of type on "Any" and assign it to any variable:

   from typing import Any

   a = None    # type: Any
   a = []      # OK
   a = 2       # OK

   s = ''      # type: str
   s = a       # OK

   def foo(item: Any) -> int:
       # Typechecks; 'item' could be any type,
       # and that type might have a 'bar' method
       item.bar()
       ...

Notice that no typechecking is performed when assigning a value of
type "Any" to a more precise type. For example, the static type
checker did not report an error when assigning "a" to "s" even though
"s" was declared to be of type "str" and receives an "int" value at
runtime!

Furthermore, all functions without a return type or parameter types
will implicitly default to using "Any":

   def legacy_parser(text):
       ...
       return data

   # A static type checker will treat the above
   # as having the same signature as:
   def legacy_parser(text: Any) -> Any:
       ...
       return data

This behavior allows "Any" to be used as an *escape hatch* when you
need to mix dynamically and statically typed code.

Contrast the behavior of "Any" with the behavior of "object". Similar
to "Any", every type is a subtype of "object". However, unlike "Any",
the reverse is not true: "object" is *not* a subtype of every other
type.

That means when the type of a value is "object", a type checker will
reject almost all operations on it, and assigning it to a variable (or
using it as a return value) of a more specialized type is a type
error. For example:

   def hash_a(item: object) -> int:
       # Fails; an object does not have a 'magic' method.
       item.magic()
       ...

   def hash_b(item: Any) -> int:
       # Typechecks
       item.magic()
       ...

   # Typechecks, since ints and strs are subclasses of object
   hash_a(42)
   hash_a("foo")

   # Typechecks, since Any is compatible with all types
   hash_b(42)
   hash_b("foo")

Use "object" to indicate that a value could be any type in a typesafe
manner. Use "Any" to indicate that a value is dynamically typed.


26.1.7. Classes, functions, and decorators
==========================================

The module defines the following classes, functions and decorators:

class typing.TypeVar

   Type variable.

   用法:

      T = TypeVar('T')  # Can be anything
      A = TypeVar('A', str, bytes)  # Must be str or bytes

   Type variables exist primarily for the benefit of static type
   checkers.  They serve as the parameters for generic types as well
   as for generic function definitions.  See class Generic for more
   information on generic types.  Generic functions work as follows:

      def repeat(x: T, n: int) -> Sequence[T]:
          """Return a list containing n references to x."""
          return [x]*n

      def longest(x: A, y: A) -> A:
          """Return the longest of two strings."""
          return x if len(x) >= len(y) else y

   The latter example's signature is essentially the overloading of
   "(str, str) -> str" and "(bytes, bytes) -> bytes".  Also note that
   if the arguments are instances of some subclass of "str", the
   return type is still plain "str".

   At runtime, "isinstance(x, T)" will raise "TypeError".  In general,
   "isinstance()" and "issubclass()" should not be used with types.

   Type variables may be marked covariant or contravariant by passing
   "covariant=True" or "contravariant=True".  See **PEP 484** for more
   details.  By default type variables are invariant.  Alternatively,
   a type variable may specify an upper bound using "bound=<type>".
   This means that an actual type substituted (explicitly or
   implicitly) for the type variable must be a subclass of the
   boundary type, see **PEP 484**.

class typing.Generic

   Abstract base class for generic types.

   A generic type is typically declared by inheriting from an
   instantiation of this class with one or more type variables. For
   example, a generic mapping type might be defined as:

      class Mapping(Generic[KT, VT]):
          def __getitem__(self, key: KT) -> VT:
              ...
              # Etc.

   This class can then be used as follows:

      X = TypeVar('X')
      Y = TypeVar('Y')

      def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
          try:
              return mapping[key]
          except KeyError:
              return default

class typing.Type(Generic[CT_co])

   A variable annotated with "C" may accept a value of type "C". In
   contrast, a variable annotated with "Type[C]" may accept values
   that are classes themselves -- specifically, it will accept the
   *class object* of "C". For example:

      a = 3         # Has type 'int'
      b = int       # Has type 'Type[int]'
      c = type(a)   # Also has type 'Type[int]'

   Note that "Type[C]" is covariant:

      class User: ...
      class BasicUser(User): ...
      class ProUser(User): ...
      class TeamUser(User): ...

      # Accepts User, BasicUser, ProUser, TeamUser, ...
      def make_new_user(user_class: Type[User]) -> User:
          # ...
          return user_class()

   The fact that "Type[C]" is covariant implies that all subclasses of
   "C" should implement the same constructor signature and class
   method signatures as "C". The type checker should flag violations
   of this, but should also allow constructor calls in subclasses that
   match the constructor calls in the indicated base class. How the
   type checker is required to handle this particular case may change
   in future revisions of **PEP 484**.

   The only legal parameters for "Type" are classes, "Any", type
   variables, and unions of any of these types. For example:

      def new_non_team_user(user_class: Type[Union[BaseUser, ProUser]]): ...

   "Type[Any]" is equivalent to "Type" which in turn is equivalent to
   "type", which is the root of Python's metaclass hierarchy.

   3.5.2 新版功能.

class typing.Iterable(Generic[T_co])

   A generic version of "collections.abc.Iterable".

class typing.Iterator(Iterable[T_co])

   A generic version of "collections.abc.Iterator".

class typing.Reversible(Iterable[T_co])

   A generic version of "collections.abc.Reversible".

class typing.SupportsInt

   An ABC with one abstract method "__int__".

class typing.SupportsFloat

   An ABC with one abstract method "__float__".

class typing.SupportsComplex

   An ABC with one abstract method "__complex__".

class typing.SupportsBytes

   An ABC with one abstract method "__bytes__".

class typing.SupportsAbs

   An ABC with one abstract method "__abs__" that is covariant in its
   return type.

class typing.SupportsRound

   An ABC with one abstract method "__round__" that is covariant in
   its return type.

class typing.Container(Generic[T_co])

   A generic version of "collections.abc.Container".

class typing.Hashable

   An alias to "collections.abc.Hashable"

class typing.Sized

   An alias to "collections.abc.Sized"

class typing.Collection(Sized, Iterable[T_co], Container[T_co])

   A generic version of "collections.abc.Collection"

   3.6 新版功能.

class typing.AbstractSet(Sized, Collection[T_co])

   A generic version of "collections.abc.Set".

class typing.MutableSet(AbstractSet[T])

   A generic version of "collections.abc.MutableSet".

class typing.Mapping(Sized, Collection[KT], Generic[VT_co])

   A generic version of "collections.abc.Mapping".

class typing.MutableMapping(Mapping[KT, VT])

   A generic version of "collections.abc.MutableMapping".

class typing.Sequence(Reversible[T_co], Collection[T_co])

   A generic version of "collections.abc.Sequence".

class typing.MutableSequence(Sequence[T])

   A generic version of "collections.abc.MutableSequence".

class typing.ByteString(Sequence[int])

   A generic version of "collections.abc.ByteString".

   This type represents the types "bytes", "bytearray", and
   "memoryview".

   As a shorthand for this type, "bytes" can be used to annotate
   arguments of any of the types mentioned above.

class typing.Deque(deque, MutableSequence[T])

   A generic version of "collections.deque".

   3.6.1 新版功能.

class typing.List(list, MutableSequence[T])

   Generic version of "list". Useful for annotating return types. To
   annotate arguments it is preferred to use abstract collection types
   such as "Mapping", "Sequence", or "AbstractSet".

   This type may be used as follows:

      T = TypeVar('T', int, float)

      def vec2(x: T, y: T) -> List[T]:
          return [x, y]

      def keep_positives(vector: Sequence[T]) -> List[T]:
          return [item for item in vector if item > 0]

class typing.Set(set, MutableSet[T])

   A generic version of "builtins.set".

class typing.FrozenSet(frozenset, AbstractSet[T_co])

   A generic version of "builtins.frozenset".

class typing.MappingView(Sized, Iterable[T_co])

   A generic version of "collections.abc.MappingView".

class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])

   A generic version of "collections.abc.KeysView".

class typing.ItemsView(MappingView, Generic[KT_co, VT_co])

   A generic version of "collections.abc.ItemsView".

class typing.ValuesView(MappingView[VT_co])

   A generic version of "collections.abc.ValuesView".

class typing.Awaitable(Generic[T_co])

   A generic version of "collections.abc.Awaitable".

class typing.Coroutine(Awaitable[V_co], Generic[T_co T_contra, V_co])

   A generic version of "collections.abc.Coroutine". The variance and
   order of type variables correspond to those of "Generator", for
   example:

      from typing import List, Coroutine
      c = None # type: Coroutine[List[str], str, int]
      ...
      x = c.send('hi') # type: List[str]
      async def bar() -> None:
          x = await c # type: int

class typing.AsyncIterable(Generic[T_co])

   A generic version of "collections.abc.AsyncIterable".

class typing.AsyncIterator(AsyncIterable[T_co])

   A generic version of "collections.abc.AsyncIterator".

class typing.ContextManager(Generic[T_co])

   A generic version of "contextlib.AbstractContextManager".

   3.6 新版功能.

class typing.AsyncContextManager(Generic[T_co])

   An ABC with async abstract "__aenter__()" and "__aexit__()"
   methods.

   3.6 新版功能.

class typing.Dict(dict, MutableMapping[KT, VT])

   A generic version of "dict". The usage of this type is as follows:

      def get_position_in_index(word_list: Dict[str, int], word: str) -> int:
          return word_list[word]

class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])

   A generic version of "collections.defaultdict".

   3.5.2 新版功能.

class typing.Counter(collections.Counter, Dict[T, int])

   A generic version of "collections.Counter".

   3.6.1 新版功能.

class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])

   A generic version of "collections.ChainMap".

   3.6.1 新版功能.

class typing.Generator(Iterator[T_co], Generic[T_co, T_contra, V_co])

   A generator can be annotated by the generic type
   "Generator[YieldType, SendType, ReturnType]". For example:

      def echo_round() -> Generator[int, float, str]:
          sent = yield 0
          while sent >= 0:
              sent = yield round(sent)
          return 'Done'

   Note that unlike many other generics in the typing module, the
   "SendType" of "Generator" behaves contravariantly, not covariantly
   or invariantly.

   If your generator will only yield values, set the "SendType" and
   "ReturnType" to "None":

      def infinite_stream(start: int) -> Generator[int, None, None]:
          while True:
              yield start
              start += 1

   Alternatively, annotate your generator as having a return type of
   either "Iterable[YieldType]" or "Iterator[YieldType]":

      def infinite_stream(start: int) -> Iterator[int]:
          while True:
              yield start
              start += 1

class typing.AsyncGenerator(AsyncIterator[T_co], Generic[T_co, T_contra])

   An async generator can be annotated by the generic type
   "AsyncGenerator[YieldType, SendType]". For example:

      async def echo_round() -> AsyncGenerator[int, float]:
          sent = yield 0
          while sent >= 0.0:
              rounded = await round(sent)
              sent = yield rounded

   Unlike normal generators, async generators cannot return a value,
   so there is no "ReturnType" type parameter. As with "Generator",
   the "SendType" behaves contravariantly.

   If your generator will only yield values, set the "SendType" to
   "None":

      async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
          while True:
              yield start
              start = await increment(start)

   Alternatively, annotate your generator as having a return type of
   either "AsyncIterable[YieldType]" or "AsyncIterator[YieldType]":

      async def infinite_stream(start: int) -> AsyncIterator[int]:
          while True:
              yield start
              start = await increment(start)

   3.5.4 新版功能.

class typing.Text

   "Text" is an alias for "str". It is provided to supply a forward
   compatible path for Python 2 code: in Python 2, "Text" is an alias
   for "unicode".

   Use "Text" to indicate that a value must contain a unicode string
   in a manner that is compatible with both Python 2 and Python 3:

      def add_unicode_checkmark(text: Text) -> Text:
          return text + u' \u2713'

   3.5.2 新版功能.

class typing.io

   Wrapper namespace for I/O stream types.

   This defines the generic type "IO[AnyStr]" and subclasses "TextIO"
   and "BinaryIO", deriving from "IO[str]" and "IO[bytes]",
   respectively. These represent the types of I/O streams such as
   returned by "open()".

   These types are also accessible directly as "typing.IO",
   "typing.TextIO", and "typing.BinaryIO".

class typing.re

   Wrapper namespace for regular expression matching types.

   This defines the type aliases "Pattern" and "Match" which
   correspond to the return types from "re.compile()" and
   "re.match()".  These types (and the corresponding functions) are
   generic in "AnyStr" and can be made specific by writing
   "Pattern[str]", "Pattern[bytes]", "Match[str]", or "Match[bytes]".

   These types are also accessible directly as "typing.Pattern" and
   "typing.Match".

class typing.NamedTuple

   Typed version of namedtuple.

   用法:

      class Employee(NamedTuple):
          name: str
          id: int

   This is equivalent to:

      Employee = collections.namedtuple('Employee', ['name', 'id'])

   To give a field a default value, you can assign to it in the class
   body:

      class Employee(NamedTuple):
          name: str
          id: int = 3

      employee = Employee('Guido')
      assert employee.id == 3

   Fields with a default value must come after any fields without a
   default.

   The resulting class has two extra attributes: "_field_types",
   giving a dict mapping field names to types, and "_field_defaults",
   a dict mapping field names to default values.  (The field names are
   in the "_fields" attribute, which is part of the namedtuple API.)

   "NamedTuple" subclasses can also have docstrings and methods:

      class Employee(NamedTuple):
          """Represents an employee."""
          name: str
          id: int = 3

          def __repr__(self) -> str:
              return f'<Employee {self.name}, id={self.id}>'

   Backward-compatible usage:

      Employee = NamedTuple('Employee', [('name', str), ('id', int)])

   在 3.6 版更改: Added support for **PEP 526** variable annotation
   syntax.

   在 3.6.1 版更改: Added support for default values, methods, and
   docstrings.

typing.NewType(typ)

   A helper function to indicate a distinct types to a typechecker,
   see NewType. At runtime it returns a function that returns its
   argument. Usage:

      UserId = NewType('UserId', int)
      first_user = UserId(1)

   3.5.2 新版功能.

typing.cast(typ, val)

   Cast a value to a type.

   This returns the value unchanged.  To the type checker this signals
   that the return value has the designated type, but at runtime we
   intentionally don't check anything (we want this to be as fast as
   possible).

typing.get_type_hints(obj[, globals[, locals]])

   Return a dictionary containing type hints for a function, method,
   module or class object.

   This is often the same as "obj.__annotations__". In addition,
   forward references encoded as string literals are handled by
   evaluating them in "globals" and "locals" namespaces. If necessary,
   "Optional[t]" is added for function and method annotations if a
   default value equal to "None" is set. For a class "C", return a
   dictionary constructed by merging all the "__annotations__" along
   "C.__mro__" in reverse order.

@typing.overload

   The "@overload" decorator allows describing functions and methods
   that support multiple different combinations of argument types. A
   series of "@overload"-decorated definitions must be followed by
   exactly one non-"@overload"-decorated definition (for the same
   function/method). The "@overload"-decorated definitions are for the
   benefit of the type checker only, since they will be overwritten by
   the non-"@overload"-decorated definition, while the latter is used
   at runtime but should be ignored by a type checker.  At runtime,
   calling a "@overload"-decorated function directly will raise
   "NotImplementedError". An example of overload that gives a more
   precise type than can be expressed using a union or a type
   variable:

      @overload
      def process(response: None) -> None:
          ...
      @overload
      def process(response: int) -> Tuple[int, str]:
          ...
      @overload
      def process(response: bytes) -> str:
          ...
      def process(response):
          <actual implementation>

   See **PEP 484** for details and comparison with other typing
   semantics.

@typing.no_type_check

   Decorator to indicate that annotations are not type hints.

   This works as class or function *decorator*.  With a class, it
   applies recursively to all methods defined in that class (but not
   to methods defined in its superclasses or subclasses).

   This mutates the function(s) in place.

@typing.no_type_check_decorator

   Decorator to give another decorator the "no_type_check()" effect.

   This wraps the decorator with something that wraps the decorated
   function in "no_type_check()".

typing.Any

   Special type indicating an unconstrained type.

   * Every type is compatible with "Any".

   * "Any" is compatible with every type.

typing.NoReturn

   Special type indicating that a function never returns. For example:

      from typing import NoReturn

      def stop() -> NoReturn:
          raise RuntimeError('no way')

   3.6.5 新版功能.

typing.Union

   Union type; "Union[X, Y]" means either X or Y.

   To define a union, use e.g. "Union[int, str]".  Details:

   * The arguments must be types and there must be at least one.

   * Unions of unions are flattened, e.g.:

        Union[Union[int, str], float] == Union[int, str, float]

   * Unions of a single argument vanish, e.g.:

        Union[int] == int  # The constructor actually returns int

   * Redundant arguments are skipped, e.g.:

        Union[int, str, int] == Union[int, str]

   * When comparing unions, the argument order is ignored, e.g.:

        Union[int, str] == Union[str, int]

   * When a class and its subclass are present, the latter is
     skipped, e.g.:

        Union[int, object] == object

   * You cannot subclass or instantiate a union.

   * You cannot write "Union[X][Y]".

   * You can use "Optional[X]" as a shorthand for "Union[X, None]".

typing.Optional

   Optional type.

   "Optional[X]" is equivalent to "Union[X, None]".

   Note that this is not the same concept as an optional argument,
   which is one that has a default.  An optional argument with a
   default does not require the "Optional" qualifier on its type
   annotation just because it is optional. For example:

      def foo(arg: int = 0) -> None:
          ...

   On the other hand, if an explicit value of "None" is allowed, the
   use of "Optional" is appropriate, whether the argument is optional
   or not. For example:

      def foo(arg: Optional[int] = None) -> None:
          ...

typing.Tuple

   Tuple type; "Tuple[X, Y]" is the type of a tuple of two items with
   the first item of type X and the second of type Y.

   Example: "Tuple[T1, T2]" is a tuple of two elements corresponding
   to type variables T1 and T2.  "Tuple[int, float, str]" is a tuple
   of an int, a float and a string.

   To specify a variable-length tuple of homogeneous type, use literal
   ellipsis, e.g. "Tuple[int, ...]". A plain "Tuple" is equivalent to
   "Tuple[Any, ...]", and in turn to "tuple".

typing.Callable

   Callable type; "Callable[[int], str]" is a function of (int) ->
   str.

   The subscription syntax must always be used with exactly two
   values: the argument list and the return type.  The argument list
   must be a list of types or an ellipsis; the return type must be a
   single type.

   There is no syntax to indicate optional or keyword arguments; such
   function types are rarely used as callback types. "Callable[...,
   ReturnType]" (literal ellipsis) can be used to type hint a callable
   taking any number of arguments and returning "ReturnType".  A plain
   "Callable" is equivalent to "Callable[..., Any]", and in turn to
   "collections.abc.Callable".

typing.ClassVar

   Special type construct to mark class variables.

   As introduced in **PEP 526**, a variable annotation wrapped in
   ClassVar indicates that a given attribute is intended to be used as
   a class variable and should not be set on instances of that class.
   Usage:

      class Starship:
          stats: ClassVar[Dict[str, int]] = {} # class variable
          damage: int = 10                     # instance variable

   "ClassVar" accepts only types and cannot be further subscribed.

   "ClassVar" is not a class itself, and should not be used with
   "isinstance()" or "issubclass()". "ClassVar" does not change Python
   runtime behavior, but it can be used by third-party type checkers.
   For example, a type checker might flag the following code as an
   error:

      enterprise_d = Starship(3000)
      enterprise_d.stats = {} # Error, setting class variable on instance
      Starship.stats = {}     # This is OK

   3.5.3 新版功能.

typing.AnyStr

   "AnyStr" is a type variable defined as "AnyStr = TypeVar('AnyStr',
   str, bytes)".

   It is meant to be used for functions that may accept any kind of
   string without allowing different kinds of strings to mix. For
   example:

      def concat(a: AnyStr, b: AnyStr) -> AnyStr:
          return a + b

      concat(u"foo", u"bar")  # Ok, output has type 'unicode'
      concat(b"foo", b"bar")  # Ok, output has type 'bytes'
      concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes

typing.TYPE_CHECKING

   A special constant that is assumed to be "True" by 3rd party static
   type checkers. It is "False" at runtime. Usage:

      if TYPE_CHECKING:
          import expensive_mod

      def fun(arg: 'expensive_mod.SomeType') -> None:
          local_var: expensive_mod.AnotherType = other_fun()

   Note that the first type annotation must be enclosed in quotes,
   making it a "forward reference", to hide the "expensive_mod"
   reference from the interpreter runtime.  Type annotations for local
   variables are not evaluated, so the second annotation does not need
   to be enclosed in quotes.

   3.5.2 新版功能.
