26.1. `typing` — 类型标注支持¶

3.5 新版功能.

This module supports type hints as specified by PEP 484. The most fundamental support consists of the types Any, Union, Tuple, Callable, TypeVar, and Generic. For full specification please see PEP 484. For a simplified introduction to type hints see 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。

This also means that it is not possible to create a subtype of Derived since it is an identity function at runtime, not an actual type. Similarly, it is not possible to create another NewType() based on a Derived type:

from typing import NewType

UserId = NewType('UserId', int)

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

# Also does not typecheck
ProUserId = NewType('ProUserId', UserId)

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。

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(Generic)¶

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 parametrized 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。

The Generic base class uses a metaclass that defines __getitem__() so that LoggedVar[t] is valid as a type:

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)

The metaclass used by Generic is a subclass of abc.ABCMeta. A generic class can be an ABC by including abstract methods or properties, and generic classes can also have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

26.1.6. `Any` 类型¶

Any 是一种特殊的类型。静态类型检查器将所有类型视为与 Any 兼容，反之亦然， Any 也与所有类型相兼容。

这意味着可对类型为 Any 的值执行任何操作或方法调用，并将其赋值给任何变量:

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()
    ...

需要注意的是，将 Any 类型的值赋值给另一个更具体的类型时，Python不会执行类型检查。例如，当把 a 赋值给 s 时，即使 s 被声明为 str 类型，在运行时接收到的是 int 值，静态类型检查器也不会报错。

此外，所有返回值无类型或形参无类型的函数将隐式地默认使用 Any 类型:

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

当需要混用动态类型和静态类型的代码时，上述行为可以让 Any 被用作 应急出口 。

Any 和 object 的行为对比。与 Any 相似，所有的类型都是 object 的子类型。然而不同于 Any，反之并不成立： object 不是其他所有类型的子类型。

这意味着当一个值的类型是 object 的时候，类型检查器会拒绝对它的几乎所有的操作。把它赋值给一个指定了类型的变量（或者当作返回值）是一个类型错误。比如说：

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")

使用 object 示意一个值可以类型安全地兼容任何类型。使用 Any 示意一个值地类型是动态定义的。

26.1.7. 类,函数和修饰器.¶

这个模块定义了如下的类,模块和修饰器.

class typing.TypeVar¶

类型变量

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.

isinstance(x, T) 会在运行时抛出 TypeError 异常。一般地说， isinstance() 和 issubclass() 不应该和类型一起使用。

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.

这个类之后可以被这样用:

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])¶

一个注解为 C 的变量可以接受一个类型为 C 的值。相对地，一个注解为 Type[C] 的变量可以接受本身为类的值 —— 更精确地说它接受 C 的 类对象 ，例如：

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

注意 Type[C] 是协变的：

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()

Type[C] 是协变的这一事实暗示了任何 C 的子类应当实现与 C 相同的构造器签名和类方法签名。类型检查器应当标记违反的情况，但应当也允许子类中调用构造器符合指示的基类。类型检查器被要求如何处理这种情况可能会在 PEP 484 将来的版本中改变。

The only legal parameters for Type are classes, unions of classes, and Any. For example:

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

Type[Any] 等价于 Type，因此继而等价于 type，它是 Python 的元类层级的根部。

class typing.Iterable(Generic[T_co])¶: collections.abc.Iterable 的泛型版本。

class typing.Iterator(Iterable[T_co])¶: collections.abc.Iterator 的泛型版本。

class typing.Reversible(Iterable[T_co])¶: collections.abc.Reversible 的泛型版本。

class typing.SupportsInt¶: An ABC with one abstract method __int__.

class typing.SupportsFloat¶: An ABC with one abstract method __float__.

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])¶: collections.abc.Container 的泛型版本。

class typing.Hashable¶: collections.abc.Hashable 的别名。

class typing.Sized¶: collections.abc.Sized 的别名。

class typing.AbstractSet(Sized, Iterable[T_co], Container[T_co])¶: collections.abc.Set 的泛型版本。

class typing.MutableSet(AbstractSet[T])¶: collections.abc.MutableSet 的泛型版本。

class typing.Mapping(Sized, Iterable[KT], Container[KT], Generic[VT_co])¶: A generic version of collections.abc.Mapping.

class typing.MutableMapping(Mapping[KT, VT])¶: collections.abc.MutableMapping 的泛型版本。

class typing.Sequence(Sized, Iterable[T_co], Container[T_co])¶: collections.abc.Sequence 的泛型版本。

class typing.MutableSequence(Sequence[T])¶: collections.abc.MutableSequence 的泛型版本。

class typing.ByteString(Sequence[int])¶

collections.abc.ByteString 的泛型版本。

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])¶: collections.deque 的泛型版本。

3.5.4 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.

这个类型可以这样用:

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])¶: collections.abc.MappingView 的泛型版本。

class typing.KeysView(MappingView[KT_co], AbstractSet[KT_co])¶: collections.abc.KeysView 的泛型版本。

class typing.ItemsView(MappingView, Generic[KT_co, VT_co])¶: collections.abc.ItemsView 的泛型版本。

class typing.ValuesView(MappingView[VT_co])¶: collections.abc.ValuesView 的泛型版本。

class typing.Awaitable(Generic[T_co])¶: collections.abc.Awaitable 的泛型版本。

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])¶: collections.abc.AsyncIterable 的泛型版本。

class typing.AsyncIterator(AsyncIterable[T_co])¶: collections.abc.AsyncIterator 的泛型版本。

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

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'

class typing.io¶

Wrapper namespace for I/O stream types.

This defines the generic type IO[AnyStr] and aliases TextIO and BinaryIO for respectively IO[str] and IO[bytes]. These representing the types of I/O streams such as returned by open().

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].

typing.NamedTuple(typename, fields)¶

Typed version of namedtuple.

用法:

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

这相当于:

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

The resulting class has one extra attribute: _field_types, giving a dict mapping field names to types. (The field names are in the _fields attribute, which is part of the namedtuple API.)

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)

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]])¶

返回一个字典，字典内含有函数、方法、模块或类对象的类型提示。

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(arg)¶

用于指明标注不是类型提示的装饰器。

The argument must be a class or function; if it is a class, it applies recursively to all methods defined in that class (but not to methods defined in its superclasses or subclasses).

此方法会就地地修改函数。

@typing.no_type_check_decorator(decorator)¶

使其它装饰器起到 no_type_check() 效果的装饰器。

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

typing.Any¶

特殊类型，表明类型没有任何限制。

每一个类型都对 Any 兼容。
Any 对每一个类型都兼容。

typing.Union¶

联合类型； Union[X, Y] 意味着：要不是 X，要不是 Y。

使用形如 Union[int, str] 的形式来定义一个联合类型。细节如下:

参数必须是类型，而且必须至少有一个参数。

联合类型的联合类型会被展开打平，比如:

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

仅有一个参数的联合类型会坍缩成参数自身，比如:
```
Union[int] == int  # The constructor actually returns int
```

多余的参数会被跳过，比如:

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

在比较联合类型的时候，参数顺序会被忽略，比如:
```
Union[int, str] == Union[str, int]
```
When a class and its subclass are present, the latter is skipped, e.g.:
```
Union[int, object] == object
```
你不能继承或者实例化一个联合类型。
你不能写成 Union[X][Y] 。
你可以使用 Optional[X] 作为 Union[X, None] 的缩写。

typing.Optional¶

可选类型。

Optional[X] 等价于 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 needn’t use the Optional qualifier on its type annotation (although it is inferred if the default is None). A mandatory argument may still have an Optional type if an explicit value of None is allowed.

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.

举例： Tuple[T1, T2] 是一个二元组，类型分别为 T1 和 T2。 Tuple[int, float, str] 是一个由整数、浮点数和字符串组成的三元组。

为表达一个同类型元素的变长元组，使用省略号字面量，如 Tuple[int, ...] 。单独的一个 Tuple 等价于 Tuple[Any, ...]，进而等价于 tuple 。

typing.Callable¶

可调用类型； Callable[[int], str] 是一个函数，接受一个 int 参数，返回一个 str 。

下标值的语法必须恰为两个值：参数列表和返回类型。参数列表必须是一个类型和省略号组成的列表；返回值必须是单一一个类型。

不存在语法来表示可选的或关键词参数，这类函数类型罕见用于回调函数。 Callable[..., ReturnType] （使用字面省略号）能被用于提示一个可调用对象，接受任意数量的参数并且返回 ReturnType。单独的 Callable 等价于 Callable[..., Any] ，并且进而等价于 collections.abc.Callable 。

typing.ClassVar¶

特殊的类型构造器，用以标记类变量。

在 PEP 526 中被引入，ClassVar 包裹起来的变量注解指示了给定属性预期用于类变量，并且不应在类的实例上被设置。用法：

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

ClassVar 仅接受类型，并且不能被再次下标。

ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). Note that ClassVar does not change Python runtime behavior; it can be used by 3rd party type checkers, so that the following code might flagged as an error by those:

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.

26.1. `typing` — 类型标注支持¶

26.1.1. 类型别名¶

26.1.2. NewType¶

26.1.3. Callable¶

26.1.4. 泛型(Generic)¶

26.1.5. 用户定义的泛型类型¶

26.1.6. `Any` 类型¶

26.1.7. 类,函数和修饰器.¶

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26.1. typing — 类型标注支持¶

26.1.1. 类型别名¶

26.1.2. NewType¶

26.1.3. Callable¶

26.1.4. 泛型(Generic)¶

26.1.5. 用户定义的泛型类型¶

26.1.6. Any 类型¶

26.1.7. 类,函数和修饰器.¶

26.1. `typing` — 类型标注支持¶

26.1.6. `Any` 类型¶