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# More types

This section introduces a few additional kinds of types, including `NoReturn`, `NewType`, and types for async code. It also discusses how to give functions more precise types using overloads. All of these are only situationally useful, so feel free to skip this section and come back when you have a need for some of them.

Here’s a quick summary of what’s covered here:

- `NoReturn` lets you tell mypy that a function never returns normally.

- `NewType` lets you define a variant of a type that is treated as a separate type by mypy but is identical to the original type at runtime. For example, you can have `UserId` as a variant of `int` that is just an `int` at runtime.

- `@overload` lets you define a function that can accept multiple distinct signatures. This is useful if you need to encode a relationship between the arguments and the return type that would be difficult to express normally.

- Async types let you type check programs using `async` and `await`.

## The NoReturn type

Mypy provides support for functions that never return. For example, a function that unconditionally raises an exception:

    from typing import NoReturn

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

Mypy will ensure that functions annotated as returning `NoReturn` truly never return, either implicitly or explicitly. Mypy will also recognize that the code after calls to such functions is unreachable and will behave accordingly:

    def f(x: int) -> int:
        if x == 0:
            return x
        stop()
        return 'whatever works'  # No error in an unreachable block

In earlier Python versions you need to install `typing_extensions` using pip to use `NoReturn` in your code. Python 3 command line:

    python3 -m pip install --upgrade typing-extensions

## NewTypes

There are situations where you may want to avoid programming errors by creating simple derived classes that are only used to distinguish certain values from base class instances. Example:

    class UserId(int):
        pass

    def get_by_user_id(user_id: UserId):
        ...

However, this approach introduces some runtime overhead. To avoid this, the typing module provides a helper object `NewType` that creates simple unique types with almost zero runtime overhead. Mypy will treat the statement `Derived = NewType('Derived', Base)` as being roughly equivalent to the following definition:

    class Derived(Base):
        def __init__(self, _x: Base) -> None:
            ...

However, at runtime, `NewType('Derived', Base)` will return a dummy callable that simply returns its argument:

    def Derived(_x):
        return _x

Mypy will require explicit casts from `int` where `UserId` is expected, while implicitly casting from `UserId` where `int` is expected. Examples:

    from typing import NewType

    UserId = NewType('UserId', int)

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

    UserId('user')          # Fails type check

    name_by_id(42)          # Fails type check
    name_by_id(UserId(42))  # OK

    num: int = UserId(5) + 1

`NewType` accepts exactly two arguments. The first argument must be a string literal containing the name of the new type and must equal the name of the variable to which the new type is assigned. The second argument must be a properly subclassable class, i.e., not a type construct like `Union`, etc.

The callable returned by `NewType` accepts only one argument; this is equivalent to supporting only one constructor accepting an instance of the base class (see above). Example:

    from typing import NewType

    class PacketId:
        def __init__(self, major: int, minor: int) -> None:
            self._major = major
            self._minor = minor

    TcpPacketId = NewType('TcpPacketId', PacketId)

    packet = PacketId(100, 100)
    tcp_packet = TcpPacketId(packet)  # OK

    tcp_packet = TcpPacketId(127, 0)  # Fails in type checker and at runtime

You cannot use `isinstance()` or `issubclass()` on the object returned by `NewType`, nor can you subclass an object returned by `NewType`.

Note

Unlike type aliases, `NewType` will create an entirely new and unique type when used. The intended purpose of `NewType` is to help you detect cases where you accidentally mixed together the old base type and the new derived type.

For example, the following will successfully typecheck when using type aliases:

    UserId = int

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

    name_by_id(3)  # ints and UserId are synonymous

But a similar example using `NewType` will not typecheck:

    from typing import NewType

    UserId = NewType('UserId', int)

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

    name_by_id(3)  # int is not the same as UserId

## Function overloading

Sometimes the arguments and types in a function depend on each other in ways that can’t be captured with a `Union`. For example, suppose we want to write a function that can accept x-y coordinates. If we pass in just a single x-y coordinate, we return a `ClickEvent` object. However, if we pass in two x-y coordinates, we return a `DragEvent` object.

Our first attempt at writing this function might look like this:

    from typing import Union, Optional

    def mouse_event(x1: int,
                    y1: int,
                    x2: Optional[int] = None,
                    y2: Optional[int] = None) -> Union[ClickEvent, DragEvent]:
        if x2 is None and y2 is None:
            return ClickEvent(x1, y1)
        elif x2 is not None and y2 is not None:
            return DragEvent(x1, y1, x2, y2)
        else:
            raise TypeError("Bad arguments")

While this function signature works, it’s too loose: it implies `mouse_event` could return either object regardless of the number of arguments we pass in. It also does not prohibit a caller from passing in the wrong number of ints: mypy would treat calls like `mouse_event(1, 2, 20)` as being valid, for example.

We can do better by using overloading which lets us give the same function multiple type annotations (signatures) to more accurately describe the function’s behavior:

    from typing import Union, overload

    # Overload *variants* for 'mouse_event'.
    # These variants give extra information to the type checker.
    # They are ignored at runtime.

    @overload
    def mouse_event(x1: int, y1: int) -> ClickEvent: ...
    @overload
    def mouse_event(x1: int, y1: int, x2: int, y2: int) -> DragEvent: ...

    # The actual *implementation* of 'mouse_event'.
    # The implementation contains the actual runtime logic.
    #
    # It may or may not have type hints. If it does, mypy
    # will check the body of the implementation against the
    # type hints.
    #
    # Mypy will also check and make sure the signature is
    # consistent with the provided variants.

    def mouse_event(x1: int,
                    y1: int,
                    x2: Optional[int] = None,
                    y2: Optional[int] = None) -> Union[ClickEvent, DragEvent]:
        if x2 is None and y2 is None:
            return ClickEvent(x1, y1)
        elif x2 is not None and y2 is not None:
            return DragEvent(x1, y1, x2, y2)
        else:
            raise TypeError("Bad arguments")

This allows mypy to understand calls to `mouse_event` much more precisely. For example, mypy will understand that `mouse_event(5, 25)` will always have a return type of `ClickEvent` and will report errors for calls like `mouse_event(5, 25, 2)`.

As another example, suppose we want to write a custom container class that implements the `__getitem__` method (`[]` bracket indexing). If this method receives an integer we return a single item. If it receives a `slice`, we return a `Sequence` of items.

We can precisely encode this relationship between the argument and the return type by using overloads like so:

    from typing import Sequence, TypeVar, Union, overload

    T = TypeVar('T')

    class MyList(Sequence[T]):
        @overload
        def __getitem__(self, index: int) -> T: ...

        @overload
        def __getitem__(self, index: slice) -> Sequence[T]: ...

        def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]:
            if isinstance(index, int):
                # Return a T here
            elif isinstance(index, slice):
                # Return a sequence of Ts here
            else:
                raise TypeError(...)

Note

If you just need to constrain a type variable to certain types or subtypes, you can use a value restriction.

The default values of a function’s arguments don’t affect its signature – only the absence or presence of a default value does. So in order to reduce redundancy, it’s possible to replace default values in overload definitions with `...` as a placeholder:

    from typing import overload

    class M: ...

    @overload
    def get_model(model_or_pk: M, flag: bool = ...) -> M: ...
    @overload
    def get_model(model_or_pk: int, flag: bool = ...) -> M | None: ...

    def get_model(model_or_pk: int | M, flag: bool = True) -> M | None:
        ...

### Runtime behavior

An overloaded function must consist of two or more overload variants followed by an implementation. The variants and the implementations must be adjacent in the code: think of them as one indivisible unit.

The variant bodies must all be empty; only the implementation is allowed to contain code. This is because at runtime, the variants are completely ignored: they’re overridden by the final implementation function.

This means that an overloaded function is still an ordinary Python function! There is no automatic dispatch handling and you must manually handle the different types in the implementation (e.g. by using `if` statements and `isinstance` checks).

If you are adding an overload within a stub file, the implementation function should be omitted: stubs do not contain runtime logic.

Note

While we can leave the variant body empty using the `pass` keyword, the more common convention is to instead use the ellipsis (`...`) literal.

### Type checking calls to overloads

When you call an overloaded function, mypy will infer the correct return type by picking the best matching variant, after taking into consideration both the argument types and arity. However, a call is never type checked against the implementation. This is why mypy will report calls like `mouse_event(5, 25, 3)` as being invalid even though it matches the implementation signature.

If there are multiple equally good matching variants, mypy will select the variant that was defined first. For example, consider the following program:

    # For Python 3.8 and below you must use `typing.List` instead of `list`. e.g.
    # from typing import List
    from typing import overload

    @overload
    def summarize(data: list[int]) -> float: ...

    @overload
    def summarize(data: list[str]) -> str: ...

    def summarize(data):
        if not data:
            return 0.0
        elif isinstance(data[0], int):
            # Do int specific code
        else:
            # Do str-specific code

    # What is the type of 'output'? float or str?
    output = summarize([])

The `summarize([])` call matches both variants: an empty list could be either a `list[int]` or a `list[str]`. In this case, mypy will break the tie by picking the first matching variant: `output` will have an inferred type of `float`. The implementor is responsible for making sure `summarize` breaks ties in the same way at runtime.

However, there are two exceptions to the “pick the first match” rule. First, if multiple variants match due to an argument being of type `Any`, mypy will make the inferred type also be `Any`:

    dynamic_var: Any = some_dynamic_function()

    # output2 is of type 'Any'
    output2 = summarize(dynamic_var)

Second, if multiple variants match due to one or more of the arguments being a union, mypy will make the inferred type be the union of the matching variant returns:

    some_list: Union[list[int], list[str]]

    # output3 is of type 'Union[float, str]'
    output3 = summarize(some_list)

Note

Due to the “pick the first match” rule, changing the order of your overload variants can change how mypy type checks your program.

To minimize potential issues, we recommend that you:

1. Make sure your overload variants are listed in the same order as the runtime checks (e.g. `isinstance` checks) in your implementation.

2. Order your variants and runtime checks from most to least specific. (See the following section for an example).

### Type checking the variants

Mypy will perform several checks on your overload variant definitions to ensure they behave as expected. First, mypy will check and make sure that no overload variant is shadowing a subsequent one. For example, consider the following function which adds together two `Expression` objects, and contains a special-case to handle receiving two `Literal` types:

    from typing import overload, Union

    class Expression:
        # ...snip...

    class Literal(Expression):
        # ...snip...

    # Warning -- the first overload variant shadows the second!

    @overload
    def add(left: Expression, right: Expression) -> Expression: ...

    @overload
    def add(left: Literal, right: Literal) -> Literal: ...

    def add(left: Expression, right: Expression) -> Expression:
        # ...snip...

While this code snippet is technically type-safe, it does contain an anti-pattern: the second variant will never be selected! If we try calling `add(Literal(3), Literal(4))`, mypy will always pick the first variant and evaluate the function call to be of type `Expression`, not `Literal`. This is because `Literal` is a subtype of `Expression`, which means the “pick the first match” rule will always halt after considering the first overload.

Because having an overload variant that can never be matched is almost certainly a mistake, mypy will report an error. To fix the error, we can either 1) delete the second overload or 2) swap the order of the overloads:

    # Everything is ok now -- the variants are correctly ordered
    # from most to least specific.

    @overload
    def add(left: Literal, right: Literal) -> Literal: ...

    @overload
    def add(left: Expression, right: Expression) -> Expression: ...

    def add(left: Expression, right: Expression) -> Expression:
        # ...snip...

Mypy will also type check the different variants and flag any overloads that have inherently unsafely overlapping variants. For example, consider the following unsafe overload definition:

    from typing import overload, Union

    @overload
    def unsafe_func(x: int) -> int: ...

    @overload
    def unsafe_func(x: object) -> str: ...

    def unsafe_func(x: object) -> Union[int, str]:
        if isinstance(x, int):
            return 42
        else:
            return "some string"

On the surface, this function definition appears to be fine. However, it will result in a discrepancy between the inferred type and the actual runtime type when we try using it like so:

    some_obj: object = 42
    unsafe_func(some_obj) + " danger danger"  # Type checks, yet crashes at runtime!

Since `some_obj` is of type `object`, mypy will decide that `unsafe_func` must return something of type `str` and concludes the above will type check. But in reality, `unsafe_func` will return an int, causing the code to crash at runtime!

To prevent these kinds of issues, mypy will detect and prohibit inherently unsafely overlapping overloads on a best-effort basis. Two variants are considered unsafely overlapping when both of the following are true:

1. All of the arguments of the first variant are potentially compatible with the second.

2. The return type of the first variant is not compatible with (e.g. is not a subtype of) the second.

So in this example, the `int` argument in the first variant is a subtype of the `object` argument in the second, yet the `int` return type is not a subtype of `str`. Both conditions are true, so mypy will correctly flag `unsafe_func` as being unsafe.

Note that in cases where you ignore the overlapping overload error, mypy will usually still infer the types you expect at callsites.

However, mypy will not detect all unsafe uses of overloads. For example, suppose we modify the above snippet so it calls `summarize` instead of `unsafe_func`:

    some_list: list[str] = []
    summarize(some_list) + "danger danger"  # Type safe, yet crashes at runtime!

We run into a similar issue here. This program type checks if we look just at the annotations on the overloads. But since `summarize(...)` is designed to be biased towards returning a float when it receives an empty list, this program will actually crash during runtime.

The reason mypy does not flag definitions like `summarize` as being potentially unsafe is because if it did, it would be extremely difficult to write a safe overload. For example, suppose we define an overload with two variants that accept types `A` and `B` respectively. Even if those two types were completely unrelated, the user could still potentially trigger a runtime error similar to the ones above by passing in a value of some third type `C` that inherits from both `A` and `B`.

Thankfully, these types of situations are relatively rare. What this does mean, however, is that you should exercise caution when designing or using an overloaded function that can potentially receive values that are an instance of two seemingly unrelated types.

### Type checking the implementation

The body of an implementation is type-checked against the type hints provided on the implementation. For example, in the `MyList` example up above, the code in the body is checked with argument list `index: Union[int, slice]` and a return type of `Union[T, Sequence[T]]`. If there are no annotations on the implementation, then the body is not type checked. If you want to force mypy to check the body anyways, use the `--check-untyped-defs` flag (more details here).

The variants must also also be compatible with the implementation type hints. In the `MyList` example, mypy will check that the parameter type `int` and the return type `T` are compatible with `Union[int, slice]` and `Union[T, Sequence]` for the first variant. For the second variant it verifies the parameter type `slice` and the return type `Sequence[T]` are compatible with `Union[int, slice]` and `Union[T, Sequence]`.

Note

The overload semantics documented above are new as of mypy 0.620.

Previously, mypy used to perform type erasure on all overload variants. For example, the `summarize` example from the previous section used to be illegal because `list[str]` and `list[int]` both erased to just `list[Any]`. This restriction was removed in mypy 0.620.

Mypy also previously used to select the best matching variant using a different algorithm. If this algorithm failed to find a match, it would default to returning `Any`. The new algorithm uses the “pick the first match” rule and will fall back to returning `Any` only if the input arguments also contain `Any`.

### Conditional overloads

Sometimes it is useful to define overloads conditionally. Common use cases include types that are unavailable at runtime or that only exist in a certain Python version. All existing overload rules still apply. For example, there must be at least two overloads.

Note

Mypy can only infer a limited number of conditions. Supported ones currently include `TYPE_CHECKING`, `MYPY`, Python version and system platform checks, `--always-true`, and `--always-false` values.

    from typing import TYPE_CHECKING, Any, overload

    if TYPE_CHECKING:
        class A: ...
        class B: ...

    if TYPE_CHECKING:
        @overload
        def func(var: A) -> A: ...

        @overload
        def func(var: B) -> B: ...

    def func(var: Any) -> Any:
        return var

    reveal_type(func(A()))  # Revealed type is "A"

    # flags: --python-version 3.10
    import sys
    from typing import Any, overload

    class A: ...
    class B: ...
    class C: ...
    class D: ...

    if sys.version_info < (3, 7):
        @overload
        def func(var: A) -> A: ...

    elif sys.version_info >= (3, 10):
        @overload
        def func(var: B) -> B: ...

    else:
        @overload
        def func(var: C) -> C: ...

    @overload
    def func(var: D) -> D: ...

    def func(var: Any) -> Any:
        return var

    reveal_type(func(B()))  # Revealed type is "B"
    reveal_type(func(C()))  # No overload variant of "func" matches argument type "C"
        # Possible overload variants:
        #     def func(var: B) -> B
        #     def func(var: D) -> D
        # Revealed type is "Any"

Note

In the last example, mypy is executed with `--python-version 3.10`. Therefore, the condition `sys.version_info >= (3, 10)` will match and the overload for `B` will be added. The overloads for `A` and `C` are ignored! The overload for `D` is not defined conditionally and thus is also added.

When mypy cannot infer a condition to be always `True` or always `False`, an error is emitted.

    from typing import Any, overload

    class A: ...
    class B: ...

    def g(bool_var: bool) -> None:
        if bool_var:  # Condition can't be inferred, unable to merge overloads
            @overload
            def func(var: A) -> A: ...

            @overload
            def func(var: B) -> B: ...

        def func(var: Any) -> Any: ...

        reveal_type(func(A()))  # Revealed type is "Any"

## Advanced uses of self-types

Normally, mypy doesn’t require annotations for the first arguments of instance and class methods. However, they may be needed to have more precise static typing for certain programming patterns.

### Restricted methods in generic classes

In generic classes some methods may be allowed to be called only for certain values of type arguments:

    T = TypeVar('T')

    class Tag(Generic[T]):
        item: T
        def uppercase_item(self: Tag[str]) -> str:
            return self.item.upper()

    def label(ti: Tag[int], ts: Tag[str]) -> None:
        ti.uppercase_item()  # E: Invalid self argument "Tag[int]" to attribute function
                             # "uppercase_item" with type "Callable[[Tag[str]], str]"
        ts.uppercase_item()  # This is OK

This pattern also allows matching on nested types in situations where the type argument is itself generic:

    T = TypeVar('T', covariant=True)
    S = TypeVar('S')

     class Storage(Generic[T]):
         def __init__(self, content: T) -> None:
             self.content = content
         def first_chunk(self: Storage[Sequence[S]]) -> S:
             return self.content[0]

     page: Storage[list[str]]
     page.first_chunk()  # OK, type is "str"

     Storage(0).first_chunk()  # Error: Invalid self argument "Storage[int]" to attribute function
                               # "first_chunk" with type "Callable[[Storage[Sequence[S]]], S]"

Finally, one can use overloads on self-type to express precise types of some tricky methods:

    T = TypeVar('T')

    class Tag(Generic[T]):
        @overload
        def export(self: Tag[str]) -> str: ...
        @overload
        def export(self, converter: Callable[[T], str]) -> str: ...

        def export(self, converter=None):
            if isinstance(self.item, str):
                return self.item
            return converter(self.item)

In particular, an `__init__()` method overloaded on self-type may be useful to annotate generic class constructors where type arguments depend on constructor parameters in a non-trivial way, see e.g. `Popen`.

### Mixin classes

Using host class protocol as a self-type in mixin methods allows more code re-usability for static typing of mixin classes. For example, one can define a protocol that defines common functionality for host classes instead of adding required abstract methods to every mixin:

    class Lockable(Protocol):
        @property
        def lock(self) -> Lock: ...

    class AtomicCloseMixin:
        def atomic_close(self: Lockable) -> int:
            with self.lock:
                # perform actions

    class AtomicOpenMixin:
        def atomic_open(self: Lockable) -> int:
            with self.lock:
                # perform actions

    class File(AtomicCloseMixin, AtomicOpenMixin):
        def __init__(self) -> None:
            self.lock = Lock()

    class Bad(AtomicCloseMixin):
        pass

    f = File()
    b: Bad
    f.atomic_close()  # OK
    b.atomic_close()  # Error: Invalid self type for "atomic_close"

Note that the explicit self-type is required to be a protocol whenever it is not a supertype of the current class. In this case mypy will check the validity of the self-type only at the call site.

### Precise typing of alternative constructors

Some classes may define alternative constructors. If these classes are generic, self-type allows giving them precise signatures:

    T = TypeVar('T')

    class Base(Generic[T]):
        Q = TypeVar('Q', bound='Base[T]')

        def __init__(self, item: T) -> None:
            self.item = item

        @classmethod
        def make_pair(cls: Type[Q], item: T) -> tuple[Q, Q]:
            return cls(item), cls(item)

    class Sub(Base[T]):
        ...

    pair = Sub.make_pair('yes')  # Type is "tuple[Sub[str], Sub[str]]"
    bad = Sub[int].make_pair('no')  # Error: Argument 1 to "make_pair" of "Base"
                                    # has incompatible type "str"; expected "int"

## Typing async/await

Mypy lets you type coroutines that use the `async/await` syntax. For more information regarding coroutines, see PEP 492 and the asyncio documentation.

Functions defined using `async def` are typed similar to normal functions. The return type annotation should be the same as the type of the value you expect to get back when `await`-ing the coroutine.

    import asyncio

    async def format_string(tag: str, count: int) -> str:
        return f'T-minus {count} ({tag})'

    async def countdown(tag: str, count: int) -> str:
        while count > 0:
            my_str = await format_string(tag, count)  # type is inferred to be str
            print(my_str)
            await asyncio.sleep(0.1)
            count -= 1
        return "Blastoff!"

    asyncio.run(countdown("Millennium Falcon", 5))

The result of calling an `async def` function without awaiting will automatically be inferred to be a value of type `Coroutine[Any, Any, T]`, which is a subtype of `Awaitable[T]`:

    my_coroutine = countdown("Millennium Falcon", 5)
    reveal_type(my_coroutine)  # Revealed type is "typing.Coroutine[Any, Any, builtins.str]"

### Asynchronous iterators

If you have an asynchronous iterator, you can use the `AsyncIterator` type in your annotations:

    from typing import Optional, AsyncIterator
    import asyncio

    class arange:
        def __init__(self, start: int, stop: int, step: int) -> None:
            self.start = start
            self.stop = stop
            self.step = step
            self.count = start - step

        def __aiter__(self) -> AsyncIterator[int]:
            return self

        async def __anext__(self) -> int:
            self.count += self.step
            if self.count == self.stop:
                raise StopAsyncIteration
            else:
                return self.count

    async def run_countdown(tag: str, countdown: AsyncIterator[int]) -> str:
        async for i in countdown:
            print(f'T-minus {i} ({tag})')
            await asyncio.sleep(0.1)
        return "Blastoff!"

    asyncio.run(run_countdown("Serenity", arange(5, 0, -1)))

Async generators (introduced in PEP 525) are an easy way to create async iterators:

    from typing import AsyncGenerator, Optional
    import asyncio

    # Could also type this as returning AsyncIterator[int]
    async def arange(start: int, stop: int, step: int) -> AsyncGenerator[int, None]:
        current = start
        while (step > 0 and current < stop) or (step < 0 and current > stop):
            yield current
            current += step

    asyncio.run(run_countdown("Battlestar Galactica", arange(5, 0, -1)))

One common confusion is that the presence of a `yield` statement in an `async def` function has an effect on the type of the function:

    from typing import AsyncIterator

    async def arange(stop: int) -> AsyncIterator[int]:
        # When called, arange gives you an async iterator
        # Equivalent to Callable[[int], AsyncIterator[int]]
        i = 0
        while i < stop:
            yield i
            i += 1

    async def coroutine(stop: int) -> AsyncIterator[int]:
        # When called, coroutine gives you something you can await to get an async iterator
        # Equivalent to Callable[[int], Coroutine[Any, Any, AsyncIterator[int]]]
        return arange(stop)

    async def main() -> None:
        reveal_type(arange(5))  # Revealed type is "typing.AsyncIterator[builtins.int]"
        reveal_type(coroutine(5))  # Revealed type is "typing.Coroutine[Any, Any, typing.AsyncIterator[builtins.int]]"

        await arange(5)  # Error: Incompatible types in "await" (actual type "AsyncIterator[int]", expected type "Awaitable[Any]")
        reveal_type(await coroutine(5))  # Revealed type is "typing.AsyncIterator[builtins.int]"

This can sometimes come up when trying to define base classes, Protocols or overloads:

    from typing import AsyncIterator, Protocol, overload

    class LauncherIncorrect(Protocol):
        # Because launch does not have yield, this has type
        # Callable[[], Coroutine[Any, Any, AsyncIterator[int]]]
        # instead of
        # Callable[[], AsyncIterator[int]]
        async def launch(self) -> AsyncIterator[int]:
            raise NotImplementedError

    class LauncherCorrect(Protocol):
        def launch(self) -> AsyncIterator[int]:
            raise NotImplementedError

    class LauncherAlsoCorrect(Protocol):
        async def launch(self) -> AsyncIterator[int]:
            raise NotImplementedError
            if False:
                yield 0

    # The type of the overloads is independent of the implementation.
    # In particular, their type is not affected by whether or not the
    # implementation contains a `yield`.
    # Use of `def`` makes it clear the type is Callable[..., AsyncIterator[int]],
    # whereas with `async def` it would be Callable[..., Coroutine[Any, Any, AsyncIterator[int]]]
    @overload
    def launch(*, count: int = ...) -> AsyncIterator[int]: ...
    @overload
    def launch(*, time: float = ...) -> AsyncIterator[int]: ...

    async def launch(*, count: int = 0, time: float = 0) -> AsyncIterator[int]:
        # The implementation of launch is an async generator and contains a yield
        yield 0