Sometimes the types in a function depend on each other in ways that
can’t be captured with a
Union. For example, the
 bracket indexing) method can take an integer and return a
single item, or take a
slice and return a
Sequence of items.
You might be tempted to annotate it like so:
from typing import Sequence, TypeVar, Union T = TypeVar('T') class MyList(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(...)
But this is too loose, as it implies that when you pass in an
you might sometimes get out a single item and sometimes a sequence.
The return type depends on the parameter type in a way that can’t be
expressed using a type variable. Instead, we can use overloading
to give the same function multiple type annotations (signatures) and
accurately describe the function’s behavior.
from typing import overload, Sequence, TypeVar, Union T = TypeVar('T') class MyList(Sequence[T]): # The @overload definitions are just for the type checker, # and overwritten by the real implementation below. @overload def __getitem__(self, index: int) -> T: pass # Don't put code here # All overloads and the implementation must be adjacent # in the source file, and overload order may matter: # when two overloads may overlap, the more specific one # should come first. @overload def __getitem__(self, index: slice) -> Sequence[T]: pass # Don't put code here # The implementation goes last, without @overload. # It may or may not have type hints; if it does, # these are checked against the overload definitions # as well as against the implementation body. def __getitem__(self, index: Union[int, slice]) -> Union[T, Sequence[T]]: # This is exactly the same as before. if isinstance(index, int): ... # Return a T here elif isinstance(index, slice): ... # Return a sequence of Ts here else: raise TypeError(...)
Calls to overloaded functions are type checked against the variants,
not against the implementation. A call like
my_list would have
Union[T, Sequence[T]] because it matches the
first overloaded definition, and ignores the type annotations on the
__getitem__. The code in the body of the
__getitem__ is checked against the annotations on
the the corresponding declaration. In this case the body is checked
index: Union[int, slice] and a return type
Union[T, Sequence[T]]. If there are no annotations on the
corresponding definition, then code in the function body is not type
The annotations on the function body must be compatible with the
types given for the overloaded variants listed above it. The type
checker will verify that all the types listed the overloaded variants
are compatible with the types given for the implementation. In this
case it checks that the parameter type
int and the return type
T are compatible with
Union[int, slice] and
Union[T, Sequence[T]] for the first variant. For the second
variant it verifies that the parameter type
slice are the return
Sequence[T] are compatible with
Union[int, slice] and
Overloaded function variants are still ordinary Python functions and
they still define a single runtime object. There is no automatic
dispatch happening, and you must manually handle the different types
in the implementation (usually with
isinstance() checks, as
shown in the example).
The overload variants must be adjacent in the code. This makes code clearer, as you don’t have to hunt for overload variants across the file.
Overloads in stub files are exactly the same, except there is no implementation.
As generic type variables are erased at runtime when constructing
instances of generic types, an overloaded function cannot have
variants that only differ in a generic type argument,
If you just need to constrain a type variable to certain types or subtypes, you can use a value restriction.