ruff/crates/ty_python_semantic/resources/mdtest/dataclasses/dataclasses.md

Dataclasses

Basic

Decorating a class with @dataclass is a convenient way to add special methods such as __init__, __repr__, and __eq__ to a class. The following example shows the basic usage of the @dataclass decorator. By default, only the three mentioned methods are generated.

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int | None = None

alice1 = Person("Alice", 30)
alice2 = Person(name="Alice", age=30)
alice3 = Person(age=30, name="Alice")
alice4 = Person("Alice", age=30)

reveal_type(alice1)  # revealed: Person
reveal_type(type(alice1))  # revealed: type[Person]

reveal_type(alice1.name)  # revealed: str
reveal_type(alice1.age)  # revealed: int | None

reveal_type(repr(alice1))  # revealed: str

reveal_type(alice1 == alice2)  # revealed: bool
reveal_type(alice1 == "Alice")  # revealed: bool

bob = Person("Bob")
bob2 = Person("Bob", None)
bob3 = Person(name="Bob")
bob4 = Person(name="Bob", age=None)

The signature of the __init__ method is generated based on the classes attributes. The following calls are not valid:

# error: [missing-argument]
Person()

# error: [too-many-positional-arguments]
Person("Eve", 20, "too many arguments")

# error: [invalid-argument-type]
Person("Eve", "string instead of int")

# error: [invalid-argument-type]
# error: [invalid-argument-type]
Person(20, "Eve")

Signature of __init__

Declarations in the class body are used to generate the signature of the __init__ method. If the attributes are not just declarations, but also bindings, the type inferred from bindings is used as the default value.

from dataclasses import dataclass

@dataclass
class D:
    x: int
    y: str = "default"
    z: int | None = 1 + 2

reveal_type(D.__init__)  # revealed: (self: D, x: int, y: str = Literal["default"], z: int | None = Literal[3]) -> None

This also works if the declaration and binding are split:

@dataclass
class D:
    x: int | None
    x = None

reveal_type(D.__init__)  # revealed: (self: D, x: int | None = None) -> None

Non-fully static types are handled correctly:

from typing import Any

@dataclass
class C:
    w: type[Any]
    x: Any
    y: int | Any
    z: tuple[int, Any]

reveal_type(C.__init__)  # revealed: (self: C, w: type[Any], x: Any, y: int | Any, z: tuple[int, Any]) -> None

Variables without annotations are ignored:

@dataclass
class D:
    x: int
    y = 1

reveal_type(D.__init__)  # revealed: (self: D, x: int) -> None

If attributes without default values are declared after attributes with default values, a TypeError will be raised at runtime. Ideally, we would emit a diagnostic in that case:

@dataclass
class D:
    x: int = 1
    # TODO: this should be an error: field without default defined after field with default
    y: str

Pure class attributes (ClassVar) are not included in the signature of __init__:

from typing import ClassVar

@dataclass
class D:
    x: int
    y: ClassVar[str] = "default"
    z: bool

reveal_type(D.__init__)  # revealed: (self: D, x: int, z: bool) -> None

d = D(1, True)
reveal_type(d.x)  # revealed: int
reveal_type(d.y)  # revealed: str
reveal_type(d.z)  # revealed: bool

Function declarations do not affect the signature of __init__:

@dataclass
class D:
    x: int

    def y(self) -> str:
        return ""

reveal_type(D.__init__)  # revealed: (self: D, x: int) -> None

And neither do nested class declarations:

@dataclass
class D:
    x: int

    class Nested:
        y: str

reveal_type(D.__init__)  # revealed: (self: D, x: int) -> None

But if there is a variable annotation with a function or class literal type, the signature of __init__ will include this field:

from ty_extensions import TypeOf

class SomeClass: ...

def some_function() -> None: ...
@dataclass
class D:
    function_literal: TypeOf[some_function]
    class_literal: TypeOf[SomeClass]
    class_subtype_of: type[SomeClass]

# revealed: (self: D, function_literal: def some_function() -> None, class_literal: <class 'SomeClass'>, class_subtype_of: type[SomeClass]) -> None
reveal_type(D.__init__)

More realistically, dataclasses can have Callable attributes:

from typing import Callable

@dataclass
class D:
    c: Callable[[int], str]

reveal_type(D.__init__)  # revealed: (self: D, c: (int, /) -> str) -> None

Implicit instance attributes do not affect the signature of __init__:

@dataclass
class D:
    x: int

    def f(self, y: str) -> None:
        self.y: str = y

reveal_type(D(1).y)  # revealed: str

reveal_type(D.__init__)  # revealed: (self: D, x: int) -> None

Annotating expressions does not lead to an entry in __annotations__ at runtime, and so it wouldn't be included in the signature of __init__. This is a case that we currently don't detect:

@dataclass
class D:
    # (x) is an expression, not a "simple name"
    (x): int = 1

# TODO: should ideally not include a `x` parameter
reveal_type(D.__init__)  # revealed: (self: D, x: int = Literal[1]) -> None

@dataclass calls with arguments

The @dataclass decorator can take several arguments to customize the existence of the generated methods. The following test makes sure that we still treat the class as a dataclass if (the default) arguments are passed in:

from dataclasses import dataclass

@dataclass(init=True, repr=True, eq=True)
class Person:
    name: str
    age: int | None = None

alice = Person("Alice", 30)
reveal_type(repr(alice))  # revealed: str
reveal_type(alice == alice)  # revealed: bool

If init is set to False, no __init__ method is generated:

from dataclasses import dataclass

@dataclass(init=False)
class C:
    x: int

C()  # Okay

# error: [too-many-positional-arguments]
C(1)

repr(C())

C() == C()

Other dataclass parameters

repr

A custom __repr__ method is generated by default. It can be disabled by passing repr=False, but in that case __repr__ is still available via object.__repr__:

from dataclasses import dataclass

@dataclass(repr=False)
class WithoutRepr:
    x: int

reveal_type(WithoutRepr(1).__repr__)  # revealed: bound method WithoutRepr.__repr__() -> str

eq

The same is true for __eq__. Setting eq=False disables the generated __eq__ method, but __eq__ is still available via object.__eq__:

from dataclasses import dataclass

@dataclass(eq=False)
class WithoutEq:
    x: int

reveal_type(WithoutEq(1) == WithoutEq(2))  # revealed: bool

order

[environment]
python-version = "3.12"

order is set to False by default. If order=True, __lt__, __le__, __gt__, and __ge__ methods will be generated:

from dataclasses import dataclass

@dataclass
class WithoutOrder:
    x: int

WithoutOrder(1) < WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) <= WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) > WithoutOrder(2)  # error: [unsupported-operator]
WithoutOrder(1) >= WithoutOrder(2)  # error: [unsupported-operator]

@dataclass(order=True)
class WithOrder:
    x: int

WithOrder(1) < WithOrder(2)
WithOrder(1) <= WithOrder(2)
WithOrder(1) > WithOrder(2)
WithOrder(1) >= WithOrder(2)

Comparisons are only allowed for WithOrder instances:

WithOrder(1) < 2  # error: [unsupported-operator]
WithOrder(1) <= 2  # error: [unsupported-operator]
WithOrder(1) > 2  # error: [unsupported-operator]
WithOrder(1) >= 2  # error: [unsupported-operator]

This also works for generic dataclasses:

from dataclasses import dataclass

@dataclass(order=True)
class GenericWithOrder[T]:
    x: T

GenericWithOrder[int](1) < GenericWithOrder[int](1)

GenericWithOrder[int](1) < GenericWithOrder[str]("a")  # error: [unsupported-operator]

If a class already defines one of the comparison methods, a TypeError is raised at runtime. Ideally, we would emit a diagnostic in that case:

@dataclass(order=True)
class AlreadyHasCustomDunderLt:
    x: int

    # TODO: Ideally, we would emit a diagnostic here
    def __lt__(self, other: object) -> bool:
        return False

unsafe_hash

To do

frozen

If true (the default is False), assigning to fields will generate a diagnostic.

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenClass:
    x: int

frozen_instance = MyFrozenClass(1)
frozen_instance.x = 2  # error: [invalid-assignment]

If __setattr__() or __delattr__() is defined in the class, we should emit a diagnostic.

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenClass:
    x: int

    # TODO: Emit a diagnostic here
    def __setattr__(self, name: str, value: object) -> None: ...

    # TODO: Emit a diagnostic here
    def __delattr__(self, name: str) -> None: ...

This also works for generic dataclasses:

[environment]
python-version = "3.12"

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenGeneric[T]:
    x: T

frozen_instance = MyFrozenGeneric[int](1)
frozen_instance.x = 2  # error: [invalid-assignment]

Attempting to mutate an unresolved attribute on a frozen dataclass:

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenClass: ...

frozen = MyFrozenClass()
frozen.x = 2  # error: [invalid-assignment] "Can not assign to unresolved attribute `x` on type `MyFrozenClass`"

A diagnostic is also emitted if a frozen dataclass is inherited, and an attempt is made to mutate an attribute in the child class:

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenClass:
    x: int = 1

class MyFrozenChildClass(MyFrozenClass): ...

frozen = MyFrozenChildClass()
frozen.x = 2  # error: [invalid-assignment]

The same diagnostic is emitted if a frozen dataclass is inherited, and an attempt is made to delete an attribute:

from dataclasses import dataclass

@dataclass(frozen=True)
class MyFrozenClass:
    x: int = 1

class MyFrozenChildClass(MyFrozenClass): ...

frozen = MyFrozenChildClass()
del frozen.x  # TODO this should emit an [invalid-assignment]

match_args

To do

kw_only

An error is emitted if a dataclass is defined with kw_only=True and positional arguments are passed to the constructor.

[environment]
python-version = "3.10"

from dataclasses import dataclass

@dataclass(kw_only=True)
class A:
    x: int
    y: int

# error: [missing-argument] "No arguments provided for required parameters `x`, `y`"
# error: [too-many-positional-arguments] "Too many positional arguments: expected 0, got 2"
a = A(1, 2)
a = A(x=1, y=2)

The class-level parameter can be overridden per-field.

from dataclasses import dataclass, field

@dataclass(kw_only=True)
class A:
    a: str = field(kw_only=False)
    b: int = 0

reveal_type(A.__init__)  # revealed: (self: A, a: str, *, b: int = Literal[0]) -> None

A("hi")

If some fields are kw_only, they should appear after all positional fields in the __init__ signature.

@dataclass
class A:
    b: int = field(kw_only=True, default=3)
    a: str

A("hi")

The field-level kw_only value takes precedence over the KW_ONLY pseudo-type.

from dataclasses import field, dataclass, KW_ONLY

@dataclass
class C:
    _: KW_ONLY
    x: int = field(kw_only=False)

C(x=1)
C(1)

kw_only - Python < 3.10

For Python < 3.10, kw_only is not supported.

[environment]
python-version = "3.9"

from dataclasses import dataclass

@dataclass(kw_only=True)  # TODO: Emit a diagnostic here
class A:
    x: int
    y: int

kw_only - Python 3.13

[environment]
python-version = "3.13"

from dataclasses import dataclass, field

@dataclass
class Employee:
    e_id: int = field(kw_only=True, default=0)
    name: str

Employee("Alice")
Employee(name="Alice")
Employee(name="Alice", e_id=1)
Employee(e_id=1, name="Alice")
Employee("Alice", e_id=1)

Employee("Alice", 1)  # error: [too-many-positional-arguments]

kw_only - Python 3.14

[environment]
python-version = "3.14"

from dataclasses import dataclass, field

@dataclass
class Employee:
    # Python 3.14 introduces a new `doc` parameter for `dataclasses.field`
    e_id: int = field(kw_only=True, default=0, doc="Global employee ID")
    name: str

Employee("Alice")
Employee(name="Alice")
Employee(name="Alice", e_id=1)
Employee(e_id=1, name="Alice")
Employee("Alice", e_id=1)

Employee("Alice", 1)  # error: [too-many-positional-arguments]

slots

If a dataclass is defined with slots=True, the __slots__ attribute is generated as a tuple. It is not present otherwise.

from dataclasses import dataclass
from typing import Tuple

@dataclass
class A:
    x: int
    y: int

# revealed: Unknown
# error: [unresolved-attribute]
reveal_type(A.__slots__)

@dataclass(slots=True)
class B:
    x: int
    y: int

reveal_type(B.__slots__)  # revealed: tuple[Literal["x"], Literal["y"]]

weakref_slot

To do

Final fields

Dataclass fields can be annotated with Final, which means that the field cannot be reassigned after the instance is created. Fields that are additionally annotated with ClassVar are not part of the __init__ signature.

from dataclasses import dataclass
from typing import Final, ClassVar

@dataclass
class C:
    # a `Final` annotation without a right-hand side is not allowed in normal classes,
    # but valid for dataclasses. The field will be initialized in the synthesized
    # `__init__` method
    instance_variable_no_default: Final[int]
    instance_variable: Final[int] = 1
    class_variable1: ClassVar[Final[int]] = 1
    class_variable2: ClassVar[Final[int]] = 1

reveal_type(C.__init__)  # revealed: (self: C, instance_variable_no_default: int, instance_variable: int = Literal[1]) -> None

c = C(1)
# error: [invalid-assignment] "Cannot assign to final attribute `instance_variable` on type `C`"
c.instance_variable = 2

Inheritance

Normal class inheriting from a dataclass

from dataclasses import dataclass

@dataclass
class Base:
    x: int

class Derived(Base): ...

d = Derived(1)  # OK
reveal_type(d.x)  # revealed: int

Dataclass inheriting from normal class

from dataclasses import dataclass

class Base:
    x: int = 1

@dataclass
class Derived(Base):
    y: str

d = Derived("a")

# error: [too-many-positional-arguments]
# error: [invalid-argument-type]
Derived(1, "a")

Dataclass inheriting from another dataclass

from dataclasses import dataclass

@dataclass
class Base:
    x: int
    y: str

@dataclass
class Derived(Base):
    z: bool

d = Derived(1, "a", True)  # OK

reveal_type(d.x)  # revealed: int
reveal_type(d.y)  # revealed: str
reveal_type(d.z)  # revealed: bool

# error: [missing-argument]
Derived(1, "a")

# error: [missing-argument]
Derived(True)

Overwriting attributes from base class

The following example comes from the Python documentation. The x attribute appears just once in the __init__ signature, and the default value is taken from the derived class

from dataclasses import dataclass
from typing import Any

@dataclass
class Base:
    x: Any = 15.0
    y: int = 0

@dataclass
class C(Base):
    z: int = 10
    x: int = 15

reveal_type(C.__init__)  # revealed: (self: C, x: int = Literal[15], y: int = Literal[0], z: int = Literal[10]) -> None

Conditionally defined fields

Statically known conditions

Fields that are defined in always-reachable branches are always present in the synthesized __init__ method. Fields that are defined in never-reachable branches are not present:

from dataclasses import dataclass

@dataclass
class C:
    normal: int

    if 1 + 2 == 3:
        always_present: str

    if 1 + 2 == 4:
        never_present: bool

reveal_type(C.__init__)  # revealed: (self: C, normal: int, always_present: str) -> None

Dynamic conditions

If a field is conditionally defined, we currently assume that it is always present. A more complex alternative here would be to synthesized a union of all possible __init__ signatures:

from dataclasses import dataclass

def flag() -> bool:
    return True

@dataclass
class C:
    normal: int

    if flag():
        conditionally_present: str

reveal_type(C.__init__)  # revealed: (self: C, normal: int, conditionally_present: str) -> None

Generic dataclasses

[environment]
python-version = "3.12"

Basic

from dataclasses import dataclass

@dataclass
class DataWithDescription[T]:
    data: T
    description: str

reveal_type(DataWithDescription[int])  # revealed: <class 'DataWithDescription[int]'>

d_int = DataWithDescription[int](1, "description")  # OK
reveal_type(d_int.data)  # revealed: int
reveal_type(d_int.description)  # revealed: str

# error: [invalid-argument-type]
DataWithDescription[int](None, "description")

Deriving from generic dataclasses

This is a regression test for https://github.com/astral-sh/ty/issues/853.

from dataclasses import dataclass

@dataclass
class Wrap[T]:
    data: T

reveal_type(Wrap[int].__init__)  # revealed: (self: Wrap[int], data: int) -> None

@dataclass
class WrappedInt(Wrap[int]):
    other_field: str

reveal_type(WrappedInt.__init__)  # revealed: (self: WrappedInt, data: int, other_field: str) -> None

# Make sure that another generic type parameter does not affect the `data` field
@dataclass
class WrappedIntAndExtraData[T](Wrap[int]):
    extra_data: T

# revealed: (self: WrappedIntAndExtraData[bytes], data: int, extra_data: bytes) -> None
reveal_type(WrappedIntAndExtraData[bytes].__init__)

Descriptor-typed fields

Same type in __get__ and __set__

For the following descriptor, the return type of __get__ and the type of the value parameter in __set__ are the same. The generated __init__ method takes an argument of this type (instead of the type of the descriptor), and the default value is also of this type:

from typing import overload
from dataclasses import dataclass

class UppercaseString:
    _value: str = ""

    def __get__(self, instance: object, owner: None | type) -> str:
        return self._value

    def __set__(self, instance: object, value: str) -> None:
        self._value = value.upper()

@dataclass
class C:
    upper: UppercaseString = UppercaseString()

reveal_type(C.__init__)  # revealed: (self: C, upper: str = str) -> None

c = C("abc")
reveal_type(c.upper)  # revealed: str

# This is also okay:
C()

# error: [invalid-argument-type]
C(1)

# error: [too-many-positional-arguments]
C("a", "b")

Different types in __get__ and __set__

In general, the type of the __init__ parameter is determined by the value parameter type of the __set__ method (str in the example below). However, the default value is generated by calling the descriptor's __get__ method as if it had been called on the class itself, i.e. passing None for the instance argument.

from typing import Literal, overload
from dataclasses import dataclass

class ConvertToLength:
    _len: int = 0

    @overload
    def __get__(self, instance: None, owner: type) -> Literal[""]: ...
    @overload
    def __get__(self, instance: object, owner: type | None) -> int: ...
    def __get__(self, instance: object | None, owner: type | None) -> str | int:
        if instance is None:
            return ""

        return self._len

    def __set__(self, instance, value: str) -> None:
        self._len = len(value)

@dataclass
class C:
    converter: ConvertToLength = ConvertToLength()

reveal_type(C.__init__)  # revealed: (self: C, converter: str = Literal[""]) -> None

c = C("abc")
reveal_type(c.converter)  # revealed: int

# This is also okay:
C()

# error: [invalid-argument-type]
C(1)

# error: [too-many-positional-arguments]
C("a", "b")

With overloaded __set__ method

If the __set__ method is overloaded, we determine the type for the __init__ parameter as the union of all possible value parameter types:

from typing import overload
from dataclasses import dataclass

class AcceptsStrAndInt:
    def __get__(self, instance, owner) -> int:
        return 0

    @overload
    def __set__(self, instance: object, value: str) -> None: ...
    @overload
    def __set__(self, instance: object, value: int) -> None: ...
    def __set__(self, instance: object, value) -> None:
        pass

@dataclass
class C:
    field: AcceptsStrAndInt = AcceptsStrAndInt()

reveal_type(C.__init__)  # revealed: (self: C, field: str | int = int) -> None

dataclasses.field

To do

dataclass.fields

Dataclasses have a special __dataclass_fields__ class variable member. The DataclassInstance protocol checks for the presence of this attribute. It is used in the dataclasses.fields and dataclasses.asdict functions, for example:

from dataclasses import dataclass, fields, asdict

@dataclass
class Foo:
    x: int

foo = Foo(1)

reveal_type(foo.__dataclass_fields__)  # revealed: dict[str, Field[Any]]
reveal_type(fields(Foo))  # revealed: tuple[Field[Any], ...]
reveal_type(asdict(foo))  # revealed: dict[str, Any]

The class objects themselves also have a __dataclass_fields__ attribute:

reveal_type(Foo.__dataclass_fields__)  # revealed: dict[str, Field[Any]]

They can be passed into fields as well, because it also accepts type[DataclassInstance] arguments:

reveal_type(fields(Foo))  # revealed: tuple[Field[Any], ...]

But calling asdict on the class object is not allowed:

# TODO: this should be a invalid-argument-type error, but we don't properly check the
# types (and more importantly, the `ClassVar` type qualifier) of protocol members yet.
asdict(Foo)

dataclasses.KW_ONLY

If an attribute is annotated with dataclasses.KW_ONLY, it is not added to the synthesized __init__ of the class. Instead, this special marker annotation causes Python at runtime to ensure that all annotations following it have keyword-only parameters generated for them in the class's synthesized __init__ method.

[environment]
python-version = "3.10"

from dataclasses import dataclass, field, KW_ONLY

@dataclass
class C:
    x: int
    _: KW_ONLY
    y: str

reveal_type(C.__init__)  # revealed: (self: C, x: int, *, y: str) -> None

# error: [missing-argument]
# error: [too-many-positional-arguments]
C(3, "")

C(3, y="")

Using KW_ONLY to annotate more than one field in a dataclass causes a TypeError to be raised at runtime:

@dataclass
class Fails:  # error: [duplicate-kw-only]
    a: int
    b: KW_ONLY
    c: str
    d: KW_ONLY
    e: bytes

reveal_type(Fails.__init__)  # revealed: (self: Fails, a: int, *, c: str, e: bytes) -> None

This also works if KW_ONLY is used in a conditional branch:

def flag() -> bool:
    return True

@dataclass
class D:  # error: [duplicate-kw-only]
    x: int
    _1: KW_ONLY

    if flag():
        y: str
        _2: KW_ONLY
        z: float

KW_ONLY should only affect fields declared after it within the same class, not fields in subclasses:

from dataclasses import dataclass, KW_ONLY

@dataclass
class D:
    x: int
    _: KW_ONLY
    y: str

@dataclass
class E(D):
    z: bytes

# This should work: x=1 (positional), z=b"foo" (positional), y="foo" (keyword-only)
E(1, b"foo", y="foo")

reveal_type(E.__init__)  # revealed: (self: E, x: int, z: bytes, *, y: str) -> None

Other special cases

dataclasses.dataclass

We also understand dataclasses if they are decorated with the fully qualified name:

import dataclasses

@dataclasses.dataclass
class C:
    x: str

reveal_type(C.__init__)  # revealed: (self: C, x: str) -> None

Dataclass with custom __init__ method

If a class already defines __init__, it is not replaced by the dataclass decorator.

from dataclasses import dataclass

@dataclass(init=True)
class C:
    x: str

    def __init__(self, x: int) -> None:
        self.x = str(x)

C(1)  # OK

# error: [invalid-argument-type]
C("a")

Similarly, if we set init=False, we still recognize the custom __init__ method:

@dataclass(init=False)
class D:
    def __init__(self, x: int) -> None:
        self.x = str(x)

D(1)  # OK
D()  # error: [missing-argument]

Return type of dataclass(...)

A call like dataclass(order=True) returns a callable itself, which is then used as the decorator. We can store the callable in a variable and later use it as a decorator:

from dataclasses import dataclass

dataclass_with_order = dataclass(order=True)

reveal_type(dataclass_with_order)  # revealed: <decorator produced by dataclass-like function>

@dataclass_with_order
class C:
    x: int

C(1) < C(2)  # ok

Using dataclass as a function

from dataclasses import dataclass

class B:
    x: int

# error: [missing-argument]
dataclass(B)()

# error: [invalid-argument-type]
dataclass(B)("a")

reveal_type(dataclass(B)(3).x)  # revealed: int

Internals

The dataclass decorator returns the class itself. This means that the type of Person is type, and attributes like the MRO are unchanged:

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int | None = None

reveal_type(type(Person))  # revealed: <class 'type'>
reveal_type(Person.__mro__)  # revealed: tuple[<class 'Person'>, <class 'object'>]

The generated methods have the following signatures:

reveal_type(Person.__init__)  # revealed: (self: Person, name: str, age: int | None = None) -> None

reveal_type(Person.__repr__)  # revealed: def __repr__(self) -> str

reveal_type(Person.__eq__)  # revealed: def __eq__(self, value: object, /) -> bool

Function-like behavior of synthesized methods

Here, we make sure that the synthesized methods of dataclasses behave like proper functions.

[environment]
python-version = "3.12"

from dataclasses import dataclass
from typing import Callable
from types import FunctionType
from ty_extensions import CallableTypeOf, TypeOf, static_assert, is_subtype_of, is_assignable_to, is_equivalent_to

@dataclass(order=True)
class C:
    x: int

reveal_type(C.__init__)  # revealed: (self: C, x: int) -> None
reveal_type(type(C.__init__))  # revealed: <class 'FunctionType'>

# We can access attributes that are defined on functions:
reveal_type(type(C.__init__).__code__)  # revealed: CodeType
reveal_type(C.__init__.__code__)  # revealed: CodeType

def equivalent_signature(self: C, x: int) -> None:
    pass

type DunderInitType = TypeOf[C.__init__]
type EquivalentPureCallableType = Callable[[C, int], None]
type EquivalentFunctionLikeCallableType = CallableTypeOf[equivalent_signature]

static_assert(is_subtype_of(DunderInitType, EquivalentPureCallableType))
static_assert(is_assignable_to(DunderInitType, EquivalentPureCallableType))

static_assert(not is_subtype_of(EquivalentPureCallableType, DunderInitType))
static_assert(not is_assignable_to(EquivalentPureCallableType, DunderInitType))

static_assert(is_subtype_of(DunderInitType, EquivalentFunctionLikeCallableType))
static_assert(is_assignable_to(DunderInitType, EquivalentFunctionLikeCallableType))

static_assert(is_subtype_of(EquivalentFunctionLikeCallableType, DunderInitType))
static_assert(is_assignable_to(EquivalentFunctionLikeCallableType, DunderInitType))

static_assert(is_equivalent_to(EquivalentFunctionLikeCallableType, DunderInitType))

static_assert(is_subtype_of(DunderInitType, FunctionType))

It should be possible to mock out synthesized methods:

from unittest.mock import Mock

def test_c():
    c = C(1)
    c.__lt__ = Mock()