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# Error codes enabled by default

This section documents various errors codes that mypy can generate with default options. See Error codes for general documentation about error codes. Error codes for optional checks documents additional error codes that you can enable.

## Check that attribute exists [attr-defined]

Mypy checks that an attribute is defined in the target class or module when using the dot operator. This applies to both getting and setting an attribute. New attributes are defined by assignments in the class body, or assignments to `self.x` in methods. These assignments don’t generate `attr-defined` errors.

Example:

    class Resource:
        def __init__(self, name: str) -> None:
            self.name = name

    r = Resource('x')
    print(r.name)  # OK
    print(r.id)  # Error: "Resource" has no attribute "id"  [attr-defined]
    r.id = 5  # Error: "Resource" has no attribute "id"  [attr-defined]

This error code is also generated if an imported name is not defined in the module in a `from ... import` statement (as long as the target module can be found):

    # Error: Module "os" has no attribute "non_existent"  [attr-defined]
    from os import non_existent

A reference to a missing attribute is given the `Any` type. In the above example, the type of `non_existent` will be `Any`, which can be important if you silence the error.

## Check that attribute exists in each union item [union-attr]

If you access the attribute of a value with a union type, mypy checks that the attribute is defined for every type in that union. Otherwise the operation can fail at runtime. This also applies to optional types.

Example:

    class Cat:
        def sleep(self) -> None: ...
        def miaow(self) -> None: ...

    class Dog:
        def sleep(self) -> None: ...
        def follow_me(self) -> None: ...

    def func(animal: Cat | Dog) -> None:
        # OK: 'sleep' is defined for both Cat and Dog
        animal.sleep()
        # Error: Item "Cat" of "Cat | Dog" has no attribute "follow_me"  [union-attr]
        animal.follow_me()

You can often work around these errors by using `assert isinstance(obj, ClassName)` or `assert obj is not None` to tell mypy that you know that the type is more specific than what mypy thinks.

## Check that name is defined [name-defined]

Mypy expects that all references to names have a corresponding definition in an active scope, such as an assignment, function definition or an import. This can catch missing definitions, missing imports, and typos.

This example accidentally calls `sort()` instead of `sorted()`:

    x = sort([3, 2, 4])  # Error: Name "sort" is not defined  [name-defined]

## Check that a variable is not used before it’s defined [used-before-def]

Mypy will generate an error if a name is used before it’s defined. While the name-defined check will catch issues with names that are undefined, it will not flag if a variable is used and then defined later in the scope. used-before-def check will catch such cases.

Example:

    print(x)  # Error: Name "x" is used before definition [used-before-def]
    x = 123

## Check arguments in calls [call-arg]

Mypy expects that the number and names of arguments match the called function. Note that argument type checks have a separate error code `arg-type`.

Example:

    def greet(name: str) -> None:
         print('hello', name)

    greet('jack')  # OK
    greet('jill', 'jack')  # Error: Too many arguments for "greet"  [call-arg]

## Check argument types [arg-type]

Mypy checks that argument types in a call match the declared argument types in the signature of the called function (if one exists).

Example:

    def first(x: list[int]) -> int:
        return x[0] if x else 0

    t = (5, 4)
    # Error: Argument 1 to "first" has incompatible type "tuple[int, int]";
    #        expected "list[int]"  [arg-type]
    print(first(t))

## Check calls to overloaded functions [call-overload]

When you call an overloaded function, mypy checks that at least one of the signatures of the overload items match the argument types in the call.

Example:

    from typing import overload

    @overload
    def inc_maybe(x: None) -> None: ...

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

    def inc_maybe(x: int | None) -> int | None:
         if x is None:
             return None
         else:
             return x + 1

    inc_maybe(None)  # OK
    inc_maybe(5)  # OK

    # Error: No overload variant of "inc_maybe" matches argument type "float"  [call-overload]
    inc_maybe(1.2)

## Check validity of types [valid-type]

Mypy checks that each type annotation and any expression that represents a type is a valid type. Examples of valid types include classes, union types, callable types, type aliases, and literal types. Examples of invalid types include bare integer literals, functions, variables, and modules.

This example incorrectly uses the function `log` as a type:

    def log(x: object) -> None:
        print('log:', repr(x))

    # Error: Function "t.log" is not valid as a type  [valid-type]
    def log_all(objs: list[object], f: log) -> None:
        for x in objs:
            f(x)

You can use `Callable` as the type for callable objects:

    from collections.abc import Callable

    # OK
    def log_all(objs: list[object], f: Callable[[object], None]) -> None:
        for x in objs:
            f(x)

## Check that NoneType is not used as a type (annotation) [nonetype-type]

The preferred way to annotate the type of None is None. NoneType is equivalent, but mypy won’t allow it by default.

    from types import NoneType
    def f(x: None) -> None:
        reveal_type(x) # note: Revealed type is "None"

    # error: NoneType should not be used as a type, please use None instead  [nonetype-type]
    def g(x: NoneType) -> None:
        reveal_type(x) # note: Revealed type is "None"

    # error: NoneType should not be used as a type, please use None instead  [nonetype-type]
    x1: NoneType = None
    x2: None = None # OK

## Check the validity of a class’s metaclass [metaclass]

Mypy checks whether the metaclass of a class is valid. The metaclass must be a subclass of `type`. Further, the class hierarchy must yield a consistent metaclass. For more details, see the Python documentation

Note that mypy’s metaclass checking is limited and may produce false-positives. See also Gotchas and limitations of metaclass support.

Example with an error:

    class GoodMeta(type):
        pass

    class BadMeta:
        pass

    class A1(metaclass=GoodMeta):  # OK
        pass

    class A2(metaclass=BadMeta):  # Error:  Metaclasses not inheriting from "type" are not supported  [metaclass]
        pass

## Require annotation if variable type is unclear [var-annotated]

In some cases mypy can’t infer the type of a variable without an explicit annotation. Mypy treats this as an error. This typically happens when you initialize a variable with an empty collection or `None`. If mypy can’t infer the collection item type, mypy replaces any parts of the type it couldn’t infer with `Any` and generates an error.

Example with an error:

    class Bundle:
        def __init__(self) -> None:
            # Error: Need type annotation for "items"
            #        (hint: "items: list[<type>] = ...")  [var-annotated]
            self.items = []

    reveal_type(Bundle().items)  # list[Any]

To address this, we add an explicit annotation:

     class Bundle:
         def __init__(self) -> None:
             self.items: list[str] = []  # OK

    reveal_type(Bundle().items)  # list[str]

## Check validity of overrides [override]

Mypy checks that an overridden method or attribute is compatible with the base class. A method in a subclass must accept all arguments that the base class method accepts, and the return type must conform to the return type in the base class (Liskov substitution principle).

Argument types can be more general is a subclass (i.e., they can vary contravariantly). The return type can be narrowed in a subclass (i.e., it can vary covariantly). It’s okay to define additional arguments in a subclass method, as long all extra arguments have default values or can be left out (`*args`, for example).

Example:

    class Base:
        def method(self,
                   arg: int) -> int | None:
            ...

    class Derived(Base):
        def method(self,
                   arg: int | str) -> int:  # OK
            ...

    class DerivedBad(Base):
        # Error: Argument 1 of "method" is incompatible with "Base"  [override]
        def method(self,
                   arg: bool) -> int:
            ...

## Check that function returns a value [return]

If a function has a non-`None` return type, mypy expects that the function always explicitly returns a value (or raises an exception). The function should not fall off the end of the function, since this is often a bug.

Example:

    # Error: Missing return statement  [return]
    def show(x: int) -> int:
        print(x)

    # Error: Missing return statement  [return]
    def pred1(x: int) -> int:
        if x > 0:
            return x - 1

    # OK
    def pred2(x: int) -> int:
        if x > 0:
            return x - 1
        else:
            raise ValueError('not defined for zero')

## Check that functions don’t have empty bodies outside stubs [empty-body]

This error code is similar to the `[return]` code but is emitted specifically for functions and methods with empty bodies (if they are annotated with non-trivial return type). Such a distinction exists because in some contexts an empty body can be valid, for example for an abstract method or in a stub file. Also old versions of mypy used to unconditionally allow functions with empty bodies, so having a dedicated error code simplifies cross-version compatibility.

Note that empty bodies are allowed for methods in protocols, and such methods are considered implicitly abstract:

    from abc import abstractmethod
    from typing import Protocol

    class RegularABC:
        @abstractmethod
        def foo(self) -> int:
            pass  # OK
        def bar(self) -> int:
            pass  # Error: Missing return statement  [empty-body]

    class Proto(Protocol):
        def bar(self) -> int:
            pass  # OK

## Check that return value is compatible [return-value]

Mypy checks that the returned value is compatible with the type signature of the function.

Example:

    def func(x: int) -> str:
        # Error: Incompatible return value type (got "int", expected "str")  [return-value]
        return x + 1

## Check types in assignment statement [assignment]

Mypy checks that the assigned expression is compatible with the assignment target (or targets).

Example:

    class Resource:
        def __init__(self, name: str) -> None:
            self.name = name

    r = Resource('A')

    r.name = 'B'  # OK

    # Error: Incompatible types in assignment (expression has type "int",
    #        variable has type "str")  [assignment]
    r.name = 5

## Check that assignment target is not a method [method-assign]

In general, assigning to a method on class object or instance (a.k.a. monkey-patching) is ambiguous in terms of types, since Python’s static type system cannot express the difference between bound and unbound callable types. Consider this example:

    class A:
        def f(self) -> None: pass
        def g(self) -> None: pass

    def h(self: A) -> None: pass

    A.f = h  # Type of h is Callable[[A], None]
    A().f()  # This works
    A.f = A().g  # Type of A().g is Callable[[], None]
    A().f()  # ...but this also works at runtime

To prevent the ambiguity, mypy will flag both assignments by default. If this error code is disabled, mypy will treat the assigned value in all method assignments as unbound, so only the second assignment will still generate an error.

Note

This error code is a subcode of the more general `[assignment]` code.

## Check type variable values [type-var]

Mypy checks that value of a type variable is compatible with a value restriction or the upper bound type.

Example (Python 3.12 syntax):

    def add[T1: (int, float)](x: T1, y: T1) -> T1:
        return x + y

    add(4, 5.5)  # OK

    # Error: Value of type variable "T1" of "add" cannot be "str"  [type-var]
    add('x', 'y')

## Check uses of various operators [operator]

Mypy checks that operands support a binary or unary operation, such as `+` or `~`. Indexing operations are so common that they have their own error code `index` (see below).

Example:

    # Error: Unsupported operand types for + ("int" and "str")  [operator]
    1 + 'x'

## Check indexing operations [index]

Mypy checks that the indexed value in indexing operation such as `x[y]` supports indexing, and that the index expression has a valid type.

Example:

    a = {'x': 1, 'y': 2}

    a['x']  # OK

    # Error: Invalid index type "int" for "dict[str, int]"; expected type "str"  [index]
    print(a[1])

    # Error: Invalid index type "bytes" for "dict[str, int]"; expected type "str"  [index]
    a[b'x'] = 4

## Check list items [list-item]

When constructing a list using `[item, ...]`, mypy checks that each item is compatible with the list type that is inferred from the surrounding context.

Example:

    # Error: List item 0 has incompatible type "int"; expected "str"  [list-item]
    a: list[str] = [0]

## Check dict items [dict-item]

When constructing a dictionary using `{key: value, ...}` or `dict(key=value, ...)`, mypy checks that each key and value is compatible with the dictionary type that is inferred from the surrounding context.

Example:

    # Error: Dict entry 0 has incompatible type "str": "str"; expected "str": "int"  [dict-item]
    d: dict[str, int] = {'key': 'value'}

## Check TypedDict items [typeddict-item]

When constructing a TypedDict object, mypy checks that each key and value is compatible with the TypedDict type that is inferred from the surrounding context.

When getting a TypedDict item, mypy checks that the key exists. When assigning to a TypedDict, mypy checks that both the key and the value are valid.

Example:

    from typing import TypedDict

    class Point(TypedDict):
        x: int
        y: int

    # Error: Incompatible types (expression has type "float",
    #        TypedDict item "x" has type "int")  [typeddict-item]
    p: Point = {'x': 1.2, 'y': 4}

## Check TypedDict Keys [typeddict-unknown-key]

When constructing a TypedDict object, mypy checks whether the definition contains unknown keys, to catch invalid keys and misspellings. On the other hand, mypy will not generate an error when a previously constructed TypedDict value with extra keys is passed to a function as an argument, since TypedDict values support structural subtyping (“static duck typing”) and the keys are assumed to have been validated at the point of construction. Example:

    from typing import TypedDict

    class Point(TypedDict):
        x: int
        y: int

    class Point3D(Point):
        z: int

    def add_x_coordinates(a: Point, b: Point) -> int:
        return a["x"] + b["x"]

    a: Point = {"x": 1, "y": 4}
    b: Point3D = {"x": 2, "y": 5, "z": 6}

    add_x_coordinates(a, b)  # OK

    # Error: Extra key "z" for TypedDict "Point"  [typeddict-unknown-key]
    add_x_coordinates(a, {"x": 1, "y": 4, "z": 5})

Setting a TypedDict item using an unknown key will also generate this error, since it could be a misspelling:

    a: Point = {"x": 1, "y": 2}
    # Error: Extra key "z" for TypedDict "Point"  [typeddict-unknown-key]
    a["z"] = 3

Reading an unknown key will generate the more general (and serious) `typeddict-item` error, which is likely to result in an exception at runtime:

    a: Point = {"x": 1, "y": 2}
    # Error: TypedDict "Point" has no key "z"  [typeddict-item]
    _ = a["z"]

Note

This error code is a subcode of the wider `[typeddict-item]` code.

## Check that type of target is known [has-type]

Mypy sometimes generates an error when it hasn’t inferred any type for a variable being referenced. This can happen for references to variables that are initialized later in the source file, and for references across modules that form an import cycle. When this happens, the reference gets an implicit `Any` type.

In this example the definitions of `x` and `y` are circular:

    class Problem:
        def set_x(self) -> None:
            # Error: Cannot determine type of "y"  [has-type]
            self.x = self.y

        def set_y(self) -> None:
            self.y = self.x

To work around this error, you can add an explicit type annotation to the target variable or attribute. Sometimes you can also reorganize the code so that the definition of the variable is placed earlier than the reference to the variable in a source file. Untangling cyclic imports may also help.

We add an explicit annotation to the `y` attribute to work around the issue:

    class Problem:
        def set_x(self) -> None:
            self.x = self.y  # OK

        def set_y(self) -> None:
            self.y: int = self.x  # Added annotation here

## Check for an issue with imports [import]

Mypy generates an error if it can’t resolve an import statement. This is a parent error code of import-not-found and import-untyped

See Missing imports for how to work around these errors.

## Check that import target can be found [import-not-found]

Mypy generates an error if it can’t find the source code or a stub file for an imported module.

Example:

    # Error: Cannot find implementation or library stub for module named "m0dule_with_typo"  [import-not-found]
    import m0dule_with_typo

See Missing imports for how to work around these errors.

## Check that import target can be found [import-untyped]

Mypy generates an error if it can find the source code for an imported module, but that module does not provide type annotations (via PEP 561).

Example:

    # Error: Library stubs not installed for "bs4"  [import-untyped]
    import bs4
    # Error: Skipping analyzing "no_py_typed": module is installed, but missing library stubs or py.typed marker  [import-untyped]
    import no_py_typed

In some cases, these errors can be fixed by installing an appropriate stub package. See Missing imports for more details.

## Check that each name is defined once [no-redef]

Mypy may generate an error if you have multiple definitions for a name in the same namespace. The reason is that this is often an error, as the second definition may overwrite the first one. Also, mypy often can’t be able to determine whether references point to the first or the second definition, which would compromise type checking.

If you silence this error, all references to the defined name refer to the first definition.

Example:

    class A:
        def __init__(self, x: int) -> None: ...

    class A:  # Error: Name "A" already defined on line 1  [no-redef]
        def __init__(self, x: str) -> None: ...

    # Error: Argument 1 to "A" has incompatible type "str"; expected "int"
    #        (the first definition wins!)
    A('x')

## Check that called function returns a value [func-returns-value]

Mypy reports an error if you call a function with a `None` return type and don’t ignore the return value, as this is usually (but not always) a programming error.

In this example, the `if f()` check is always false since `f` returns `None`:

    def f() -> None:
        ...

    # OK: we don't do anything with the return value
    f()

    # Error: "f" does not return a value (it only ever returns None)  [func-returns-value]
    if f():
         print("not false")

## Check instantiation of abstract classes [abstract]

Mypy generates an error if you try to instantiate an abstract base class (ABC). An abstract base class is a class with at least one abstract method or attribute. (See also `abc` module documentation)

Sometimes a class is made accidentally abstract, often due to an unimplemented abstract method. In a case like this you need to provide an implementation for the method to make the class concrete (non-abstract).

Example:

    from abc import ABCMeta, abstractmethod

    class Persistent(metaclass=ABCMeta):
        @abstractmethod
        def save(self) -> None: ...

    class Thing(Persistent):
        def __init__(self) -> None:
            ...

        ...  # No "save" method

    # Error: Cannot instantiate abstract class "Thing" with abstract attribute "save"  [abstract]
    t = Thing()

## Safe handling of abstract type object types [type-abstract]

Mypy always allows instantiating (calling) type objects typed as `type[t]`, even if it is not known that `t` is non-abstract, since it is a common pattern to create functions that act as object factories (custom constructors). Therefore, to prevent issues described in the above section, when an abstract type object is passed where `type[t]` is expected, mypy will give an error. Example (Python 3.12 syntax):

    from abc import ABCMeta, abstractmethod

    class Config(metaclass=ABCMeta):
        @abstractmethod
        def get_value(self, attr: str) -> str: ...

    def make_many[T](typ: type[T], n: int) -> list[T]:
        return [typ() for _ in range(n)]  # This will raise if typ is abstract

    # Error: Only concrete class can be given where "type[Config]" is expected [type-abstract]
    make_many(Config, 5)

## Check that call to an abstract method via super is valid [safe-super]

Abstract methods often don’t have any default implementation, i.e. their bodies are just empty. Calling such methods in subclasses via `super()` will cause runtime errors, so mypy prevents you from doing so:

    from abc import abstractmethod
    class Base:
        @abstractmethod
        def foo(self) -> int: ...
    class Sub(Base):
        def foo(self) -> int:
            return super().foo() + 1  # error: Call to abstract method "foo" of "Base" with
                                      # trivial body via super() is unsafe  [safe-super]
    Sub().foo()  # This will crash at runtime.

Mypy considers the following as trivial bodies: a `pass` statement, a literal ellipsis `...`, a docstring, and a `raise NotImplementedError` statement.

## Check the target of NewType [valid-newtype]

The target of a `NewType` definition must be a class type. It can’t be a union type, `Any`, or various other special types.

You can also get this error if the target has been imported from a module whose source mypy cannot find, since any such definitions are treated by mypy as values with `Any` types. Example:

    from typing import NewType

    # The source for "acme" is not available for mypy
    from acme import Entity  # type: ignore

    # Error: Argument 2 to NewType(...) must be subclassable (got "Any")  [valid-newtype]
    UserEntity = NewType('UserEntity', Entity)

To work around the issue, you can either give mypy access to the sources for `acme` or create a stub file for the module. See Missing imports for more information.

## Check the return type of __exit__ [exit-return]

If mypy can determine that `__exit__` always returns `False`, mypy checks that the return type is not `bool`. The boolean value of the return type affects which lines mypy thinks are reachable after a `with` statement, since any `__exit__` method that can return `True` may swallow exceptions. An imprecise return type can result in mysterious errors reported near `with` statements.

To fix this, use either `typing.Literal[False]` or `None` as the return type. Returning `None` is equivalent to returning `False` in this context, since both are treated as false values.

Example:

    class MyContext:
        ...
        def __exit__(self, exc, value, tb) -> bool:  # Error
            print('exit')
            return False

This produces the following output from mypy:

    example.py:3: error: "bool" is invalid as return type for "__exit__" that always returns False
    example.py:3: note: Use "typing_extensions.Literal[False]" as the return type or change it to
        "None"
    example.py:3: note: If return type of "__exit__" implies that it may return True, the context
        manager may swallow exceptions

You can use `Literal[False]` to fix the error:

    from typing import Literal

    class MyContext:
        ...
        def __exit__(self, exc, value, tb) -> Literal[False]:  # OK
            print('exit')
            return False

You can also use `None`:

    class MyContext:
        ...
        def __exit__(self, exc, value, tb) -> None:  # Also OK
            print('exit')

## Check that naming is consistent [name-match]

The definition of a named tuple or a TypedDict must be named consistently when using the call-based syntax. Example:

    from typing import NamedTuple

    # Error: First argument to namedtuple() should be "Point2D", not "Point"
    Point2D = NamedTuple("Point", [("x", int), ("y", int)])

## Check that literal is used where expected [literal-required]

There are some places where only a (string) literal value is expected for the purposes of static type checking, for example a `TypedDict` key, or a `__match_args__` item. Providing a `str`-valued variable in such contexts will result in an error. Note that in many cases you can also use `Final` or `Literal` variables. Example:

    from typing import Final, Literal, TypedDict

    class Point(TypedDict):
        x: int
        y: int

    def test(p: Point) -> None:
        X: Final = "x"
        p[X]  # OK

        Y: Literal["y"] = "y"
        p[Y]  # OK

        key = "x"  # Inferred type of key is `str`
        # Error: TypedDict key must be a string literal;
        #   expected one of ("x", "y")  [literal-required]
        p[key]

## Check that overloaded functions have an implementation [no-overload-impl]

Overloaded functions outside of stub files must be followed by a non overloaded implementation.

    from typing import overload

    @overload
    def func(value: int) -> int:
        ...

    @overload
    def func(value: str) -> str:
        ...

    # presence of required function below is checked
    def func(value):
        pass  # actual implementation

## Check that coroutine return value is used [unused-coroutine]

Mypy ensures that return values of async def functions are not ignored, as this is usually a programming error, as the coroutine won’t be executed at the call site.

    async def f() -> None:
        ...

    async def g() -> None:
        f()  # Error: missing await
        await f()  # OK

You can work around this error by assigning the result to a temporary, otherwise unused variable:

    _ = f()  # No error

## Warn about top level await expressions [top-level-await]

This error code is separate from the general `[syntax]` errors, because in some environments (e.g. IPython) a top level `await` is allowed. In such environments a user may want to use `--disable-error-code=top-level-await`, which allows one to still have errors for other improper uses of `await`, for example:

    async def f() -> None:
        ...

    top = await f()  # Error: "await" outside function  [top-level-await]

## Warn about await expressions used outside of coroutines [await-not-async]

`await` must be used inside a coroutine.

    async def f() -> None:
        ...

    def g() -> None:
        await f()  # Error: "await" outside coroutine ("async def")  [await-not-async]

## Check types in assert_type [assert-type]

The inferred type for an expression passed to `assert_type` must match the provided type.

    from typing_extensions import assert_type

    assert_type([1], list[int])  # OK

    assert_type([1], list[str])  # Error

## Check that function isn’t used in boolean context [truthy-function]

Functions will always evaluate to true in boolean contexts.

    def f():
        ...

    if f:  # Error: Function "Callable[[], Any]" could always be true in boolean context  [truthy-function]
        pass

## Check that string formatting/interpolation is type-safe [str-format]

Mypy will check that f-strings, `str.format()` calls, and `%` interpolations are valid (when corresponding template is a literal string). This includes checking number and types of replacements, for example:

    # Error: Cannot find replacement for positional format specifier 1 [str-format]
    "{} and {}".format("spam")
    "{} and {}".format("spam", "eggs")  # OK
    # Error: Not all arguments converted during string formatting [str-format]
    "{} and {}".format("spam", "eggs", "cheese")

    # Error: Incompatible types in string interpolation
    # (expression has type "float", placeholder has type "int") [str-format]
    "{:d}".format(3.14)

## Check for implicit bytes coercions [str-bytes-safe]

Warn about cases where a bytes object may be converted to a string in an unexpected manner.

    b = b"abc"

    # Error: If x = b'abc' then f"{x}" or "{}".format(x) produces "b'abc'", not "abc".
    # If this is desired behavior, use f"{x!r}" or "{!r}".format(x).
    # Otherwise, decode the bytes [str-bytes-safe]
    print(f"The alphabet starts with {b}")

    # Okay
    print(f"The alphabet starts with {b!r}")  # The alphabet starts with b'abc'
    print(f"The alphabet starts with {b.decode('utf-8')}")  # The alphabet starts with abc

## Check that `str` is not unpacked [str-unpack]

It can sometimes be surprising that `str` is iterable, especially when unpacking in an assignment.

Example:

    def print_dict(d: dict[str, str]) -> int:
        # We meant to do d.items(), but instead we're unpacking the str keys of d

        # Error: Unpacking a string is disallowed
        for k, v in d:
            print(k, v)

## Check that overloaded functions don’t overlap [overload-overlap]

Warn if multiple `@overload` variants overlap in potentially unsafe ways. This guards against the following situation:

    from typing import overload

    class A: ...
    class B(A): ...

    @overload
    def foo(x: B) -> int: ...  # Error: Overloaded function signatures 1 and 2 overlap with incompatible return types  [overload-overlap]
    @overload
    def foo(x: A) -> str: ...
    def foo(x): ...

    def takes_a(a: A) -> str:
        return foo(a)

    a: A = B()
    value = takes_a(a)
    # mypy will think that value is a str, but it could actually be an int
    reveal_type(value) # Revealed type is "builtins.str"

Note that in cases where you ignore this error, mypy will usually still infer the types you expect.

See overloading for more explanation.

## Check for overload signatures that cannot match [overload-cannot-match]

Warn if an `@overload` variant can never be matched, because an earlier overload has a wider signature. For example, this can happen if the two overloads accept the same parameters and each parameter on the first overload has the same type or a wider type than the corresponding parameter on the second overload.

Example:

    from typing import overload, Union

    @overload
    def process(response1: object, response2: object) -> object:
        ...
    @overload
    def process(response1: int, response2: int) -> int: # E: Overloaded function signature 2 will never be matched: signature 1's parameter type(s) are the same or broader  [overload-cannot-match]
        ...

    def process(response1: object, response2: object) -> object:
        return response1 + response2

## Notify about an annotation in an unchecked function [annotation-unchecked]

Sometimes a user may accidentally omit an annotation for a function, and mypy will not check the body of this function (unless one uses `--check-untyped-defs` or `--disallow-untyped-defs`). To avoid such situations go unnoticed, mypy will show a note, if there are any type annotations in an unchecked function:

    def test_assignment():  # "-> None" return annotation is missing
        # Note: By default the bodies of untyped functions are not checked,
        # consider using --check-untyped-defs [annotation-unchecked]
        x: int = "no way"

Note that mypy will still exit with return code `0`, since such behaviour is specified by PEP 484.

## Decorator preceding property not supported [prop-decorator]

Mypy does not yet support analysis of decorators that precede the property decorator. If the decorator does not preserve the declared type of the property, mypy will not infer the correct type for the declaration. If the decorator cannot be moved after the `@property` decorator, then you must use a type ignore comment:

    class MyClass:
        @special  # type: ignore[prop-decorator]
        @property
        def magic(self) -> str:
            return "xyzzy"

Note

For backward compatibility, this error code is a subcode of the generic `[misc]` code.

## Report syntax errors [syntax]

If the code being checked is not syntactically valid, mypy issues a syntax error. Most, but not all, syntax errors are blocking errors: they can’t be ignored with a `# type: ignore` comment.

## ReadOnly key of a TypedDict is mutated [typeddict-readonly-mutated]

Consider this example:

    from datetime import datetime
    from typing import TypedDict
    from typing_extensions import ReadOnly

    class User(TypedDict):
        username: ReadOnly[str]
        last_active: datetime

    user: User = {'username': 'foobar', 'last_active': datetime.now()}
    user['last_active'] = datetime.now()  # ok
    user['username'] = 'other'  # error: ReadOnly TypedDict key "key" TypedDict is mutated  [typeddict-readonly-mutated]

PEP 705 specifies how `ReadOnly` special form works for `TypedDict` objects.

## Check that `TypeIs` narrows types [narrowed-type-not-subtype]

PEP 742 requires that when `TypeIs` is used, the narrowed type must be a subtype of the original type:

    from typing_extensions import TypeIs

    def f(x: int) -> TypeIs[str]:  # Error, str is not a subtype of int
        ...

    def g(x: object) -> TypeIs[str]:  # OK
        ...

## String appears in a context which expects a TypeForm [maybe-unrecognized-str-typeform]

TypeForm literals may contain string annotations:

    typx1: TypeForm = str | None
    typx2: TypeForm = 'str | None'  # OK
    typx3: TypeForm = 'str' | None  # OK

However TypeForm literals containing a string annotation can only be recognized by mypy in the following locations:

    typx_var: TypeForm = 'str | None'  # assignment r-value

    def func(typx_param: TypeForm) -> TypeForm:
        return 'str | None'  # returned expression

    func('str | None')  # callable's argument

If you try to use a string annotation in some other location which expects a TypeForm, the string value will always be treated as a `str` even if a `TypeForm` would be more appropriate and this error code will be generated:

    # Error: TypeForm containing a string annotation cannot be recognized here. Surround with TypeForm(...) to recognize.  [maybe-unrecognized-str-typeform]
    # Error: List item 0 has incompatible type "str"; expected "TypeForm[Any]"  [list-item]
    list_of_typx: list[TypeForm] = ['str | None', float]

Fix the error by surrounding the entire type with `TypeForm(...)`:

    list_of_typx: list[TypeForm] = [TypeForm('str | None'), float]  # OK

Similarly, if you try to use a string literal in a location which expects a TypeForm, this error code will be generated:

    dict_of_typx = {'str_or_none': TypeForm(str | None)}
    # Error: TypeForm containing a string annotation cannot be recognized here. Surround with TypeForm(...) to recognize.  [maybe-unrecognized-str-typeform]
    list_of_typx: list[TypeForm] = [dict_of_typx['str_or_none']]

Fix the error by adding `# type: ignore[maybe-unrecognized-str-typeform]` to the line with the string literal:

    dict_of_typx = {'str_or_none': TypeForm(str | None)}
    list_of_typx: list[TypeForm] = [dict_of_typx['str_or_none']]  # type: ignore[maybe-unrecognized-str-typeform]

## Miscellaneous checks [misc]

Mypy performs numerous other, less commonly failing checks that don’t have specific error codes. These use the `misc` error code. Other than being used for multiple unrelated errors, the `misc` error code is not special. For example, you can ignore all errors in this category by using `# type: ignore[misc]` comment. Since these errors are not expected to be common, it’s unlikely that you’ll see two different errors with the `misc` code on a single line – though this can certainly happen once in a while.

Note

Future mypy versions will likely add new error codes for some errors that currently use the `misc` error code.