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  • ========================================
    
    :mod:`typing` --- Support for type hints
    ========================================
    
    .. module:: typing
    
       :synopsis: Support for type hints (see :pep:`484`).
    
    .. versionadded:: 3.5
    
    
       The Python runtime does not enforce function and variable type annotations.
       They can be used by third party tools such as type checkers, IDEs, linters,
       etc.
    
    This module provides runtime support for type hints. The most fundamental
    
    support consists of the types :data:`Any`, :data:`Union`, :data:`Callable`,
    :class:`TypeVar`, and :class:`Generic`. For a full specification, please see
    :pep:`484`. For a simplified introduction to type hints, see :pep:`483`.
    
    
    
    The function below takes and returns a string and is annotated as follows::
    
       def greeting(name: str) -> str:
           return 'Hello ' + name
    
    
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    In the function ``greeting``, the argument ``name`` is expected to be of type
    
    :class:`str` and the return type :class:`str`. Subtypes are accepted as
    arguments.
    
    New features are frequently added to the ``typing`` module.
    The `typing_extensions <https://pypi.org/project/typing-extensions/>`_ package
    provides backports of these new features to older versions of Python.
    
    
    .. _relevant-peps:
    
    Relevant PEPs
    =============
    
    Since the initial introduction of type hints in :pep:`484` and :pep:`483`, a
    number of PEPs have modified and enhanced Python's framework for type
    annotations. These include:
    
    * :pep:`526`: Syntax for Variable Annotations
         *Introducing* syntax for annotating variables outside of function
         definitions, and :data:`ClassVar`
    * :pep:`544`: Protocols: Structural subtyping (static duck typing)
         *Introducing* :class:`Protocol` and the
         :func:`@runtime_checkable<runtime_checkable>` decorator
    * :pep:`585`: Type Hinting Generics In Standard Collections
    
         *Introducing* :class:`types.GenericAlias` and the ability to use standard
         library classes as :ref:`generic types<types-genericalias>`
    
    * :pep:`586`: Literal Types
         *Introducing* :class:`Literal`
    * :pep:`589`: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys
         *Introducing* :class:`TypedDict`
    * :pep:`591`: Adding a final qualifier to typing
         *Introducing* :data:`Final` and the :func:`@final<final>` decorator
    * :pep:`593`: Flexible function and variable annotations
         *Introducing* :class:`Annotated`
    
    
    A type alias is defined by assigning the type to the alias. In this example,
    
    ``Vector`` and ``list[float]`` will be treated as interchangeable synonyms::
    
       def scale(scalar: float, vector: Vector) -> Vector:
           return [scalar * num for num in vector]
    
       # typechecks; a list of floats qualifies as a Vector.
       new_vector = scale(2.0, [1.0, -4.2, 5.4])
    
    Type aliases are useful for simplifying complex type signatures. For example::
    
    
       ConnectionOptions = dict[str, str]
       Address = tuple[str, int]
       Server = tuple[Address, ConnectionOptions]
    
       def broadcast_message(message: str, servers: Sequence[Server]) -> None:
    
           ...
    
       # The static type checker will treat the previous type signature as
       # being exactly equivalent to this one.
       def broadcast_message(
               message: str,
    
               servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
    
    Note that ``None`` as a type hint is a special case and is replaced by
    ``type(None)``.
    
    
    Use the :func:`NewType` helper to create distinct types::
    
    
       from typing import NewType
    
       UserId = NewType('UserId', int)
       some_id = UserId(524313)
    
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    The static type checker will treat the new type as if it were a subclass
    of the original type. This is useful in helping catch logical errors::
    
       def get_user_name(user_id: UserId) -> str:
           ...
    
       # typechecks
       user_a = get_user_name(UserId(42351))
    
       # does not typecheck; an int is not a UserId
       user_b = get_user_name(-1)
    
    You may still perform all ``int`` operations on a variable of type ``UserId``,
    but the result will always be of type ``int``. This lets you pass in a
    ``UserId`` wherever an ``int`` might be expected, but will prevent you from
    accidentally creating a ``UserId`` in an invalid way::
    
    
       # 'output' is of type 'int', not 'UserId'
    
    Note that these checks are enforced only by the static type checker. At runtime,
    
    the statement ``Derived = NewType('Derived', Base)`` will make ``Derived`` a
    
    callable that immediately returns whatever parameter you pass it. That means
    
    the expression ``Derived(some_value)`` does not create a new class or introduce
    any overhead beyond that of a regular function call.
    
    More precisely, the expression ``some_value is Derived(some_value)`` is always
    true at runtime.
    
    This also means that it is not possible to create a subtype of ``Derived``
    
    since it is an identity function at runtime, not an actual type::
    
    
       from typing import NewType
    
       UserId = NewType('UserId', int)
    
       # Fails at runtime and does not typecheck
       class AdminUserId(UserId): pass
    
    
    However, it is possible to create a :func:`NewType` based on a 'derived' ``NewType``::
    
       from typing import NewType
    
       UserId = NewType('UserId', int)
    
    
    and typechecking for ``ProUserId`` will work as expected.
    
    
    See :pep:`484` for more details.
    
    .. note::
    
       Recall that the use of a type alias declares two types to be *equivalent* to
       one another. Doing ``Alias = Original`` will make the static type checker
       treat ``Alias`` as being *exactly equivalent* to ``Original`` in all cases.
       This is useful when you want to simplify complex type signatures.
    
       In contrast, ``NewType`` declares one type to be a *subtype* of another.
       Doing ``Derived = NewType('Derived', Original)`` will make the static type
       checker treat ``Derived`` as a *subclass* of ``Original``, which means a
       value of type ``Original`` cannot be used in places where a value of type
       ``Derived`` is expected. This is useful when you want to prevent logic
       errors with minimal runtime cost.
    
    
    
    Frameworks expecting callback functions of specific signatures might be
    
    type hinted using ``Callable[[Arg1Type, Arg2Type], ReturnType]``.
    
    
       def feeder(get_next_item: Callable[[], str]) -> None:
           # Body
    
       def async_query(on_success: Callable[[int], None],
                       on_error: Callable[[int, Exception], None]) -> None:
           # Body
    
    
       async def on_update(value: str) -> None:
           # Body
       callback: Callable[[str], Awaitable[None]] = on_update
    
    
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    It is possible to declare the return type of a callable without specifying
    the call signature by substituting a literal ellipsis
    
    for the list of arguments in the type hint: ``Callable[..., ReturnType]``.
    
    
    Since type information about objects kept in containers cannot be statically
    inferred in a generic way, abstract base classes have been extended to support
    subscription to denote expected types for container elements.
    
    
    
       def notify_by_email(employees: Sequence[Employee],
                           overrides: Mapping[str, str]) -> None: ...
    
    
    Generics can be parameterized by using a factory available in typing
    
       from collections.abc import Sequence
       from typing import TypeVar
    
    
       T = TypeVar('T')      # Declare type variable
    
       def first(l: Sequence[T]) -> T:   # Generic function
           return l[0]
    
    
    
    A user-defined class can be defined as a generic class.
    
    
    
       from typing import TypeVar, Generic
       from logging import Logger
    
       T = TypeVar('T')
    
       class LoggedVar(Generic[T]):
           def __init__(self, value: T, name: str, logger: Logger) -> None:
               self.name = name
               self.logger = logger
               self.value = value
    
           def set(self, new: T) -> None:
               self.log('Set ' + repr(self.value))
               self.value = new
    
           def get(self) -> T:
               self.log('Get ' + repr(self.value))
               return self.value
    
           def log(self, message: str) -> None:
    
               self.logger.info('%s: %s', self.name, message)
    
    ``Generic[T]`` as a base class defines that the class ``LoggedVar`` takes a
    single type parameter ``T`` . This also makes ``T`` valid as a type within the
    class body.
    
    The :class:`Generic` base class defines :meth:`~object.__class_getitem__` so
    that ``LoggedVar[t]`` is valid as a type::
    
    
       def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
           for var in vars:
               var.set(0)
    
    
    A generic type can have any number of type variables. All varieties of
    :class:`TypeVar` are permissible as parameters for a generic type::
    
       T = TypeVar('T', contravariant=True)
       B = TypeVar('B', bound=Sequence[bytes], covariant=True)
    
    Each type variable argument to :class:`Generic` must be distinct.
    
    This is thus invalid::
    
       from typing import TypeVar, Generic
       ...
    
       T = TypeVar('T')
    
       class Pair(Generic[T, T]):   # INVALID
           ...
    
    
    You can use multiple inheritance with :class:`Generic`::
    
       from collections.abc import Sized
       from typing import TypeVar, Generic
    
    
       T = TypeVar('T')
    
       class LinkedList(Sized, Generic[T]):
           ...
    
    
    When inheriting from generic classes, some type variables could be fixed::
    
        from collections.abc import Mapping
        from typing import TypeVar
    
    
        T = TypeVar('T')
    
        class MyDict(Mapping[str, T]):
            ...
    
    In this case ``MyDict`` has a single parameter, ``T``.
    
    
    Using a generic class without specifying type parameters assumes
    :data:`Any` for each position. In the following example, ``MyIterable`` is
    
    not generic but implicitly inherits from ``Iterable[Any]``::
    
    
       class MyIterable(Iterable): # Same as Iterable[Any]
    
    
    User defined generic type aliases are also supported. Examples::
    
    
       from collections.abc import Iterable
       from typing import TypeVar, Union
    
       S = TypeVar('S')
       Response = Union[Iterable[S], int]
    
       # Return type here is same as Union[Iterable[str], int]
       def response(query: str) -> Response[str]:
           ...
    
       T = TypeVar('T', int, float, complex)
    
       def inproduct(v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
    
    .. versionchanged:: 3.7
        :class:`Generic` no longer has a custom metaclass.
    
    A user-defined generic class can have ABCs as base classes without a metaclass
    
    conflict. Generic metaclasses are not supported. The outcome of parameterizing
    generics is cached, and most types in the typing module are hashable and
    comparable for equality.
    
    A special kind of type is :data:`Any`. A static type checker will treat
    every type as being compatible with :data:`Any` and :data:`Any` as being
    
    This means that it is possible to perform any operation or method call on a
    
    value of type :data:`Any` and assign it to any variable::
    
           # Typechecks; 'item' could be any type,
           # and that type might have a 'bar' method
    
           item.bar()
           ...
    
    Notice that no typechecking is performed when assigning a value of type
    
    :data:`Any` to a more precise type. For example, the static type checker did
    
    not report an error when assigning ``a`` to ``s`` even though ``s`` was
    declared to be of type :class:`str` and receives an :class:`int` value at
    runtime!
    
    Furthermore, all functions without a return type or parameter types will
    
    implicitly default to using :data:`Any`::
    
    
       def legacy_parser(text):
           ...
           return data
    
       # A static type checker will treat the above
       # as having the same signature as:
       def legacy_parser(text: Any) -> Any:
           ...
           return data
    
    
    This behavior allows :data:`Any` to be used as an *escape hatch* when you
    
    need to mix dynamically and statically typed code.
    
    
    Contrast the behavior of :data:`Any` with the behavior of :class:`object`.
    Similar to :data:`Any`, every type is a subtype of :class:`object`. However,
    unlike :data:`Any`, the reverse is not true: :class:`object` is *not* a
    
    subtype of every other type.
    
    That means when the type of a value is :class:`object`, a type checker will
    reject almost all operations on it, and assigning it to a variable (or using
    it as a return value) of a more specialized type is a type error. For example::
    
       def hash_a(item: object) -> int:
    
           # Fails; an object does not have a 'magic' method.
    
           item.magic()
           ...
    
       def hash_b(item: Any) -> int:
           # Typechecks
           item.magic()
           ...
    
       # Typechecks, since ints and strs are subclasses of object
       hash_a(42)
       hash_a("foo")
    
       # Typechecks, since Any is compatible with all types
       hash_b(42)
       hash_b("foo")
    
    Use :class:`object` to indicate that a value could be any type in a typesafe
    
    manner. Use :data:`Any` to indicate that a value is dynamically typed.
    
    Initially :pep:`484` defined the Python static type system as using
    
    *nominal subtyping*. This means that a class ``A`` is allowed where
    a class ``B`` is expected if and only if ``A`` is a subclass of ``B``.
    
    This requirement previously also applied to abstract base classes, such as
    
    :class:`~collections.abc.Iterable`. The problem with this approach is that a class had
    
    to be explicitly marked to support them, which is unpythonic and unlike
    what one would normally do in idiomatic dynamically typed Python code.
    
    For example, this conforms to :pep:`484`::
    
       from collections.abc import Sized, Iterable, Iterator
    
    
       class Bucket(Sized, Iterable[int]):
           ...
           def __len__(self) -> int: ...
           def __iter__(self) -> Iterator[int]: ...
    
    :pep:`544` allows to solve this problem by allowing users to write
    the above code without explicit base classes in the class definition,
    allowing ``Bucket`` to be implicitly considered a subtype of both ``Sized``
    and ``Iterable[int]`` by static type checkers. This is known as
    *structural subtyping* (or static duck-typing)::
    
    
    
       class Bucket:  # Note: no base classes
           ...
           def __len__(self) -> int: ...
           def __iter__(self) -> Iterator[int]: ...
    
       def collect(items: Iterable[int]) -> int: ...
       result = collect(Bucket())  # Passes type check
    
    Moreover, by subclassing a special class :class:`Protocol`, a user
    can define new custom protocols to fully enjoy structural subtyping
    (see examples below).
    
    
    The module defines the following classes, functions and decorators.
    
       This module defines several types that are subclasses of pre-existing
       standard library classes which also extend :class:`Generic`
       to support type variables inside ``[]``.
       These types became redundant in Python 3.9 when the
       corresponding pre-existing classes were enhanced to support ``[]``.
    
       The redundant types are deprecated as of Python 3.9 but no
       deprecation warnings will be issued by the interpreter.
       It is expected that type checkers will flag the deprecated types
       when the checked program targets Python 3.9 or newer.
    
       The deprecated types will be removed from the :mod:`typing` module
       in the first Python version released 5 years after the release of Python 3.9.0.
       See details in :pep:`585`—*Type Hinting Generics In Standard Collections*.
    
    Special typing primitives
    -------------------------
    
    These can be used as types in annotations and do not support ``[]``.
    
       Special type indicating an unconstrained type.
    
       * Every type is compatible with :data:`Any`.
       * :data:`Any` is compatible with every type.
    
       Special type indicating that a function never returns.
       For example::
    
          def stop() -> NoReturn:
              raise RuntimeError('no way')
    
       .. versionadded:: 3.5.4
       .. versionadded:: 3.6.2
    
    These can be used as types in annotations using ``[]``, each having a unique syntax.
    
       Tuple type; ``Tuple[X, Y]`` is the type of a tuple of two items
       with the first item of type X and the second of type Y. The type of
       the empty tuple can be written as ``Tuple[()]``.
    
       Example: ``Tuple[T1, T2]`` is a tuple of two elements corresponding
       to type variables T1 and T2.  ``Tuple[int, float, str]`` is a tuple
       of an int, a float and a string.
    
       To specify a variable-length tuple of homogeneous type,
       use literal ellipsis, e.g. ``Tuple[int, ...]``. A plain :data:`Tuple`
       is equivalent to ``Tuple[Any, ...]``, and in turn to :class:`tuple`.
    
          :class:`builtins.tuple <tuple>` now supports ``[]``. See :pep:`585` and
          :ref:`types-genericalias`.
    
       Union type; ``Union[X, Y]`` means either X or Y.
    
       To define a union, use e.g. ``Union[int, str]``.  Details:
    
       * The arguments must be types and there must be at least one.
    
       * Unions of unions are flattened, e.g.::
    
           Union[Union[int, str], float] == Union[int, str, float]
    
       * Unions of a single argument vanish, e.g.::
    
           Union[int] == int  # The constructor actually returns int
    
       * Redundant arguments are skipped, e.g.::
    
           Union[int, str, int] == Union[int, str]
    
       * When comparing unions, the argument order is ignored, e.g.::
    
           Union[int, str] == Union[str, int]
    
       * You cannot subclass or instantiate a union.
    
       * You cannot write ``Union[X][Y]``.
    
       * You can use ``Optional[X]`` as a shorthand for ``Union[X, None]``.
    
       .. versionchanged:: 3.7
          Don't remove explicit subclasses from unions at runtime.
    
    .. data:: Optional
    
       Optional type.
    
       ``Optional[X]`` is equivalent to ``Union[X, None]``.
    
       Note that this is not the same concept as an optional argument,
       which is one that has a default.  An optional argument with a
       default does not require the ``Optional`` qualifier on its type
       annotation just because it is optional. For example::
    
          def foo(arg: int = 0) -> None:
              ...
    
       On the other hand, if an explicit value of ``None`` is allowed, the
       use of ``Optional`` is appropriate, whether the argument is optional
       or not. For example::
    
          def foo(arg: Optional[int] = None) -> None:
              ...
    
    .. data:: Callable
    
       Callable type; ``Callable[[int], str]`` is a function of (int) -> str.
    
       The subscription syntax must always be used with exactly two
       values: the argument list and the return type.  The argument list
       must be a list of types or an ellipsis; the return type must be
       a single type.
    
       There is no syntax to indicate optional or keyword arguments;
       such function types are rarely used as callback types.
       ``Callable[..., ReturnType]`` (literal ellipsis) can be used to
       type hint a callable taking any number of arguments and returning
       ``ReturnType``.  A plain :data:`Callable` is equivalent to
       ``Callable[..., Any]``, and in turn to
       :class:`collections.abc.Callable`.
    
       .. deprecated:: 3.9
    
          :class:`collections.abc.Callable` now supports ``[]``. See :pep:`585` and
          :ref:`types-genericalias`.
    
    .. class:: Type(Generic[CT_co])
    
    
       A variable annotated with ``C`` may accept a value of type ``C``. In
       contrast, a variable annotated with ``Type[C]`` may accept values that are
       classes themselves -- specifically, it will accept the *class object* of
       ``C``. For example::
    
          a = 3         # Has type 'int'
          b = int       # Has type 'Type[int]'
          c = type(a)   # Also has type 'Type[int]'
    
       Note that ``Type[C]`` is covariant::
    
          class User: ...
          class BasicUser(User): ...
          class ProUser(User): ...
          class TeamUser(User): ...
    
          # Accepts User, BasicUser, ProUser, TeamUser, ...
          def make_new_user(user_class: Type[User]) -> User:
              # ...
              return user_class()
    
       The fact that ``Type[C]`` is covariant implies that all subclasses of
       ``C`` should implement the same constructor signature and class method
       signatures as ``C``. The type checker should flag violations of this,
       but should also allow constructor calls in subclasses that match the
       constructor calls in the indicated base class. How the type checker is
       required to handle this particular case may change in future revisions of
    
       The only legal parameters for :class:`Type` are classes, :data:`Any`,
       :ref:`type variables <generics>`, and unions of any of these types.
       For example::
    
          def new_non_team_user(user_class: Type[Union[BasicUser, ProUser]]): ...
    
    
       ``Type[Any]`` is equivalent to ``Type`` which in turn is equivalent
       to ``type``, which is the root of Python's metaclass hierarchy.
    
    
          :class:`builtins.type <type>` now supports ``[]``. See :pep:`585` and
          :ref:`types-genericalias`.
    
       A type that can be used to indicate to type checkers that the
       corresponding variable or function parameter has a value equivalent to
       the provided literal (or one of several literals). For example::
    
          def validate_simple(data: Any) -> Literal[True]:  # always returns True
              ...
    
          MODE = Literal['r', 'rb', 'w', 'wb']
          def open_helper(file: str, mode: MODE) -> str:
              ...
    
          open_helper('/some/path', 'r')  # Passes type check
          open_helper('/other/path', 'typo')  # Error in type checker
    
       ``Literal[...]`` cannot be subclassed. At runtime, an arbitrary value
       is allowed as type argument to ``Literal[...]``, but type checkers may
       impose restrictions. See :pep:`586` for more details about literal types.
    
          ``Literal`` now de-duplicates parameters.  Equality comparisons of
    
          ``Literal`` objects are no longer order dependent. ``Literal`` objects
          will now raise a :exc:`TypeError` exception during equality comparisons
    
          if one of their parameters are not :term:`hashable`.
    
       Special type construct to mark class variables.
    
       As introduced in :pep:`526`, a variable annotation wrapped in ClassVar
       indicates that a given attribute is intended to be used as a class variable
       and should not be set on instances of that class. Usage::
    
              stats: ClassVar[dict[str, int]] = {} # class variable
    
              damage: int = 10                     # instance variable
    
       :data:`ClassVar` accepts only types and cannot be further subscribed.
    
       :data:`ClassVar` is not a class itself, and should not
       be used with :func:`isinstance` or :func:`issubclass`.
       :data:`ClassVar` does not change Python runtime behavior, but
       it can be used by third-party type checkers. For example, a type checker
       might flag the following code as an error::
    
          enterprise_d = Starship(3000)
          enterprise_d.stats = {} # Error, setting class variable on instance
          Starship.stats = {}     # This is OK
    
       A special typing construct to indicate to type checkers that a name
       cannot be re-assigned or overridden in a subclass. For example::
    
          MAX_SIZE: Final = 9000
          MAX_SIZE += 1  # Error reported by type checker
    
          class Connection:
              TIMEOUT: Final[int] = 10
    
          class FastConnector(Connection):
              TIMEOUT = 1  # Error reported by type checker
    
       There is no runtime checking of these properties. See :pep:`591` for
       more details.
    
       A type, introduced in :pep:`593` (``Flexible function and variable
       annotations``), to decorate existing types with context-specific metadata
       (possibly multiple pieces of it, as ``Annotated`` is variadic).
       Specifically, a type ``T`` can be annotated with metadata ``x`` via the
       typehint ``Annotated[T, x]``. This metadata can be used for either static
       analysis or at runtime. If a library (or tool) encounters a typehint
       ``Annotated[T, x]`` and has no special logic for metadata ``x``, it
       should ignore it and simply treat the type as ``T``. Unlike the
       ``no_type_check`` functionality that currently exists in the ``typing``
       module which completely disables typechecking annotations on a function
       or a class, the ``Annotated`` type allows for both static typechecking
    
       of ``T`` (which can safely ignore ``x``)
    
       together with runtime access to ``x`` within a specific application.
    
       Ultimately, the responsibility of how to interpret the annotations (if
       at all) is the responsibility of the tool or library encountering the
       ``Annotated`` type. A tool or library encountering an ``Annotated`` type
       can scan through the annotations to determine if they are of interest
       (e.g., using ``isinstance()``).
    
       When a tool or a library does not support annotations or encounters an
       unknown annotation it should just ignore it and treat annotated type as
       the underlying type.
    
       It's up to the tool consuming the annotations to decide whether the
       client is allowed to have several annotations on one type and how to
       merge those annotations.
    
       Since the ``Annotated`` type allows you to put several annotations of
       the same (or different) type(s) on any node, the tools or libraries
       consuming those annotations are in charge of dealing with potential
       duplicates. For example, if you are doing value range analysis you might
       allow this::
    
           T1 = Annotated[int, ValueRange(-10, 5)]
           T2 = Annotated[T1, ValueRange(-20, 3)]
    
       Passing ``include_extras=True`` to :func:`get_type_hints` lets one
       access the extra annotations at runtime.
    
       * The first argument to ``Annotated`` must be a valid type
    
       * Multiple type annotations are supported (``Annotated`` supports variadic
         arguments)::
    
           Annotated[int, ValueRange(3, 10), ctype("char")]
    
       * ``Annotated`` must be called with at least two arguments (
         ``Annotated[int]`` is not valid)
    
       * The order of the annotations is preserved and matters for equality
         checks::
    
           Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
               int, ctype("char"), ValueRange(3, 10)
           ]
    
       * Nested ``Annotated`` types are flattened, with metadata ordered
         starting with the innermost annotation::
    
           Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
               int, ValueRange(3, 10), ctype("char")
           ]
    
       * Duplicated annotations are not removed::
    
           Annotated[int, ValueRange(3, 10)] != Annotated[
               int, ValueRange(3, 10), ValueRange(3, 10)
           ]
    
       * ``Annotated`` can be used with nested and generic aliases::
    
           Vec = Annotated[list[tuple[T, T]], MaxLen(10)]
    
           V == Annotated[list[tuple[int, int]], MaxLen(10)]
    
    Building generic types
    """"""""""""""""""""""
    
    These are not used in annotations. They are building blocks for creating generic types.
    
       Abstract base class for generic types.
    
       A generic type is typically declared by inheriting from an
       instantiation of this class with one or more type variables.
       For example, a generic mapping type might be defined as::
    
          class Mapping(Generic[KT, VT]):
              def __getitem__(self, key: KT) -> VT:
                  ...
                  # Etc.
    
       This class can then be used as follows::
    
          def lookup_name(mapping: Mapping[X, Y], key: X, default: Y) -> Y:
              try:
                  return mapping[key]
              except KeyError:
                  return default
    
          S = TypeVar('S', bound=str)  # Can be any subtype of str
          A = TypeVar('A', str, bytes)  # Must be exactly str or bytes
    
        Type variables exist primarily for the benefit of static type
        checkers.  They serve as the parameters for generic types as well
    
        as for generic function definitions.  See :class:`Generic` for more
    
        information on generic types.  Generic functions work as follows::
    
           def repeat(x: T, n: int) -> Sequence[T]:
               """Return a list containing n references to x."""
               return [x]*n
    
           def print_capitalized(x: S) -> S:
               """Print x capitalized, and return x."""
               print(x.capitalize())
               return x
    
    
           def concatenate(x: A, y: A) -> A:
               """Add two strings or bytes objects together."""
               return x + y
    
        Note that type variables can be *bound*, *constrained*, or neither, but
        cannot be both bound *and* constrained.
    
        Constrained type variables and bound type variables have different
        semantics in several important ways. Using a *constrained* type variable
        means that the ``TypeVar`` can only ever be solved as being exactly one of
        the constraints given::
    
           a = concatenate('one', 'two')  # Ok, variable 'a' has type 'str'
           b = concatenate(StringSubclass('one'), StringSubclass('two'))  # Inferred type of variable 'b' is 'str',
                                                                          # despite 'StringSubclass' being passed in
           c = concatenate('one', b'two')  # error: type variable 'A' can be either 'str' or 'bytes' in a function call, but not both
    
        Using a *bound* type variable, however, means that the ``TypeVar`` will be
        solved using the most specific type possible::
    
           print_capitalized('a string')  # Ok, output has type 'str'
    
           class StringSubclass(str):
               pass
    
           print_capitalized(StringSubclass('another string'))  # Ok, output has type 'StringSubclass'
           print_capitalized(45)  # error: int is not a subtype of str
    
        Type variables can be bound to concrete types, abstract types (ABCs or
        protocols), and even unions of types::
    
           U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
           V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method
    
        Bound type variables are particularly useful for annotating
        :func:`classmethods <classmethod>` that serve as alternative constructors.
        In the following example (©
        `Raymond Hettinger <https://www.youtube.com/watch?v=HTLu2DFOdTg>`_), the
        type variable ``C`` is bound to the ``Circle`` class through the use of a
        forward reference. Using this type variable to annotate the
        ``with_circumference`` classmethod, rather than hardcoding the return type
        as ``Circle``, means that a type checker can correctly infer the return
        type even if the method is called on a subclass::
    
           import math
    
           C = TypeVar('C', bound='Circle')
    
           class Circle:
               """An abstract circle"""
    
               def __init__(self, radius: float) -> None:
                   self.radius = radius
    
               # Use a type variable to show that the return type
    
               # will always be an instance of whatever ``cls`` is
    
               @classmethod
               def with_circumference(cls: type[C], circumference: float) -> C:
                   """Create a circle with the specified circumference"""
                   radius = circumference / (math.pi * 2)
                   return cls(radius)
    
    
           class Tire(Circle):
               """A specialised circle (made out of rubber)"""
    
               MATERIAL = 'rubber'
    
    
           c = Circle.with_circumference(3)  # Ok, variable 'c' has type 'Circle'
           t = Tire.with_circumference(4)  # Ok, variable 't' has type 'Tire' (not 'Circle')
    
        At runtime, ``isinstance(x, T)`` will raise :exc:`TypeError`.  In general,
        :func:`isinstance` and :func:`issubclass` should not be used with types.
    
        Type variables may be marked covariant or contravariant by passing
        ``covariant=True`` or ``contravariant=True``.  See :pep:`484` for more
    
        details.  By default, type variables are invariant.
    
       ``AnyStr`` is a :class:`constrained type variable <TypeVar>` defined as
    
       ``AnyStr = TypeVar('AnyStr', str, bytes)``.
    
       It is meant to be used for functions that may accept any kind of string
       without allowing different kinds of strings to mix. For example::
    
          def concat(a: AnyStr, b: AnyStr) -> AnyStr:
              return a + b
    
          concat(u"foo", u"bar")  # Ok, output has type 'unicode'
          concat(b"foo", b"bar")  # Ok, output has type 'bytes'
          concat(u"foo", b"bar")  # Error, cannot mix unicode and bytes
    
       Base class for protocol classes. Protocol classes are defined like this::
    
          class Proto(Protocol):
              def meth(self) -> int:
                  ...
    
       Such classes are primarily used with static type checkers that recognize
       structural subtyping (static duck-typing), for example::
    
          def func(x: Proto) -> int:
              return x.meth()
    
          func(C())  # Passes static type check
    
       See :pep:`544` for details. Protocol classes decorated with
       :func:`runtime_checkable` (described later) act as simple-minded runtime
       protocols that check only the presence of given attributes, ignoring their
       type signatures.