"dataclasses" --- Data Classes
******************************

**Código fuente:** Lib/dataclasses.py

======================================================================

This module provides a decorator and functions for automatically
adding generated *special methods* such as "__init__()" and
"__repr__()" to user-defined classes.  It was originally described in
**PEP 557**.

Las variables miembro a utilizar en estos métodos generados son
definidas teniendo en cuenta anotaciones de tipo **PEP 526**. Por
ejemplo, en este código:

   from dataclasses import dataclass

   @dataclass
   class InventoryItem:
       """Class for keeping track of an item in inventory."""
       name: str
       unit_price: float
       quantity_on_hand: int = 0

       def total_cost(self) -> float:
           return self.unit_price * self.quantity_on_hand

will add, among other things, a "__init__()" that looks like:

   def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
       self.name = name
       self.unit_price = unit_price
       self.quantity_on_hand = quantity_on_hand

Note that this method is automatically added to the class: it is not
directly specified in the "InventoryItem" definition shown above.

Added in version 3.7.


Contenidos del módulo
=====================

@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)

   This function is a *decorator* that is used to add generated
   *special methods* to classes, as described below.

   The "@dataclass" decorator examines the class to find "field"s.  A
   "field" is defined as a class variable that has a *type
   annotation*.  With two exceptions described below, nothing in
   "@dataclass" examines the type specified in the variable
   annotation.

   El orden de los campos en los métodos generados es el mismo en el
   que se encuentran en la definición de la clase.

   The "@dataclass" decorator will add various "dunder" methods to the
   class, described below.  If any of the added methods already exist
   in the class, the behavior depends on the parameter, as documented
   below. The decorator returns the same class that it is called on;
   no new class is created.

   If "@dataclass" is used just as a simple decorator with no
   parameters, it acts as if it has the default values documented in
   this signature.  That is, these three uses of "@dataclass" are
   equivalent:

      @dataclass
      class C:
          ...

      @dataclass()
      class C:
          ...

      @dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False,
                 match_args=True, kw_only=False, slots=False, weakref_slot=False)
      class C:
          ...

   The parameters to "@dataclass" are:

   * *init*: If true (the default), a "__init__()" method will be
     generated.

     If the class already defines "__init__()", this parameter is
     ignored.

   * *repr*: If true (the default), a "__repr__()" method will be
     generated.  The generated repr string will have the class name
     and the name and repr of each field, in the order they are
     defined in the class.  Fields that are marked as being excluded
     from the repr are not included.  For example:
     "InventoryItem(name='widget', unit_price=3.0,
     quantity_on_hand=10)".

     If the class already defines "__repr__()", this parameter is
     ignored.

   * *eq*: If true (the default), an "__eq__()" method will be
     generated.  This method compares the class as if it were a tuple
     of its fields, in order.  Both instances in the comparison must
     be of the identical type.

     If the class already defines "__eq__()", this parameter is
     ignored.

   * *order*: If true (the default is "False"), "__lt__()",
     "__le__()", "__gt__()", and "__ge__()" methods will be generated.
     These compare the class as if it were a tuple of its fields, in
     order.  Both instances in the comparison must be of the identical
     type.  If *order* is true and *eq* is false, a "ValueError" is
     raised.

     If the class already defines any of "__lt__()", "__le__()",
     "__gt__()", or "__ge__()", then "TypeError" is raised.

   * *unsafe_hash*: If "False" (the default), a "__hash__()" method is
     generated according to how *eq* and *frozen* are set.

     "__hash__()" is used by built-in "hash()", and when objects are
     added to hashed collections such as dictionaries and sets.
     Having a "__hash__()" implies that instances of the class are
     immutable. Mutability is a complicated property that depends on
     the programmer's intent, the existence and behavior of
     "__eq__()", and the values of the *eq* and *frozen* flags in the
     "@dataclass" decorator.

     By default, "@dataclass" will not implicitly add a "__hash__()"
     method unless it is safe to do so.  Neither will it add or change
     an existing explicitly defined "__hash__()" method.  Setting the
     class attribute "__hash__ = None" has a specific meaning to
     Python, as described in the "__hash__()" documentation.

     If "__hash__()" is not explicitly defined, or if it is set to
     "None", then "@dataclass" *may* add an implicit "__hash__()"
     method. Although not recommended, you can force "@dataclass" to
     create a "__hash__()" method with "unsafe_hash=True". This might
     be the case if your class is logically immutable but can still be
     mutated. This is a specialized use case and should be considered
     carefully.

     Here are the rules governing implicit creation of a "__hash__()"
     method.  Note that you cannot both have an explicit "__hash__()"
     method in your dataclass and set "unsafe_hash=True"; this will
     result in a "TypeError".

     If *eq* and *frozen* are both true, by default "@dataclass" will
     generate a "__hash__()" method for you.  If *eq* is true and
     *frozen* is false, "__hash__()" will be set to "None", marking it
     unhashable (which it is, since it is mutable).  If *eq* is false,
     "__hash__()" will be left untouched meaning the "__hash__()"
     method of the superclass will be used (if the superclass is
     "object", this means it will fall back to id-based hashing).

   * *frozen*: If true (the default is "False"), assigning to fields
     will generate an exception.  This emulates read-only frozen
     instances.  If "__setattr__()" or "__delattr__()" is defined in
     the class, then "TypeError" is raised.  See the discussion below.

   * *match_args*: If true (the default is "True"), the
     "__match_args__" tuple will be created from the list of non
     keyword-only parameters to the generated "__init__()" method
     (even if "__init__()" is not generated, see above).  If false, or
     if "__match_args__" is already defined in the class, then
     "__match_args__" will not be generated.

      Added in version 3.10.

   * *kw_only*: If true (the default value is "False"), then all
     fields will be marked as keyword-only.  If a field is marked as
     keyword-only, then the only effect is that the "__init__()"
     parameter generated from a keyword-only field must be specified
     with a keyword when "__init__()" is called. See the *parameter*
     glossary entry for details.  Also see the "KW_ONLY" section.

     Keyword-only fields are not included in "__match_args__".

      Added in version 3.10.

   * *slots*: If true (the default is "False"), "__slots__" attribute
     will be generated and new class will be returned instead of the
     original one. If "__slots__" is already defined in the class,
     then "TypeError" is raised.

      Advertencia:

        Calling no-arg "super()" in dataclasses using "slots=True"
        will result in the following exception being raised:
        "TypeError: super(type, obj): obj must be an instance or
        subtype of type". The two-arg "super()" is a valid workaround.
        See gh-90562 for full details.

      Advertencia:

        Passing parameters to a base class "__init_subclass__()" when
        using "slots=True" will result in a "TypeError". Either use
        "__init_subclass__" with no parameters or use default values
        as a workaround. See gh-91126 for full details.

      Added in version 3.10.

      Distinto en la versión 3.11: If a field name is already included
      in the "__slots__" of a base class, it will not be included in
      the generated "__slots__" to prevent overriding them. Therefore,
      do not use "__slots__" to retrieve the field names of a
      dataclass. Use "fields()" instead. To be able to determine
      inherited slots, base class "__slots__" may be any iterable, but
      *not* an iterator.

   * *weakref_slot*: If true (the default is "False"), add a slot
     named "__weakref__", which is required to make an instance
     "weakref-able". It is an error to specify "weakref_slot=True"
     without also specifying "slots=True".

      Added in version 3.11.

   Los "fields" pueden especificar un valor por defecto opcionalmente,
   simplemente usando la sintaxis normal de Python:

      @dataclass
      class C:
          a: int       # 'a' has no default value
          b: int = 0   # assign a default value for 'b'

   In this example, both "a" and "b" will be included in the added
   "__init__()" method, which will be defined as:

      def __init__(self, a: int, b: int = 0):

   Si, en la definición de una clase, a un campo con valor por defecto
   le sigue un campo sin valor por defecto será lanzada una excepción
   "TypeError". Esto se aplica también a la implementación de una
   clase única o como resultado de herencia de clases.

dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)

   For common and simple use cases, no other functionality is
   required.  There are, however, some dataclass features that require
   additional per-field information.  To satisfy this need for
   additional information, you can replace the default field value
   with a call to the provided "field()" function.  For example:

      @dataclass
      class C:
          mylist: list[int] = field(default_factory=list)

      c = C()
      c.mylist += [1, 2, 3]

   Como se muestra arriba, el valor "MISSING" es un objeto centinela
   que se usa para detectar si el usuario proporciona algunos
   parámetros. Este centinela se utiliza porque "None" es un valor
   válido para algunos parámetros con un significado distinto. Ningún
   código debe utilizar directamente el valor "MISSING".

   The parameters to "field()" are:

   * *default*: If provided, this will be the default value for this
     field.  This is needed because the "field()" call itself replaces
     the normal position of the default value.

   * *default_factory*: If provided, it must be a zero-argument
     callable that will be called when a default value is needed for
     this field.  Among other purposes, this can be used to specify
     fields with mutable default values, as discussed below.  It is an
     error to specify both *default* and *default_factory*.

   * *init*: If true (the default), this field is included as a
     parameter to the generated "__init__()" method.

   * *repr*: If true (the default), this field is included in the
     string returned by the generated "__repr__()" method.

   * *hash*: This can be a bool or "None".  If true, this field is
     included in the generated "__hash__()" method.  If false, this
     field is excluded from the generated "__hash__()". If "None" (the
     default), use the value of *compare*: this would normally be the
     expected behavior, since a field should be included in the hash
     if it's used for comparisons.  Setting this value to anything
     other than "None" is discouraged.

     Una posible razón para definir "hash=False" y "compare=True"
     podría ser el caso en el que computar el valor *hash* para dicho
     campo es costoso pero el campo es necesario para los métodos de
     comparación, siempre que existan otros campos que contribuyen al
     valor hash del tipo. Incluso si un campo se excluye del hash, se
     seguirá utilizando a la hora de comparar.

   * *compare*: If true (the default), this field is included in the
     generated equality and comparison methods ("__eq__()",
     "__gt__()", et al.).

   * *metadata*: This can be a mapping or "None". "None" is treated as
     an empty dict.  This value is wrapped in "MappingProxyType()" to
     make it read-only, and exposed on the "Field" object. It is not
     used at all by Data Classes, and is provided as a third-party
     extension mechanism. Multiple third-parties can each have their
     own key, to use as a namespace in the metadata.

   * *kw_only*: If true, this field will be marked as keyword-only.
     This is used when the generated "__init__()" method's parameters
     are computed.

     Keyword-only fields are also not included in "__match_args__".

      Added in version 3.10.

   If the default value of a field is specified by a call to
   "field()", then the class attribute for this field will be replaced
   by the specified *default* value.  If *default* is not provided,
   then the class attribute will be deleted.  The intent is that after
   the "@dataclass" decorator runs, the class attributes will all
   contain the default values for the fields, just as if the default
   value itself were specified.  For example, after:

      @dataclass
      class C:
          x: int
          y: int = field(repr=False)
          z: int = field(repr=False, default=10)
          t: int = 20

   The class attribute "C.z" will be "10", the class attribute "C.t"
   will be "20", and the class attributes "C.x" and "C.y" will not be
   set.

class dataclasses.Field

   "Field" objects describe each defined field. These objects are
   created internally, and are returned by the "fields()" module-level
   method (see below).  Users should never instantiate a "Field"
   object directly.  Its documented attributes are:

   * "name": The name of the field.

   * "type": The type of the field.

   * "default", "default_factory", "init", "repr", "hash", "compare",
     "metadata", and "kw_only" have the identical meaning and values
     as they do in the "field()" function.

   Pueden existir otros atributos, pero son privados y no deberían ser
   considerados ni depender de ellos.

dataclasses.fields(class_or_instance)

   Retorna una tupla de objetos "Field" que definen los campos para
   esta clase de datos. Acepta tanto una clase de datos como una
   instancia de esta. Lanza una excepción "TypeError" si se le pasa
   cualquier otro objeto. No retorna pseudocampos, que son "ClassVar"
   o "InitVar".

dataclasses.asdict(obj, *, dict_factory=dict)

   Converts the dataclass *obj* to a dict (by using the factory
   function *dict_factory*).  Each dataclass is converted to a dict of
   its fields, as "name: value" pairs.  dataclasses, dicts, lists, and
   tuples are recursed into.  Other objects are copied with
   "copy.deepcopy()".

   Example of using "asdict()" on nested dataclasses:

      @dataclass
      class Point:
           x: int
           y: int

      @dataclass
      class C:
           mylist: list[Point]

      p = Point(10, 20)
      assert asdict(p) == {'x': 10, 'y': 20}

      c = C([Point(0, 0), Point(10, 4)])
      assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}

   To create a shallow copy, the following workaround may be used:

      {field.name: getattr(obj, field.name) for field in fields(obj)}

   "asdict()" raises "TypeError" if *obj* is not a dataclass instance.

dataclasses.astuple(obj, *, tuple_factory=tuple)

   Converts the dataclass *obj* to a tuple (by using the factory
   function *tuple_factory*).  Each dataclass is converted to a tuple
   of its field values.  dataclasses, dicts, lists, and tuples are
   recursed into. Other objects are copied with "copy.deepcopy()".

   Continuando con el ejemplo anterior:

      assert astuple(p) == (10, 20)
      assert astuple(c) == ([(0, 0), (10, 4)],)

   To create a shallow copy, the following workaround may be used:

      tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))

   "astuple()" raises "TypeError" if *obj* is not a dataclass
   instance.

dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False, module=None)

   Creates a new dataclass with name *cls_name*, fields as defined in
   *fields*, base classes as given in *bases*, and initialized with a
   namespace as given in *namespace*.  *fields* is an iterable whose
   elements are each either "name", "(name, type)", or "(name, type,
   Field)".  If just "name" is supplied, "typing.Any" is used for
   "type".  The values of *init*, *repr*, *eq*, *order*,
   *unsafe_hash*, *frozen*, *match_args*, *kw_only*, *slots*, and
   *weakref_slot* have the same meaning as they do in "@dataclass".

   If *module* is defined, the "__module__" attribute of the dataclass
   is set to that value. By default, it is set to the module name of
   the caller.

   This function is not strictly required, because any Python
   mechanism for creating a new class with "__annotations__" can then
   apply the "@dataclass" function to convert that class to a
   dataclass.  This function is provided as a convenience.  For
   example:

      C = make_dataclass('C',
                         [('x', int),
                           'y',
                          ('z', int, field(default=5))],
                         namespace={'add_one': lambda self: self.x + 1})

   Es equivalente a:

      @dataclass
      class C:
          x: int
          y: 'typing.Any'
          z: int = 5

          def add_one(self):
              return self.x + 1

dataclasses.replace(obj, /, **changes)

   Creates a new object of the same type as *obj*, replacing fields
   with values from *changes*.  If *obj* is not a Data Class, raises
   "TypeError".  If keys in *changes* are not field names of the given
   dataclass, raises "TypeError".

   The newly returned object is created by calling the "__init__()"
   method of the dataclass.  This ensures that "__post_init__()", if
   present, is also called.

   Init-only variables without default values, if any exist, must be
   specified on the call to "replace()" so that they can be passed to
   "__init__()" and "__post_init__()".

   It is an error for *changes* to contain any fields that are defined
   as having "init=False".  A "ValueError" will be raised in this
   case.

   Be forewarned about how "init=False" fields work during a call to
   "replace()".  They are not copied from the source object, but
   rather are initialized in "__post_init__()", if they're initialized
   at all.  It is expected that "init=False" fields will be rarely and
   judiciously used.  If they are used, it might be wise to have
   alternate class constructors, or perhaps a custom "replace()" (or
   similarly named) method which handles instance copying.

dataclasses.is_dataclass(obj)

   Return "True" if its parameter is a dataclass (including subclasses
   of a dataclass) or an instance of one, otherwise return "False".

   Si se necesita conocer si una clase es una instancia de *dataclass*
   (y no una clase de datos en si misma), se debe agregar una
   verificación adicional para "not isinstance(obj, type)":

      def is_dataclass_instance(obj):
          return is_dataclass(obj) and not isinstance(obj, type)

dataclasses.MISSING

   Un valor centinela que significa que falta un default o
   default_factory.

dataclasses.KW_ONLY

   A sentinel value used as a type annotation.  Any fields after a
   pseudo-field with the type of "KW_ONLY" are marked as keyword-only
   fields.  Note that a pseudo-field of type "KW_ONLY" is otherwise
   completely ignored.  This includes the name of such a field.  By
   convention, a name of "_" is used for a "KW_ONLY" field.  Keyword-
   only fields signify "__init__()" parameters that must be specified
   as keywords when the class is instantiated.

   En este ejemplo, los campos "y" y "z" se marcarán como campos de
   solo palabras clave:

      @dataclass
      class Point:
          x: float
          _: KW_ONLY
          y: float
          z: float

      p = Point(0, y=1.5, z=2.0)

   In a single dataclass, it is an error to specify more than one
   field whose type is "KW_ONLY".

   Added in version 3.10.

exception dataclasses.FrozenInstanceError

   Raised when an implicitly defined "__setattr__()" or
   "__delattr__()" is called on a dataclass which was defined with
   "frozen=True". It is a subclass of "AttributeError".


Procesamiento posterior a la inicialización
===========================================

dataclasses.__post_init__()

   When defined on the class, it will be called by the generated
   "__init__()", normally as "self.__post_init__()". However, if any
   "InitVar" fields are defined, they will also be passed to
   "__post_init__()" in the order they were defined in the class.  If
   no "__init__()" method is generated, then "__post_init__()" will
   not automatically be called.

   Entre otros usos, esto permite inicializar valores de campo que
   dependen de uno o más campos. Por ejemplo:

      @dataclass
      class C:
          a: float
          b: float
          c: float = field(init=False)

          def __post_init__(self):
              self.c = self.a + self.b

The "__init__()" method generated by "@dataclass" does not call base
class "__init__()" methods. If the base class has an "__init__()"
method that has to be called, it is common to call this method in a
"__post_init__()" method:

   class Rectangle:
       def __init__(self, height, width):
         self.height = height
         self.width = width

   @dataclass
   class Square(Rectangle):
       side: float

       def __post_init__(self):
           super().__init__(self.side, self.side)

Note, however, that in general the dataclass-generated "__init__()"
methods don't need to be called, since the derived dataclass will take
care of initializing all fields of any base class that is a dataclass
itself.

See the section below on init-only variables for ways to pass
parameters to "__post_init__()".  Also see the warning about how
"replace()" handles "init=False" fields.


Variables de clase
==================

One of the few places where "@dataclass" actually inspects the type of
a field is to determine if a field is a class variable as defined in
**PEP 526**.  It does this by checking if the type of the field is
"typing.ClassVar".  If a field is a "ClassVar", it is excluded from
consideration as a field and is ignored by the dataclass mechanisms.
Such "ClassVar" pseudo-fields are not returned by the module-level
"fields()" function.


Variable de solo inicialización
===============================

Another place where "@dataclass" inspects a type annotation is to
determine if a field is an init-only variable.  It does this by seeing
if the type of a field is of type "dataclasses.InitVar".  If a field
is an "InitVar", it is considered a pseudo-field called an init-only
field.  As it is not a true field, it is not returned by the module-
level "fields()" function.  Init-only fields are added as parameters
to the generated "__init__()" method, and are passed to the optional
"__post_init__()" method.  They are not otherwise used by dataclasses.

Por ejemplo, supongamos que se va a inicializar un campo desde una
base de datos, de no proporcionarse un valor al crear la clase:

   @dataclass
   class C:
       i: int
       j: int | None = None
       database: InitVar[DatabaseType | None] = None

       def __post_init__(self, database):
           if self.j is None and database is not None:
               self.j = database.lookup('j')

   c = C(10, database=my_database)

In this case, "fields()" will return "Field" objects for "i" and "j",
but not for "database".


Instancias congeladas
=====================

It is not possible to create truly immutable Python objects.  However,
by passing "frozen=True" to the "@dataclass" decorator you can emulate
immutability.  In that case, dataclasses will add "__setattr__()" and
"__delattr__()" methods to the class.  These methods will raise a
"FrozenInstanceError" when invoked.

There is a tiny performance penalty when using "frozen=True":
"__init__()" cannot use simple assignment to initialize fields, and
must use "object.__setattr__()".


Herencia
========

When the dataclass is being created by the "@dataclass" decorator, it
looks through all of the class's base classes in reverse MRO (that is,
starting at "object") and, for each dataclass that it finds, adds the
fields from that base class to an ordered mapping of fields. After all
of the base class fields are added, it adds its own fields to the
ordered mapping.  All of the generated methods will use this combined,
calculated ordered mapping of fields.  Because the fields are in
insertion order, derived classes override base classes.  An example:

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

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

The final list of fields is, in order, "x", "y", "z".  The final type
of "x" is "int", as specified in class "C".

The generated "__init__()" method for "C" will look like:

   def __init__(self, x: int = 15, y: int = 0, z: int = 10):


Re-ordering of keyword-only parameters in "__init__()"
======================================================

After the parameters needed for "__init__()" are computed, any
keyword-only parameters are moved to come after all regular (non-
keyword-only) parameters.  This is a requirement of how keyword-only
parameters are implemented in Python: they must come after non-
keyword-only parameters.

In this example, "Base.y", "Base.w", and "D.t" are keyword-only
fields, and "Base.x" and "D.z" are regular fields:

   @dataclass
   class Base:
       x: Any = 15.0
       _: KW_ONLY
       y: int = 0
       w: int = 1

   @dataclass
   class D(Base):
       z: int = 10
       t: int = field(kw_only=True, default=0)

The generated "__init__()" method for "D" will look like:

   def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):

Tenga en cuenta que los parámetros se han reordenado a partir de cómo
aparecen en la lista de campos: los parámetros derivados de los campos
regulares son seguidos por los parámetros derivados de los campos de
solo palabras clave.

The relative ordering of keyword-only parameters is maintained in the
re-ordered "__init__()" parameter list.


Funciones fábrica por defecto
=============================

If a "field()" specifies a *default_factory*, it is called with zero
arguments when a default value for the field is needed.  For example,
to create a new instance of a list, use:

   mylist: list = field(default_factory=list)

If a field is excluded from "__init__()" (using "init=False") and the
field also specifies *default_factory*, then the default factory
function will always be called from the generated "__init__()"
function.  This happens because there is no other way to give the
field an initial value.


Valores por defecto mutables
============================

Python almacena los valores miembros por defecto en atributos de
clase. Considera este ejemplo, sin usar clases de datos:

   class C:
       x = []
       def add(self, element):
           self.x.append(element)

   o1 = C()
   o2 = C()
   o1.add(1)
   o2.add(2)
   assert o1.x == [1, 2]
   assert o1.x is o2.x

Note that the two instances of class "C" share the same class variable
"x", as expected.

Usando clases de datos, *si* este código fuera válido:

   @dataclass
   class D:
       x: list = []      # This code raises ValueError
       def add(self, element):
           self.x.append(element)

generaría un código similar a:

   class D:
       x = []
       def __init__(self, x=x):
           self.x = x
       def add(self, element):
           self.x.append(element)

   assert D().x is D().x

This has the same issue as the original example using class "C". That
is, two instances of class "D" that do not specify a value for "x"
when creating a class instance will share the same copy of "x".
Because dataclasses just use normal Python class creation they also
share this behavior.  There is no general way for Data Classes to
detect this condition.  Instead, the "@dataclass" decorator will raise
a "ValueError" if it detects an unhashable default parameter.  The
assumption is that if a value is unhashable, it is mutable.  This is a
partial solution, but it does protect against many common errors.

Usar las funciones de fábrica por defecto es una forma de crear nuevas
instancias de tipos mutables como valores por defecto para campos:

   @dataclass
   class D:
       x: list = field(default_factory=list)

   assert D().x is not D().x

Distinto en la versión 3.11: Instead of looking for and disallowing
objects of type "list", "dict", or "set", unhashable objects are now
not allowed as default values.  Unhashability is used to approximate
mutability.


Campos tipo descriptor
======================

Los campos a los que se asigna objetos descriptor como valor por
defecto tienen los siguientes comportamientos especiales:

* The value for the field passed to the dataclass's "__init__()"
  method is passed to the descriptor's "__set__()" method rather than
  overwriting the descriptor object.

* Similarly, when getting or setting the field, the descriptor's
  "__get__()" or "__set__()" method is called rather than returning or
  overwriting the descriptor object.

* To determine whether a field contains a default value, "@dataclass"
  will call the descriptor's "__get__()" method using its class access
  form: "descriptor.__get__(obj=None, type=cls)".  If the descriptor
  returns a value in this case, it will be used as the field's
  default. On the other hand, if the descriptor raises
  "AttributeError" in this situation, no default value will be
  provided for the field.

   class IntConversionDescriptor:
       def __init__(self, *, default):
           self._default = default

       def __set_name__(self, owner, name):
           self._name = "_" + name

       def __get__(self, obj, type):
           if obj is None:
               return self._default

           return getattr(obj, self._name, self._default)

       def __set__(self, obj, value):
           setattr(obj, self._name, int(value))

   @dataclass
   class InventoryItem:
       quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)

   i = InventoryItem()
   print(i.quantity_on_hand)   # 100
   i.quantity_on_hand = 2.5    # calls __set__ with 2.5
   print(i.quantity_on_hand)   # 2

Tenga en cuenta que si un campo está anotado con un tipo de
descriptor, pero no se le asigna un objeto descriptor como valor por
defecto, el campo actuará como un campo normal.
