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

**Source code:** Lib/dataclasses.py

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

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

The member variables to use in these generated methods are defined
using **PEP 526** type annotations.  For example, this code:

   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.

New in version 3.7.


Module contents
===============

@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.

   The order of the fields in all of the generated methods is the
   order in which they appear in the class definition.

   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
     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.

      New 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.  There is no effect
     on any other aspect of dataclasses.  See the *parameter* glossary
     entry for details.  Also see the "KW_ONLY" section.

      New 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.

      New in version 3.10.

      Changed in version 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".

      New in version 3.11.

   "field"s may optionally specify a default value, using normal
   Python syntax:

      @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):

   "TypeError" will be raised if a field without a default value
   follows a field with a default value.  This is true whether this
   occurs in a single class, or as a result of class inheritance.

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]

   As shown above, the "MISSING" value is a sentinel object used to
   detect if some parameters are provided by the user. This sentinel
   is used because "None" is a valid value for some parameters with a
   distinct meaning.  No code should directly use the "MISSING" value.

   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 "None" (the
     default), use the value of "compare": this would normally be the
     expected behavior.  A field should be considered in the hash if
     it's used for comparisons.  Setting this value to anything other
     than "None" is discouraged.

     One possible reason to set "hash=False" but "compare=True" would
     be if a field is expensive to compute a hash value for, that
     field is needed for equality testing, and there are other fields
     that contribute to the type's hash value.  Even if a field is
     excluded from the hash, it will still be used for comparisons.

   * "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.

      New 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 no "default" is 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.

   Other attributes may exist, but they are private and must not be
   inspected or relied on.

dataclasses.fields(class_or_instance)

   Returns a tuple of "Field" objects that define the fields for this
   dataclass.  Accepts either a dataclass, or an instance of a
   dataclass. Raises "TypeError" if not passed a dataclass or instance
   of one. Does not return pseudo-fields which are "ClassVar" or
   "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:

      dict((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()".

   Continuing from the previous example:

      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)

   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".

   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})

   Is equivalent to:

      @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 values in "changes" do not specify fields, 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 or an instance of
   one, otherwise return "False".

   If you need to know if a class is an instance of a dataclass (and
   not a dataclass itself), then add a further check for "not
   isinstance(obj, type)":

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

dataclasses.MISSING

   A sentinel value signifying a missing default or 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.

   In this example, the fields "y" and "z" will be marked as keyword-
   only fields:

      @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".

   New 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".


Post-init processing
====================

The generated "__init__()" code will call a method named
"__post_init__()", if "__post_init__()" is defined on the class.  It
will normally be called 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.

   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.

      @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.


Class variables
===============

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.


Init-only variables
===================

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.

For example, suppose a field will be initialized from a database, if a
value is not provided when creating the class:

   @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".


Frozen instances
================

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 "__setattr__()".


Inheritance
===========

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):

Note that the parameters have been re-ordered from how they appear in
the list of fields: parameters derived from regular fields are
followed by parameters derived from keyword-only fields.

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


Default factory functions
=========================

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.


Mutable default values
======================

Python stores default member variable values in class attributes.
Consider this example, not using dataclasses:

   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.

Using dataclasses, *if* this code was valid:

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

it would generate code similar to:

   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.

Using default factory functions is a way to create new instances of
mutable types as default values for fields:

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

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

Changed in version 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.


Descriptor-typed fields
=======================

Fields that are assigned descriptor objects as their default value
have the following special behaviors:

* 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

Note that if a field is annotated with a descriptor type, but is not
assigned a descriptor object as its default value, the field will act
like a normal field.
