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"""This module contains related classes and functions for validation."""
from __future__ import annotations as _annotations
import dataclasses
import sys
from functools import partialmethod
from types import FunctionType
from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, Union, cast, overload
from pydantic_core import PydanticUndefined, core_schema
from pydantic_core import core_schema as _core_schema
from typing_extensions import Self, TypeAlias
from ._internal import _decorators, _generics, _internal_dataclass
from .annotated_handlers import GetCoreSchemaHandler
from .errors import PydanticUserError
if sys.version_info < (3, 11):
from typing_extensions import Protocol
else:
from typing import Protocol
_inspect_validator = _decorators.inspect_validator
@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
class AfterValidator:
"""!!! abstract "Usage Documentation"
[field *after* validators](../concepts/validators.md#field-after-validator)
A metadata class that indicates that a validation should be applied **after** the inner validation logic.
Attributes:
func: The validator function.
Example:
```python
from typing import Annotated
from pydantic import AfterValidator, BaseModel, ValidationError
MyInt = Annotated[int, AfterValidator(lambda v: v + 1)]
class Model(BaseModel):
a: MyInt
print(Model(a=1).a)
#> 2
try:
Model(a='a')
except ValidationError as e:
print(e.json(indent=2))
'''
[
{
"type": "int_parsing",
"loc": [
"a"
],
"msg": "Input should be a valid integer, unable to parse string as an integer",
"input": "a",
"url": "https://errors.pydantic.dev/2/v/int_parsing"
}
]
'''
```
"""
func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
schema = handler(source_type)
info_arg = _inspect_validator(self.func, 'after')
if info_arg:
func = cast(core_schema.WithInfoValidatorFunction, self.func)
return core_schema.with_info_after_validator_function(func, schema=schema, field_name=handler.field_name)
else:
func = cast(core_schema.NoInfoValidatorFunction, self.func)
return core_schema.no_info_after_validator_function(func, schema=schema)
@classmethod
def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
return cls(func=decorator.func)
@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
class BeforeValidator:
"""!!! abstract "Usage Documentation"
[field *before* validators](../concepts/validators.md#field-before-validator)
A metadata class that indicates that a validation should be applied **before** the inner validation logic.
Attributes:
func: The validator function.
json_schema_input_type: The input type of the function. This is only used to generate the appropriate
JSON Schema (in validation mode).
Example:
```python
from typing import Annotated
from pydantic import BaseModel, BeforeValidator
MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)]
class Model(BaseModel):
a: MyInt
print(Model(a=1).a)
#> 2
try:
Model(a='a')
except TypeError as e:
print(e)
#> can only concatenate str (not "int") to str
```
"""
func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
json_schema_input_type: Any = PydanticUndefined
def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
schema = handler(source_type)
input_schema = (
None
if self.json_schema_input_type is PydanticUndefined
else handler.generate_schema(self.json_schema_input_type)
)
info_arg = _inspect_validator(self.func, 'before')
if info_arg:
func = cast(core_schema.WithInfoValidatorFunction, self.func)
return core_schema.with_info_before_validator_function(
func,
schema=schema,
field_name=handler.field_name,
json_schema_input_schema=input_schema,
)
else:
func = cast(core_schema.NoInfoValidatorFunction, self.func)
return core_schema.no_info_before_validator_function(
func, schema=schema, json_schema_input_schema=input_schema
)
@classmethod
def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
return cls(
func=decorator.func,
json_schema_input_type=decorator.info.json_schema_input_type,
)
@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
class PlainValidator:
"""!!! abstract "Usage Documentation"
[field *plain* validators](../concepts/validators.md#field-plain-validator)
A metadata class that indicates that a validation should be applied **instead** of the inner validation logic.
!!! note
Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`.
You can now use the `json_schema_input_type` argument to specify the input type of the function
to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details.
Attributes:
func: The validator function.
json_schema_input_type: The input type of the function. This is only used to generate the appropriate
JSON Schema (in validation mode). If not provided, will default to `Any`.
Example:
```python
from typing import Annotated, Union
from pydantic import BaseModel, PlainValidator
MyInt = Annotated[
int,
PlainValidator(
lambda v: int(v) + 1, json_schema_input_type=Union[str, int] # (1)!
),
]
class Model(BaseModel):
a: MyInt
print(Model(a='1').a)
#> 2
print(Model(a=1).a)
#> 2
```
1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema
generator that in validation mode, the input type for the `a` field can be either a `str` or an `int`.
"""
func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
json_schema_input_type: Any = Any
def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
# Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the
# source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper
# serialization schema. To work around this for use cases that will not involve serialization, we simply
# catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema
# and abort any attempts to handle special serialization.
from pydantic import PydanticSchemaGenerationError
try:
schema = handler(source_type)
# TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence',
# schema validation will fail. That's why we use 'type ignore' comments below.
serialization = schema.get(
'serialization',
core_schema.wrap_serializer_function_ser_schema(
function=lambda v, h: h(v),
schema=schema,
return_schema=handler.generate_schema(source_type),
),
)
except PydanticSchemaGenerationError:
serialization = None
input_schema = handler.generate_schema(self.json_schema_input_type)
info_arg = _inspect_validator(self.func, 'plain')
if info_arg:
func = cast(core_schema.WithInfoValidatorFunction, self.func)
return core_schema.with_info_plain_validator_function(
func,
field_name=handler.field_name,
serialization=serialization, # pyright: ignore[reportArgumentType]
json_schema_input_schema=input_schema,
)
else:
func = cast(core_schema.NoInfoValidatorFunction, self.func)
return core_schema.no_info_plain_validator_function(
func,
serialization=serialization, # pyright: ignore[reportArgumentType]
json_schema_input_schema=input_schema,
)
@classmethod
def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
return cls(
func=decorator.func,
json_schema_input_type=decorator.info.json_schema_input_type,
)
@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
class WrapValidator:
"""!!! abstract "Usage Documentation"
[field *wrap* validators](../concepts/validators.md#field-wrap-validator)
A metadata class that indicates that a validation should be applied **around** the inner validation logic.
Attributes:
func: The validator function.
json_schema_input_type: The input type of the function. This is only used to generate the appropriate
JSON Schema (in validation mode).
```python
from datetime import datetime
from typing import Annotated
from pydantic import BaseModel, ValidationError, WrapValidator
def validate_timestamp(v, handler):
if v == 'now':
# we don't want to bother with further validation, just return the new value
return datetime.now()
try:
return handler(v)
except ValidationError:
# validation failed, in this case we want to return a default value
return datetime(2000, 1, 1)
MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)]
class Model(BaseModel):
a: MyTimestamp
print(Model(a='now').a)
#> 2032-01-02 03:04:05.000006
print(Model(a='invalid').a)
#> 2000-01-01 00:00:00
```
"""
func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction
json_schema_input_type: Any = PydanticUndefined
def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
schema = handler(source_type)
input_schema = (
None
if self.json_schema_input_type is PydanticUndefined
else handler.generate_schema(self.json_schema_input_type)
)
info_arg = _inspect_validator(self.func, 'wrap')
if info_arg:
func = cast(core_schema.WithInfoWrapValidatorFunction, self.func)
return core_schema.with_info_wrap_validator_function(
func,
schema=schema,
field_name=handler.field_name,
json_schema_input_schema=input_schema,
)
else:
func = cast(core_schema.NoInfoWrapValidatorFunction, self.func)
return core_schema.no_info_wrap_validator_function(
func,
schema=schema,
json_schema_input_schema=input_schema,
)
@classmethod
def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
return cls(
func=decorator.func,
json_schema_input_type=decorator.info.json_schema_input_type,
)
if TYPE_CHECKING:
class _OnlyValueValidatorClsMethod(Protocol):
def __call__(self, cls: Any, value: Any, /) -> Any: ...
class _V2ValidatorClsMethod(Protocol):
def __call__(self, cls: Any, value: Any, info: _core_schema.ValidationInfo, /) -> Any: ...
class _OnlyValueWrapValidatorClsMethod(Protocol):
def __call__(self, cls: Any, value: Any, handler: _core_schema.ValidatorFunctionWrapHandler, /) -> Any: ...
class _V2WrapValidatorClsMethod(Protocol):
def __call__(
self,
cls: Any,
value: Any,
handler: _core_schema.ValidatorFunctionWrapHandler,
info: _core_schema.ValidationInfo,
/,
) -> Any: ...
_V2Validator = Union[
_V2ValidatorClsMethod,
_core_schema.WithInfoValidatorFunction,
_OnlyValueValidatorClsMethod,
_core_schema.NoInfoValidatorFunction,
]
_V2WrapValidator = Union[
_V2WrapValidatorClsMethod,
_core_schema.WithInfoWrapValidatorFunction,
_OnlyValueWrapValidatorClsMethod,
_core_schema.NoInfoWrapValidatorFunction,
]
_PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
_V2BeforeAfterOrPlainValidatorType = TypeVar(
'_V2BeforeAfterOrPlainValidatorType',
bound=Union[_V2Validator, _PartialClsOrStaticMethod],
)
_V2WrapValidatorType = TypeVar('_V2WrapValidatorType', bound=Union[_V2WrapValidator, _PartialClsOrStaticMethod])
FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain']
@overload
def field_validator(
field: str,
/,
*fields: str,
mode: Literal['wrap'],
check_fields: bool | None = ...,
json_schema_input_type: Any = ...,
) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ...
@overload
def field_validator(
field: str,
/,
*fields: str,
mode: Literal['before', 'plain'],
check_fields: bool | None = ...,
json_schema_input_type: Any = ...,
) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
@overload
def field_validator(
field: str,
/,
*fields: str,
mode: Literal['after'] = ...,
check_fields: bool | None = ...,
) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
def field_validator(
field: str,
/,
*fields: str,
mode: FieldValidatorModes = 'after',
check_fields: bool | None = None,
json_schema_input_type: Any = PydanticUndefined,
) -> Callable[[Any], Any]:
"""!!! abstract "Usage Documentation"
[field validators](../concepts/validators.md#field-validators)
Decorate methods on the class indicating that they should be used to validate fields.
Example usage:
```python
from typing import Any
from pydantic import (
BaseModel,
ValidationError,
field_validator,
)
class Model(BaseModel):
a: str
@field_validator('a')
@classmethod
def ensure_foobar(cls, v: Any):
if 'foobar' not in v:
raise ValueError('"foobar" not found in a')
return v
print(repr(Model(a='this is foobar good')))
#> Model(a='this is foobar good')
try:
Model(a='snap')
except ValidationError as exc_info:
print(exc_info)
'''
1 validation error for Model
a
Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
'''
```
For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators).
Args:
field: The first field the `field_validator` should be called on; this is separate
from `fields` to ensure an error is raised if you don't pass at least one.
*fields: Additional field(s) the `field_validator` should be called on.
mode: Specifies whether to validate the fields before or after validation.
check_fields: Whether to check that the fields actually exist on the model.
json_schema_input_type: The input type of the function. This is only used to generate
the appropriate JSON Schema (in validation mode) and can only specified
when `mode` is either `'before'`, `'plain'` or `'wrap'`.
Returns:
A decorator that can be used to decorate a function to be used as a field_validator.
Raises:
PydanticUserError:
- If `@field_validator` is used bare (with no fields).
- If the args passed to `@field_validator` as fields are not strings.
- If `@field_validator` applied to instance methods.
"""
if isinstance(field, FunctionType):
raise PydanticUserError(
'`@field_validator` should be used with fields and keyword arguments, not bare. '
"E.g. usage should be `@validator('<field_name>', ...)`",
code='validator-no-fields',
)
if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined:
raise PydanticUserError(
f"`json_schema_input_type` can't be used when mode is set to {mode!r}",
code='validator-input-type',
)
if json_schema_input_type is PydanticUndefined and mode == 'plain':
json_schema_input_type = Any
fields = field, *fields
if not all(isinstance(field, str) for field in fields):
raise PydanticUserError(
'`@field_validator` fields should be passed as separate string args. '
"E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
code='validator-invalid-fields',
)
def dec(
f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any],
) -> _decorators.PydanticDescriptorProxy[Any]:
if _decorators.is_instance_method_from_sig(f):
raise PydanticUserError(
'`@field_validator` cannot be applied to instance methods', code='validator-instance-method'
)
# auto apply the @classmethod decorator
f = _decorators.ensure_classmethod_based_on_signature(f)
dec_info = _decorators.FieldValidatorDecoratorInfo(
fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type
)
return _decorators.PydanticDescriptorProxy(f, dec_info)
return dec
_ModelType = TypeVar('_ModelType')
_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True)
class ModelWrapValidatorHandler(_core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]):
"""`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`."""
def __call__( # noqa: D102
self,
value: Any,
outer_location: str | int | None = None,
/,
) -> _ModelTypeCo: # pragma: no cover
...
class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]):
"""A `@model_validator` decorated function signature.
This is used when `mode='wrap'` and the function does not have info argument.
"""
def __call__( # noqa: D102
self,
cls: type[_ModelType],
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
handler: ModelWrapValidatorHandler[_ModelType],
/,
) -> _ModelType: ...
class ModelWrapValidator(Protocol[_ModelType]):
"""A `@model_validator` decorated function signature. This is used when `mode='wrap'`."""
def __call__( # noqa: D102
self,
cls: type[_ModelType],
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
handler: ModelWrapValidatorHandler[_ModelType],
info: _core_schema.ValidationInfo,
/,
) -> _ModelType: ...
class FreeModelBeforeValidatorWithoutInfo(Protocol):
"""A `@model_validator` decorated function signature.
This is used when `mode='before'` and the function does not have info argument.
"""
def __call__( # noqa: D102
self,
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
/,
) -> Any: ...
class ModelBeforeValidatorWithoutInfo(Protocol):
"""A `@model_validator` decorated function signature.
This is used when `mode='before'` and the function does not have info argument.
"""
def __call__( # noqa: D102
self,
cls: Any,
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
/,
) -> Any: ...
class FreeModelBeforeValidator(Protocol):
"""A `@model_validator` decorated function signature. This is used when `mode='before'`."""
def __call__( # noqa: D102
self,
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
info: _core_schema.ValidationInfo,
/,
) -> Any: ...
class ModelBeforeValidator(Protocol):
"""A `@model_validator` decorated function signature. This is used when `mode='before'`."""
def __call__( # noqa: D102
self,
cls: Any,
# this can be a dict, a model instance
# or anything else that gets passed to validate_python
# thus validators _must_ handle all cases
value: Any,
info: _core_schema.ValidationInfo,
/,
) -> Any: ...
ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType]
"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not
have info argument.
"""
ModelAfterValidator = Callable[[_ModelType, _core_schema.ValidationInfo], _ModelType]
"""A `@model_validator` decorated function signature. This is used when `mode='after'`."""
_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]]
_AnyModelBeforeValidator = Union[
FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo
]
_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]]
@overload
def model_validator(
*,
mode: Literal['wrap'],
) -> Callable[
[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
]: ...
@overload
def model_validator(
*,
mode: Literal['before'],
) -> Callable[
[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
]: ...
@overload
def model_validator(
*,
mode: Literal['after'],
) -> Callable[
[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
]: ...
def model_validator(
*,
mode: Literal['wrap', 'before', 'after'],
) -> Any:
"""!!! abstract "Usage Documentation"
[Model Validators](../concepts/validators.md#model-validators)
Decorate model methods for validation purposes.
Example usage:
```python
from typing_extensions import Self
from pydantic import BaseModel, ValidationError, model_validator
class Square(BaseModel):
width: float
height: float
@model_validator(mode='after')
def verify_square(self) -> Self:
if self.width != self.height:
raise ValueError('width and height do not match')
return self
s = Square(width=1, height=1)
print(repr(s))
#> Square(width=1.0, height=1.0)
try:
Square(width=1, height=2)
except ValidationError as e:
print(e)
'''
1 validation error for Square
Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
'''
```
For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators).
Args:
mode: A required string literal that specifies the validation mode.
It can be one of the following: 'wrap', 'before', or 'after'.
Returns:
A decorator that can be used to decorate a function to be used as a model validator.
"""
def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
# auto apply the @classmethod decorator
f = _decorators.ensure_classmethod_based_on_signature(f)
dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode)
return _decorators.PydanticDescriptorProxy(f, dec_info)
return dec
AnyType = TypeVar('AnyType')
if TYPE_CHECKING:
# If we add configurable attributes to IsInstance, we'd probably need to stop hiding it from type checkers like this
InstanceOf = Annotated[AnyType, ...] # `IsInstance[Sequence]` will be recognized by type checkers as `Sequence`
else:
@dataclasses.dataclass(**_internal_dataclass.slots_true)
class InstanceOf:
'''Generic type for annotating a type that is an instance of a given class.
Example:
```python
from pydantic import BaseModel, InstanceOf
class Foo:
...
class Bar(BaseModel):
foo: InstanceOf[Foo]
Bar(foo=Foo())
try:
Bar(foo=42)
except ValidationError as e:
print(e)
"""
[
│ {
│ │ 'type': 'is_instance_of',
│ │ 'loc': ('foo',),
│ │ 'msg': 'Input should be an instance of Foo',
│ │ 'input': 42,
│ │ 'ctx': {'class': 'Foo'},
│ │ 'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of'
│ }
]
"""
```
'''
@classmethod
def __class_getitem__(cls, item: AnyType) -> AnyType:
return Annotated[item, cls()]
@classmethod
def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
from pydantic import PydanticSchemaGenerationError
# use the generic _origin_ as the second argument to isinstance when appropriate
instance_of_schema = core_schema.is_instance_schema(_generics.get_origin(source) or source)
try:
# Try to generate the "standard" schema, which will be used when loading from JSON
original_schema = handler(source)
except PydanticSchemaGenerationError:
# If that fails, just produce a schema that can validate from python
return instance_of_schema
else:
# Use the "original" approach to serialization
instance_of_schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
function=lambda v, h: h(v), schema=original_schema
)
return core_schema.json_or_python_schema(python_schema=instance_of_schema, json_schema=original_schema)
__hash__ = object.__hash__
if TYPE_CHECKING:
SkipValidation = Annotated[AnyType, ...] # SkipValidation[list[str]] will be treated by type checkers as list[str]
else:
@dataclasses.dataclass(**_internal_dataclass.slots_true)
class SkipValidation:
"""If this is applied as an annotation (e.g., via `x: Annotated[int, SkipValidation]`), validation will be
skipped. You can also use `SkipValidation[int]` as a shorthand for `Annotated[int, SkipValidation]`.
This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes,
and know that it is safe to skip validation for one or more of the fields.
Because this converts the validation schema to `any_schema`, subsequent annotation-applied transformations
may not have the expected effects. Therefore, when used, this annotation should generally be the final
annotation applied to a type.
"""
def __class_getitem__(cls, item: Any) -> Any:
return Annotated[item, SkipValidation()]
@classmethod
def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
original_schema = handler(source)
metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]}
return core_schema.any_schema(
metadata=metadata,
serialization=core_schema.wrap_serializer_function_ser_schema(
function=lambda v, h: h(v), schema=original_schema
),
)
__hash__ = object.__hash__