kittycad.models.unit_pressure_conversion.UnitPressureConversion

class kittycad.models.unit_pressure_conversion.UnitPressureConversion(**data)[source][source]

Bases: BaseModel

Result of converting between units.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`model_computed_fields`	A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`	Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`completed_at`
`created_at`
`error`
`id`
`input`
`input_unit`
`output`
`output_unit`
`started_at`
`status`
`updated_at`
`user_id`

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'completed_at': typing.Optional[datetime.datetime], 'created_at': <class 'datetime.datetime'>, 'error': typing.Optional[str], 'id': <class 'kittycad.models.uuid.Uuid'>, 'input': <class 'float'>, 'input_unit': <enum 'UnitPressure'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'output': typing.Optional[float], 'output_unit': <enum 'UnitPressure'>, 'started_at': typing.Optional[datetime.datetime], 'status': <enum 'ApiCallStatus'>, 'updated_at': <class 'datetime.datetime'>, 'user_id': <class 'kittycad.models.uuid.Uuid'>}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Dict[str, Any]

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.unit_pressure_conversion'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <enum 'UnitPressure'>, 'members': [UnitPressure.ATMOSPHERES, UnitPressure.BARS, UnitPressure.HECTOPASCALS, UnitPressure.KILOPASCALS, UnitPressure.MILLIBARS, UnitPressure.PASCALS, UnitPressure.PSI], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_pressure.UnitPressure:94734235614768', 'sub_type': 'str', 'type': 'enum'}], 'schema': {'cls': <class 'kittycad.models.unit_pressure_conversion.UnitPressureConversion'>, 'config': {'title': 'UnitPressureConversion'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.unit_pressure_conversion.UnitPressureConversion'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.unit_pressure_conversion.UnitPressureConversion'>>]}, 'ref': 'kittycad.models.unit_pressure_conversion.UnitPressureConversion:94734218503360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'completed_at': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'created_at': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'model-field'}, 'error': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.uuid.Uuid'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'input': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'input_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_pressure.UnitPressure:94734235614768', 'type': 'definition-ref'}, 'type': 'model-field'}, 'output': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.unit_pressure.UnitPressure:94734235614768', 'type': 'definition-ref'}, 'type': 'model-field'}, 'started_at': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'status': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ApiCallStatus'>, 'members': [ApiCallStatus.QUEUED, ApiCallStatus.UPLOADED, ApiCallStatus.IN_PROGRESS, ApiCallStatus.COMPLETED, ApiCallStatus.FAILED], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.api_call_status.ApiCallStatus:94734216892080', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'updated_at': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'microseconds_precision': 'truncate', 'type': 'datetime'}, 'type': 'model-field'}, 'user_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.uuid.Uuid'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'UnitPressureConversion', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005629077124c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "input": SerField { key_py: Py( 0x00007f83d701f410, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f83d1b39870, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "input_unit": SerField { key_py: Py( 0x00007f83d1bce530, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "completed_at": SerField { key_py: Py( 0x00007f83d26bc330, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f83d6f2d0e0, ), ), serializer: Nullable( NullableSerializer { serializer: Datetime( DatetimeSerializer, ), }, ), }, ), ), required: true, }, "started_at": SerField { key_py: Py( 0x00007f83d26a1830, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f83d6f2d0e0, ), ), serializer: Nullable( NullableSerializer { serializer: Datetime( DatetimeSerializer, ), }, ), }, ), ), required: true, }, "user_id": SerField { key_py: Py( 0x00007f83d329c630, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "status": SerField { key_py: Py( 0x00007f83d7021d30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x0000562907588eb0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "created_at": SerField { key_py: Py( 0x00007f83d26bd4f0, ), alias: None, alias_py: None, serializer: Some( Datetime( DatetimeSerializer, ), ), required: true, }, "output": SerField { key_py: Py( 0x00007f83d6fb5c40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f83d6f2d0e0, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "error": SerField { key_py: Py( 0x00007f83d6f7afe0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f83d6f2d0e0, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "id": SerField { key_py: Py( 0x00007f83d701f0d0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007f83d322abb0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: false, }, ), ), required: true, }, "updated_at": SerField { key_py: Py( 0x00007f83d26a1770, ), alias: None, alias_py: None, serializer: Some( Datetime( DatetimeSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 12, }, ), has_extra: false, root_model: false, name: "UnitPressureConversion", }, ), definitions=[Enum(EnumSerializer { class: Py(0x562908763e30), serializer: Some(Str(StrSerializer)) })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="UnitPressureConversion", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "completed_at", lookup_key: Simple { key: "completed_at", py_key: Py( 0x00007f83d1afc830, ), path: LookupPath( [ S( "completed_at", Py( 0x00007f83d1aff530, ), ), ], ), }, name_py: Py( 0x00007f83d26bc330, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f83d6f2d0e0, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Datetime( DateTimeValidator { strict: false, constraints: None, microseconds_precision: Truncate, }, ), name: "nullable[datetime]", }, ), validate_default: false, copy_default: false, name: "default[nullable[datetime]]", undefined: Py( 0x00007f83d4e0a330, ), }, ), frozen: false, }, Field { name: "created_at", lookup_key: Simple { key: "created_at", py_key: Py( 0x00007f83d1aff4f0, ), path: LookupPath( [ S( "created_at", Py( 0x00007f83d1aff5b0, ), ), ], ), }, name_py: Py( 0x00007f83d26bd4f0, ), validator: Datetime( DateTimeValidator { strict: false, constraints: None, microseconds_precision: Truncate, }, ), frozen: false, }, Field { name: "error", lookup_key: Simple { key: "error", py_key: Py( 0x00007f83d1acdef0, ), path: LookupPath( [ S( "error", Py( 0x00007f83d1acdf20, ), ), ], ), }, name_py: Py( 0x00007f83d6f7afe0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f83d6f2d0e0, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f83d4e0a330, ), }, ), frozen: false, }, Field { name: "id", lookup_key: Simple { key: "id", py_key: Py( 0x00007f83d1acdf80, ), path: LookupPath( [ S( "id", Py( 0x00007f83d1acdf50, ), ), ], ), }, name_py: Py( 0x00007f83d701f0d0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x00005629075848a0, ), config: Py( 0x00007f83d1aff380, ), name: "function-after[Uuid(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "input", lookup_key: Simple { key: "input", py_key: Py( 0x00007f83d1acdfb0, ), path: LookupPath( [ S( "input", Py( 0x00007f83d1acdfe0, ), ), ], ), }, name_py: Py( 0x00007f83d701f410, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f83d1b39870, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", undefined: Py( 0x00007f83d4e0a330, ), }, ), frozen: false, }, Field { name: "input_unit", lookup_key: Simple { key: "input_unit", py_key: Py( 0x00007f83d1aff570, ), path: LookupPath( [ S( "input_unit", Py( 0x00007f83d1aff5f0, ), ), ], ), }, name_py: Py( 0x00007f83d1bce530, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "output", lookup_key: Simple { key: "output", py_key: Py( 0x00007f83d1ace040, ), path: LookupPath( [ S( "output", Py( 0x00007f83d1ace070, ), ), ], ), }, name_py: Py( 0x00007f83d6fb5c40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f83d6f2d0e0, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f83d4e0a330, ), }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f83d1aff630, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f83d1aff670, ), ), ], ), }, name_py: Py( 0x00007f83d322abb0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "started_at", lookup_key: Simple { key: "started_at", py_key: Py( 0x00007f83d1aff6b0, ), path: LookupPath( [ S( "started_at", Py( 0x00007f83d1aff6f0, ), ), ], ), }, name_py: Py( 0x00007f83d26a1830, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f83d6f2d0e0, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Datetime( DateTimeValidator { strict: false, constraints: None, microseconds_precision: Truncate, }, ), name: "nullable[datetime]", }, ), validate_default: false, copy_default: false, name: "default[nullable[datetime]]", undefined: Py( 0x00007f83d4e0a330, ), }, ), frozen: false, }, Field { name: "status", lookup_key: Simple { key: "status", py_key: Py( 0x00007f83d1ace0a0, ), path: LookupPath( [ S( "status", Py( 0x00007f83d1ace0d0, ), ), ], ), }, name_py: Py( 0x00007f83d7021d30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000562907588eb0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "completed": 3, "in_progress": 2, "failed": 4, "uploaded": 1, "queued": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f83d2952990, ), Py( 0x00007f83d29529f0, ), Py( 0x00007f83d2952ab0, ), Py( 0x00007f83d2952b10, ), Py( 0x00007f83d2952b70, ), ], }, missing: None, expected_repr: "'queued', 'uploaded', 'in_progress', 'completed' or 'failed'", strict: false, class_repr: "ApiCallStatus", name: "str-enum[ApiCallStatus]", }, ), frozen: false, }, Field { name: "updated_at", lookup_key: Simple { key: "updated_at", py_key: Py( 0x00007f83d1aff730, ), path: LookupPath( [ S( "updated_at", Py( 0x00007f83d1aff770, ), ), ], ), }, name_py: Py( 0x00007f83d26a1770, ), validator: Datetime( DateTimeValidator { strict: false, constraints: None, microseconds_precision: Truncate, }, ), frozen: false, }, Field { name: "user_id", lookup_key: Simple { key: "user_id", py_key: Py( 0x00007f83d1ace100, ), path: LookupPath( [ S( "user_id", Py( 0x00007f83d1ace130, ), ), ], ), }, name_py: Py( 0x00007f83d329c630, ), validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x00005629075848a0, ), config: Py( 0x00007f83d1aff380, ), name: "function-after[Uuid(), str]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "UnitPressureConversion", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005629077124c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f83d4e0a330, ), name: "UnitPressureConversion", }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x562908763e30), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"millibars": 4, "atmospheres": 0, "hectopascals": 2, "bars": 1, "psi": 6, "kilopascals": 3, "pascals": 5}), expected_py_dict: None, expected_py_values: None, values: [Py(0x7f83d1ba6210), Py(0x7f83d1ba6270), Py(0x7f83d1ba62d0), Py(0x7f83d1ba6330), Py(0x7f83d1ba6390), Py(0x7f83d1ba63f0), Py(0x7f83d1ba6450)] }, missing: None, expected_repr: "'atmospheres', 'bars', 'hectopascals', 'kilopascals', 'millibars', 'pascals' or 'psi'", strict: false, class_repr: "UnitPressure", name: "str-enum[UnitPressure]" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[str | None, Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, completed_at: Optional[datetime.datetime] = None, created_at: datetime.datetime, error: Optional[str] = None, id: kittycad.models.uuid.Uuid, input: float = 0.0, input_unit: kittycad.models.unit_pressure.UnitPressure, output: Optional[float] = None, output_unit: kittycad.models.unit_pressure.UnitPressure, started_at: Optional[datetime.datetime] = None, status: kittycad.models.api_call_status.ApiCallStatus, updated_at: datetime.datetime, user_id: kittycad.models.uuid.Uuid) -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

completed_at: Optional[datetime][source]

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

created_at: datetime[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

error: Optional[str][source]

classmethod from_orm(obj)[source]

Return type:: Self

id: Uuid[source]

input: float[source]

input_unit: UnitPressure[source]

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], None]) – A set of fields to exclude from the output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Mapping[int, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], Mapping[str, Union[Set[int], Set[str], Mapping[int, Union[IncEx, Literal[True]]], Mapping[str, Union[IncEx, Literal[True]]], Literal[True]]], None]) – Field(s) to exclude from the JSON output.
context (Any | None) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'completed_at': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'created_at': FieldInfo(annotation=datetime, required=True), 'error': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'id': FieldInfo(annotation=Uuid, required=True), 'input': FieldInfo(annotation=float, required=False, default=0.0), 'input_unit': FieldInfo(annotation=UnitPressure, required=True), 'output': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'output_unit': FieldInfo(annotation=UnitPressure, required=True), 'started_at': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'status': FieldInfo(annotation=ApiCallStatus, required=True), 'updated_at': FieldInfo(annotation=datetime, required=True), 'user_id': FieldInfo(annotation=Uuid, required=True)}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None) – Whether to enforce types strictly.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (Any | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (Any | None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

output: Optional[float][source]

output_unit: UnitPressure[source]

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

started_at: Optional[datetime][source]

status: ApiCallStatus[source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

updated_at: datetime[source]

user_id: Uuid[source]

classmethod validate(value)[source]

Return type:: Self