kittycad.models.web_socket_request

Classes

OptionDebug(**data)	Return information about the connected instance
OptionHeaders(**data)	Authentication header request.
OptionMetricsResponse(**data)	The response to a metrics collection request from the server.
OptionModelingCmdBatchReq(**data)	A sequence of modeling requests.
OptionModelingCmdReq(**data)	The modeling command request.
OptionPing(**data)	The client-to-server Ping to ensure the WebSocket stays alive.
OptionSdpOffer(**data)	The SDP offer request.
OptionTrickleIce(**data)	The trickle ICE candidate request.

class kittycad.models.web_socket_request.OptionDebug(**data)

Return information about the connected instance

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['debug']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 78

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionDebug'>, 'config': {'title': 'OptionDebug'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionDebug'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionDebug:94897792878528', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {}, 'schema': {'default': 'debug', 'schema': {'expected': ['debug'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDebug', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['debug'], required=False, default='debug')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d3c07c0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f755a7a2010,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "debug",                                             },                                             expected_py: None,                                             name: "literal['debug']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 1,             },         ),         has_extra: false,         root_model: false,         name: "OptionDebug",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDebug", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa1c50,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f755a7a2010,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "debug": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556057480,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f755a7a2010,                                                 ),                                             ],                                         },                                         expected_repr: "'debug'",                                         name: "literal['debug']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['debug']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionDebug",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d3c07c0,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionDebug",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, type: Literal['debug'] = 'debug') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'type': FieldInfo(annotation=Literal['debug'], required=False, default='debug')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['debug']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionHeaders(**data)

Authentication header request.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'headers': typing.Dict[str, str], 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['headers']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 86

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionHeaders'>, 'config': {'title': 'OptionHeaders'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionHeaders'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionHeaders:94897794121728', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'headers': {'metadata': {}, 'schema': {'keys_schema': {'type': 'str'}, 'type': 'dict', 'values_schema': {'type': 'str'}}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'headers', 'schema': {'expected': ['headers'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHeaders', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'headers': FieldInfo(annotation=Dict[str, str], required=True), 'type': FieldInfo(annotation=Literal['headers'], required=False, default='headers')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d4f0000,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f755b10e2b8,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "headers",                                             },                                             expected_py: None,                                             name: "literal['headers']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "headers": SerField {                         key_py: Py(                             0x00007f755b10e2b8,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Dict(                                 DictSerializer {                                     key_serializer: Str(                                         StrSerializer,                                     ),                                     value_serializer: Str(                                         StrSerializer,                                     ),                                     filter: SchemaFilter {                                         include: None,                                         exclude: None,                                     },                                     name: "dict[str, str]",                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 2,             },         ),         has_extra: false,         root_model: false,         name: "OptionHeaders",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHeaders", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "headers",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "headers",                                         py_key: Py(                                             0x00007f7556fa1d40,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b10e2b8,                         ),                         validator: Dict(                             DictValidator {                                 strict: false,                                 key_validator: Str(                                     StrValidator {                                         strict: false,                                         coerce_numbers_to_str: false,                                     },                                 ),                                 value_validator: Str(                                     StrValidator {                                         strict: false,                                         coerce_numbers_to_str: false,                                     },                                 ),                                 min_length: None,                                 max_length: None,                                 name: "dict[str,str]",                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa1e30,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f755b10e2b8,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "headers": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556056ac0,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f755b10e2b8,                                                 ),                                             ],                                         },                                         expected_repr: "'headers'",                                         name: "literal['headers']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['headers']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionHeaders",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d4f0000,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionHeaders",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, headers: Dict[str, str], type: Literal['headers'] = 'headers') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

headers: Dict[str, str]

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'headers': FieldInfo(annotation=Dict[str, str], required=True), 'type': FieldInfo(annotation=Literal['headers'], required=False, default='headers')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['headers']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionMetricsResponse(**data)

The response to a metrics collection request from the server.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'metrics': <class 'kittycad.models.client_metrics.ClientMetrics'>, 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['metrics_response']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 68

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionMetricsResponse'>, 'config': {'title': 'OptionMetricsResponse'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionMetricsResponse'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionMetricsResponse:94897792884464', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'metrics': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.client_metrics.ClientMetrics'>, 'config': {'title': 'ClientMetrics'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.client_metrics.ClientMetrics'>>]}, 'ref': 'kittycad.models.client_metrics.ClientMetrics:94897789952560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'rtc_frame_height': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_frame_width': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_frames_decoded': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_frames_dropped': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_frames_per_second': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_frames_received': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_freeze_count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_jitter_sec': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_keyframes_decoded': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_packets_lost': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_pause_count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_pli_count': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_stun_rtt_sec': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_total_freezes_duration_sec': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'rtc_total_pauses_duration_sec': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ClientMetrics', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'metrics_response', 'schema': {'expected': ['metrics_response'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMetricsResponse', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'metrics': FieldInfo(annotation=ClientMetrics, required=True), 'type': FieldInfo(annotation=Literal['metrics_response'], required=False, default='metrics_response')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d3c1ef0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "metrics": SerField {                         key_py: Py(                             0x00007f7556fa26d0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Prebuilt(                                 PrebuiltSerializer {                                     schema_serializer: Py(                                         0x00007f7556712150,                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f755605c570,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "metrics_response",                                             },                                             expected_py: None,                                             name: "literal['metrics_response']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 2,             },         ),         has_extra: false,         root_model: false,         name: "OptionMetricsResponse",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMetricsResponse", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "metrics",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "metrics",                                         py_key: Py(                                             0x00007f7556fa1bc0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f7556fa26d0,                         ),                         validator: Prebuilt(                             PrebuiltValidator {                                 schema_validator: Py(                                     0x0000564f1cd8a6a0,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa1cb0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f755605c570,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "metrics_response": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556057e40,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f755605c570,                                                 ),                                             ],                                         },                                         expected_repr: "'metrics_response'",                                         name: "literal['metrics_response']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['metrics_response']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionMetricsResponse",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d3c1ef0,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionMetricsResponse",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, metrics: kittycad.models.client_metrics.ClientMetrics, type: Literal['metrics_response'] = 'metrics_response') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

metrics: ClientMetrics

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'metrics': FieldInfo(annotation=ClientMetrics, required=True), 'type': FieldInfo(annotation=Literal['metrics_response'], required=False, default='metrics_response')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['metrics_response']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionModelingCmdBatchReq(**data)

A sequence of modeling requests. If any request fails, following requests will not be tried.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'batch_id': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'model_config': 'ClassVar[ConfigDict]', 'requests': typing.List[kittycad.models.modeling_cmd_req.ModelingCmdReq], 'responses': <class 'bool'>, 'type': typing.Literal['modeling_cmd_batch_req']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 46

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94897783608256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94897782236432', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94897789531760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94897786141936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {}, 'schema': {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94897786844256', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'direction': {'metadata': {}, 'schema': {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94897784207408', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.transform.Transform'>, 'config': {'title': 'Transform'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.transform.Transform'>>]}, 'ref': 'kittycad.models.transform.Transform:94897785816496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'replicate': {'metadata': {}, 'schema': {'default': True, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'rotation': {'metadata': {}, 'schema': {'default': {'angle': {'unit': 'degrees', 'value': 0.0}, 'axis': {'x': 0.0, 'y': 0.0, 'z': 1.0}, 'origin': {'type': 'local'}}, 'schema': {'cls': <class 'kittycad.models.rotation.Rotation'>, 'config': {'title': 'Rotation'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.rotation.Rotation'>>]}, 'ref': 'kittycad.models.rotation.Rotation:94897785811024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94897783608256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94897782236432', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'axis': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'origin': {'metadata': {}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'generic_origin': <class 'pydantic.root_model.RootModel'>, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94897787678944[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140141937245008]", 'root_model': True, 'schema': {'choices': {'custom': {'cls': <class 'kittycad.models.origin_type.OptionCustom'>, 'config': {'title': 'OptionCustom'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionCustom'>>]}, 'ref': 'kittycad.models.origin_type.OptionCustom:94897785526736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'custom', 'schema': {'expected': ['custom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCustom', 'type': 'model-fields'}, 'type': 'model'}, 'global': {'cls': <class 'kittycad.models.origin_type.OptionGlobal'>, 'config': {'title': 'OptionGlobal'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionGlobal'>>]}, 'ref': 'kittycad.models.origin_type.OptionGlobal:94897781840752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {}, 'schema': {'default': 'global', 'schema': {'expected': ['global'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGlobal', 'type': 'model-fields'}, 'type': 'model'}, 'local': {'cls': <class 'kittycad.models.origin_type.OptionLocal'>, 'config': {'title': 'OptionLocal'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionLocal'>>]}, 'ref': 'kittycad.models.origin_type.OptionLocal:94897781837328', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {}, 'schema': {'default': 'local', 'schema': {'expected': ['local'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionLocal', 'type': 'model-fields'}, 'type': 'model'}}, 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'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'text': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, 'config': {'title': 'AnnotationTextOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>>]}, 'ref': 'kittycad.models.annotation_text_options.AnnotationTextOptions:94897781549536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'point_size': {'metadata': {}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'text': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'x': {'metadata': {}, 'schema': {'cls': <enum 'AnnotationTextAlignmentX'>, 'members': [AnnotationTextAlignmentX.LEFT, AnnotationTextAlignmentX.CENTER, AnnotationTextAlignmentX.RIGHT], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_x.AnnotationTextAlignmentX:94897785629456', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'cls': <enum 'AnnotationTextAlignmentY'>, 'members': [AnnotationTextAlignmentY.BOTTOM, AnnotationTextAlignmentY.CENTER, AnnotationTextAlignmentY.TOP], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_y.AnnotationTextAlignmentY:94897785621552', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AnnotationTextOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AnnotationOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'update_annotation', 'schema': {'expected': ['update_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionUpdateAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'view_isometric': {'cls': <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>, 'config': {'title': 'OptionViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionViewIsometric:94897788799248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'padding': {'metadata': {}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'view_isometric', 'schema': {'expected': ['view_isometric'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'volume': {'cls': <class 'kittycad.models.modeling_cmd.OptionVolume'>, 'config': {'title': 'OptionVolume'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionVolume'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionVolume:94897790184656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'output_unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitVolume'>, 'members': [UnitVolume.CM3, UnitVolume.FT3, UnitVolume.IN3, UnitVolume.M3, UnitVolume.YD3, UnitVolume.USFLOZ, UnitVolume.USGAL, UnitVolume.L, UnitVolume.ML], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_volume.UnitVolume:94897788685216', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'volume', 'schema': {'expected': ['volume'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionVolume', 'type': 'model-fields'}, 'type': 'model'}, 'zoom_to_fit': {'cls': <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>, 'config': {'title': 'OptionZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionZoomToFit:94897790282128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'animated': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'object_ids': {'metadata': {}, 'schema': {'default': [], 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'default'}, 'type': 'model-field'}, 'padding': {'metadata': {}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'zoom_to_fit', 'schema': {'expected': ['zoom_to_fit'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionZoomToFit', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'cmd_id': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'ModelingCmdReq', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}, 'responses': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'modeling_cmd_batch_req', 'schema': {'expected': ['modeling_cmd_batch_req'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionModelingCmdBatchReq', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'batch_id': FieldInfo(annotation=ModelingCmdId, required=True), 'requests': FieldInfo(annotation=List[ModelingCmdReq], required=True), 'responses': FieldInfo(annotation=bool, required=False, default=False), 'type': FieldInfo(annotation=Literal['modeling_cmd_batch_req'], required=False, default='modeling_cmd_batch_req')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d3dc4b0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "batch_id": SerField {                         key_py: Py(                             0x00007f75560581f0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "requests": SerField {                         key_py: Py(                             0x00007f7557d67930,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             List(                                 ListSerializer {                                     item_serializer: Prebuilt(                                         PrebuiltSerializer {                                             schema_serializer: Py(                                                 0x00007f75565e1740,                                             ),                                         },                                     ),                                     filter: SchemaFilter {                                         include: None,                                         exclude: None,                                     },                                     name: "list[ModelingCmdReq]",                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "responses": SerField {                         key_py: Py(                             0x00007f75582f4e70,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f755b0c49e0,                                         ),                                     ),                                     serializer: Bool(                                         BoolSerializer,                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f75560582f0,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "modeling_cmd_batch_req",                                             },                                             expected_py: None,                                             name: "literal['modeling_cmd_batch_req']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 4,             },         ),         has_extra: false,         root_model: false,         name: "OptionModelingCmdBatchReq",     }, ), definitions=[Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75565e1e70) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556712e40) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75567601b0) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556f1fb30) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75565e3120) }), Enum(EnumSerializer { class: Py(0x564f1c8f2600), serializer: Some(Str(StrSerializer)) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556761180) })])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionModelingCmdBatchReq", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "batch_id",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "batch_id",                                         py_key: Py(                                             0x00007f7556058f30,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f75560581f0,                         ),                         validator: FunctionAfter(                             FunctionAfterValidator {                                 validator: Str(                                     StrValidator {                                         strict: false,                                         coerce_numbers_to_str: false,                                     },                                 ),                                 func: Py(                                     0x0000564f1cd071d0,                                 ),                                 config: Py(                                     0x00007f7556059840,                                 ),                                 name: "function-after[ModelingCmdId(), str]",                                 field_name: None,                                 info_arg: false,                             },                         ),                         frozen: false,                     },                     Field {                         name: "requests",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "requests",                                         py_key: Py(                                             0x00007f7556058ef0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f7557d67930,                         ),                         validator: List(                             ListValidator {                                 strict: false,                                 item_validator: Some(                                     Prebuilt(                                         PrebuiltValidator {                                             schema_validator: Py(                                                 0x0000564f1d174fe0,                                             ),                                         },                                     ),                                 ),                                 min_length: None,                                 max_length: None,                                 name: OnceLock(                                     <uninit>,                                 ),                                 fail_fast: false,                             },                         ),                         frozen: false,                     },                     Field {                         name: "responses",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "responses",                                         py_key: Py(                                             0x00007f7556058eb0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f75582f4e70,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f755b0c49e0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Bool(                                     BoolValidator {                                         strict: false,                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[bool]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa1860,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f75560582f0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "modeling_cmd_batch_req": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556059040,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f75560582f0,                                                 ),                                             ],                                         },                                         expected_repr: "'modeling_cmd_batch_req'",                                         name: "literal['modeling_cmd_batch_req']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['modeling_cmd_batch_req']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionModelingCmdBatchReq",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d3dc4b0,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionModelingCmdBatchReq",     }, ), definitions=[Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cd02920) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cdb8500) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1c93d9d0) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1d08fe90) }), StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x564f1c8f2600), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"arrow": 1, "none": 0}), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some(Py(0x7f75560597c0)), values: [Py(0x7f7556749a30), Py(0x7f7556749d30)] }, missing: None, expected_repr: "'none' or 'arrow'", strict: false, class_repr: "AnnotationLineEnd", name: "str-enum[AnnotationLineEnd]" }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cc86ec0) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cda5a20) })], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, batch_id: kittycad.models.modeling_cmd_id.ModelingCmdId, requests: List[kittycad.models.modeling_cmd_req.ModelingCmdReq], responses: bool = False, type: Literal['modeling_cmd_batch_req'] = 'modeling_cmd_batch_req') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

batch_id: ModelingCmdId

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'batch_id': FieldInfo(annotation=ModelingCmdId, required=True), 'requests': FieldInfo(annotation=List[ModelingCmdReq], required=True), 'responses': FieldInfo(annotation=bool, required=False, default=False), 'type': FieldInfo(annotation=Literal['modeling_cmd_batch_req'], required=False, default='modeling_cmd_batch_req')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

requests: List[ModelingCmdReq]

responses: bool

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['modeling_cmd_batch_req']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionModelingCmdReq(**data)

The modeling command request.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'cmd': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionEngineUtilEvaluatePath, OptionStartPath, OptionMovePathPen, OptionExtendPath, OptionExtrude, OptionTwistExtrude, OptionSweep, OptionRevolve, OptionSolid3dShellFace, OptionRevolveAboutEdge, OptionLoft, OptionClosePath, OptionCameraDragStart, OptionCameraDragMove, OptionCameraDragEnd, OptionDefaultCameraGetSettings, OptionDefaultCameraGetView, OptionDefaultCameraSetView, OptionDefaultCameraLookAt, OptionDefaultCameraPerspectiveSettings, OptionDefaultCameraZoom, OptionExport2d, OptionExport3d, OptionExport, OptionEntityGetParentId, OptionEntityGetNumChildren, OptionEntityGetChildUuid, OptionEntityGetAllChildUuids, OptionEntityGetSketchPaths, OptionEntityGetDistance, OptionEntityClone, OptionEntityLinearPatternTransform, OptionEntityLinearPattern, OptionEntityCircularPattern, OptionEntityMakeHelix, OptionEntityMakeHelixFromParams, OptionEntityMakeHelixFromEdge, OptionEntityMirror, OptionEntityMirrorAcrossEdge, OptionSelectWithPoint, OptionSelectAdd, OptionSelectRemove, OptionSceneClearAll, OptionSelectReplace, OptionHighlightSetEntity, OptionHighlightSetEntities, OptionNewAnnotation, OptionUpdateAnnotation, OptionEdgeLinesVisible, OptionObjectVisible, OptionObjectBringToFront, OptionObjectSetMaterialParamsPbr, OptionGetEntityType, OptionSolid3dGetAllEdgeFaces, OptionSolid2dAddHole, OptionSolid3dGetAllOppositeEdges, OptionSolid3dGetOppositeEdge, OptionSolid3dGetNextAdjacentEdge, OptionSolid3dGetPrevAdjacentEdge, OptionSolid3dGetCommonEdge, OptionSolid3dFilletEdge, OptionFaceIsPlanar, OptionFaceGetPosition, OptionFaceGetCenter, OptionFaceGetGradient, OptionSendObject, OptionEntitySetOpacity, OptionEntityFade, OptionMakePlane, OptionPlaneSetColor, OptionSetTool, OptionMouseMove, OptionMouseClick, OptionSketchModeDisable, OptionGetSketchModePlane, OptionCurveSetConstraint, OptionEnableSketchMode, OptionEnableDryRun, OptionDisableDryRun, OptionSetBackgroundColor, OptionSetCurrentToolProperties, OptionSetDefaultSystemProperties, OptionCurveGetType, OptionCurveGetControlPoints, OptionProjectEntityToPlane, OptionProjectPointsToPlane, OptionTakeSnapshot, OptionMakeAxesGizmo, OptionPathGetInfo, OptionPathGetCurveUuidsForVertices, OptionPathGetCurveUuid, OptionPathGetVertexUuids, OptionPathGetSketchTargetUuid, OptionHandleMouseDragStart, OptionHandleMouseDragMove, OptionHandleMouseDragEnd, OptionRemoveSceneObjects, OptionPlaneIntersectAndProject, OptionCurveGetEndPoints, OptionReconfigureStream, OptionImportFiles, OptionSetSceneUnits, OptionMass, OptionDensity, OptionVolume, OptionCenterOfMass, OptionSurfaceArea, OptionDefaultCameraFocusOn, OptionSetSelectionType, OptionSetSelectionFilter, OptionDefaultCameraSetOrthographic, OptionDefaultCameraSetPerspective, OptionDefaultCameraCenterToSelection, OptionDefaultCameraCenterToScene, OptionZoomToFit, OptionOrientToFace, OptionViewIsometric, OptionSolid3dGetExtrusionFaceInfo, OptionSolid3dGetAdjacencyInfo, OptionSelectClear, OptionSelectGet, OptionGetNumObjects, OptionSetObjectTransform, OptionBooleanUnion, OptionBooleanIntersection, OptionBooleanSubtract, OptionMakeOffsetPath, OptionAddHoleFromOffset, OptionSetGridReferencePlane, OptionSetGridScale, OptionSetGridAutoScale], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'cmd_id': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['modeling_cmd_req']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 34

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94897783608256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94897782236432', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94897789531760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>, 'config': {'title': 'AxisDirectionPair'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.axis_direction_pair.AxisDirectionPair'>>]}, 'ref': 'kittycad.models.axis_direction_pair.AxisDirectionPair:94897786141936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'axis': {'metadata': {}, 'schema': {'cls': <enum 'Axis'>, 'members': [Axis.Y, Axis.Z], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.axis.Axis:94897786844256', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'direction': {'metadata': {}, 'schema': {'cls': <enum 'Direction'>, 'members': [Direction.POSITIVE, Direction.NEGATIVE], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.direction.Direction:94897784207408', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AxisDirectionPair', 'type': 'model-fields'}, 'type': 'model'}, {'cls': <class 'kittycad.models.transform.Transform'>, 'config': {'title': 'Transform'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.transform.Transform'>>]}, 'ref': 'kittycad.models.transform.Transform:94897785816496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'replicate': {'metadata': {}, 'schema': {'default': True, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'rotation': {'metadata': {}, 'schema': {'default': {'angle': {'unit': 'degrees', 'value': 0.0}, 'axis': {'x': 0.0, 'y': 0.0, 'z': 1.0}, 'origin': {'type': 'local'}}, 'schema': {'cls': <class 'kittycad.models.rotation.Rotation'>, 'config': {'title': 'Rotation'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.rotation.Rotation'>>]}, 'ref': 'kittycad.models.rotation.Rotation:94897785811024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'angle': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.angle.Angle'>, 'config': {'title': 'Angle'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.angle.Angle'>>]}, 'ref': 'kittycad.models.angle.Angle:94897783608256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitAngle'>, 'members': [UnitAngle.DEGREES, UnitAngle.RADIANS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_angle.UnitAngle:94897782236432', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'value': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Angle', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'axis': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'origin': {'metadata': {}, 'schema': {'cls': <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>, 'config': {'title': "RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]"}, 'custom_init': False, 'generic_origin': <class 'pydantic.root_model.RootModel'>, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'pydantic.root_model.RootModel[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]'>>]}, 'ref': "pydantic.root_model.RootModel:94897787678944[Annotated[Union[OptionLocal, OptionGlobal, OptionCustom], FieldInfo(annotation=NoneType, required=True, discriminator='type')]:140141937245008]", 'root_model': True, 'schema': {'choices': {'custom': {'cls': <class 'kittycad.models.origin_type.OptionCustom'>, 'config': {'title': 'OptionCustom'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionCustom'>>]}, 'ref': 'kittycad.models.origin_type.OptionCustom:94897785526736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'custom', 'schema': {'expected': ['custom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCustom', 'type': 'model-fields'}, 'type': 'model'}, 'global': {'cls': <class 'kittycad.models.origin_type.OptionGlobal'>, 'config': {'title': 'OptionGlobal'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.origin_type.OptionGlobal'>>]}, 'ref': 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'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'line_width': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'position': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94897781544448', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'text': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>, 'config': {'title': 'AnnotationTextOptions'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.annotation_text_options.AnnotationTextOptions'>>]}, 'ref': 'kittycad.models.annotation_text_options.AnnotationTextOptions:94897781549536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'point_size': {'metadata': {}, 'schema': {'type': 'int'}, 'type': 'model-field'}, 'text': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'x': {'metadata': {}, 'schema': {'cls': <enum 'AnnotationTextAlignmentX'>, 'members': [AnnotationTextAlignmentX.LEFT, AnnotationTextAlignmentX.CENTER, AnnotationTextAlignmentX.RIGHT], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_x.AnnotationTextAlignmentX:94897785629456', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'y': {'metadata': {}, 'schema': {'cls': <enum 'AnnotationTextAlignmentY'>, 'members': [AnnotationTextAlignmentY.BOTTOM, AnnotationTextAlignmentY.CENTER, AnnotationTextAlignmentY.TOP], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.annotation_text_alignment_y.AnnotationTextAlignmentY:94897785621552', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'AnnotationTextOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AnnotationOptions', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'update_annotation', 'schema': {'expected': ['update_annotation'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionUpdateAnnotation', 'type': 'model-fields'}, 'type': 'model'}, 'view_isometric': {'cls': <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>, 'config': {'title': 'OptionViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionViewIsometric'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionViewIsometric:94897788799248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'padding': {'metadata': {}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'view_isometric', 'schema': {'expected': ['view_isometric'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'volume': {'cls': <class 'kittycad.models.modeling_cmd.OptionVolume'>, 'config': {'title': 'OptionVolume'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionVolume'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionVolume:94897790184656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'output_unit': {'metadata': {}, 'schema': {'cls': <enum 'UnitVolume'>, 'members': [UnitVolume.CM3, UnitVolume.FT3, UnitVolume.IN3, UnitVolume.M3, UnitVolume.YD3, UnitVolume.USFLOZ, UnitVolume.USGAL, UnitVolume.L, UnitVolume.ML], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_volume.UnitVolume:94897788685216', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'volume', 'schema': {'expected': ['volume'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionVolume', 'type': 'model-fields'}, 'type': 'model'}, 'zoom_to_fit': {'cls': <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>, 'config': {'title': 'OptionZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.modeling_cmd.OptionZoomToFit'>>]}, 'ref': 'kittycad.models.modeling_cmd.OptionZoomToFit:94897790282128', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'animated': {'metadata': {}, 'schema': {'default': False, 'schema': {'type': 'bool'}, 'type': 'default'}, 'type': 'model-field'}, 'object_ids': {'metadata': {}, 'schema': {'default': [], 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'default'}, 'type': 'model-field'}, 'padding': {'metadata': {}, 'schema': {'default': 0.0, 'schema': {'type': 'float'}, 'type': 'default'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'zoom_to_fit', 'schema': {'expected': ['zoom_to_fit'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionZoomToFit', 'type': 'model-fields'}, 'type': 'model'}}, 'discriminator': 'type', 'from_attributes': True, 'metadata': {}, 'strict': False, 'type': 'tagged-union'}, 'type': 'model'}, 'type': 'model-field'}, 'cmd_id': {'metadata': {}, 'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'modeling_cmd_req', 'schema': {'expected': ['modeling_cmd_req'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionModelingCmdReq', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'cmd': FieldInfo(annotation=RootModel[Annotated[Union[OptionEngineUtilEvaluatePath, OptionStartPath, OptionMovePathPen, OptionExtendPath, OptionExtrude, OptionTwistExtrude, OptionSweep, OptionRevolve, OptionSolid3dShellFace, OptionRevolveAboutEdge, OptionLoft, OptionClosePath, OptionCameraDragStart, OptionCameraDragMove, OptionCameraDragEnd, OptionDefaultCameraGetSettings, OptionDefaultCameraGetView, OptionDefaultCameraSetView, OptionDefaultCameraLookAt, OptionDefaultCameraPerspectiveSettings, OptionDefaultCameraZoom, OptionExport2d, OptionExport3d, OptionExport, OptionEntityGetParentId, OptionEntityGetNumChildren, OptionEntityGetChildUuid, OptionEntityGetAllChildUuids, OptionEntityGetSketchPaths, OptionEntityGetDistance, OptionEntityClone, OptionEntityLinearPatternTransform, OptionEntityLinearPattern, OptionEntityCircularPattern, OptionEntityMakeHelix, OptionEntityMakeHelixFromParams, OptionEntityMakeHelixFromEdge, OptionEntityMirror, OptionEntityMirrorAcrossEdge, OptionSelectWithPoint, OptionSelectAdd, OptionSelectRemove, OptionSceneClearAll, OptionSelectReplace, OptionHighlightSetEntity, OptionHighlightSetEntities, OptionNewAnnotation, OptionUpdateAnnotation, OptionEdgeLinesVisible, OptionObjectVisible, OptionObjectBringToFront, OptionObjectSetMaterialParamsPbr, OptionGetEntityType, OptionSolid3dGetAllEdgeFaces, OptionSolid2dAddHole, OptionSolid3dGetAllOppositeEdges, OptionSolid3dGetOppositeEdge, OptionSolid3dGetNextAdjacentEdge, OptionSolid3dGetPrevAdjacentEdge, OptionSolid3dGetCommonEdge, OptionSolid3dFilletEdge, OptionFaceIsPlanar, OptionFaceGetPosition, OptionFaceGetCenter, OptionFaceGetGradient, OptionSendObject, OptionEntitySetOpacity, OptionEntityFade, OptionMakePlane, OptionPlaneSetColor, OptionSetTool, OptionMouseMove, OptionMouseClick, OptionSketchModeDisable, OptionGetSketchModePlane, OptionCurveSetConstraint, OptionEnableSketchMode, OptionEnableDryRun, OptionDisableDryRun, OptionSetBackgroundColor, OptionSetCurrentToolProperties, OptionSetDefaultSystemProperties, OptionCurveGetType, OptionCurveGetControlPoints, OptionProjectEntityToPlane, OptionProjectPointsToPlane, OptionTakeSnapshot, OptionMakeAxesGizmo, OptionPathGetInfo, OptionPathGetCurveUuidsForVertices, OptionPathGetCurveUuid, OptionPathGetVertexUuids, OptionPathGetSketchTargetUuid, OptionHandleMouseDragStart, OptionHandleMouseDragMove, OptionHandleMouseDragEnd, OptionRemoveSceneObjects, OptionPlaneIntersectAndProject, OptionCurveGetEndPoints, OptionReconfigureStream, OptionImportFiles, OptionSetSceneUnits, OptionMass, OptionDensity, OptionVolume, OptionCenterOfMass, OptionSurfaceArea, OptionDefaultCameraFocusOn, OptionSetSelectionType, OptionSetSelectionFilter, OptionDefaultCameraSetOrthographic, OptionDefaultCameraSetPerspective, OptionDefaultCameraCenterToSelection, OptionDefaultCameraCenterToScene, OptionZoomToFit, OptionOrientToFace, OptionViewIsometric, OptionSolid3dGetExtrusionFaceInfo, OptionSolid3dGetAdjacencyInfo, OptionSelectClear, OptionSelectGet, OptionGetNumObjects, OptionSetObjectTransform, OptionBooleanUnion, OptionBooleanIntersection, OptionBooleanSubtract, OptionMakeOffsetPath, OptionAddHoleFromOffset, OptionSetGridReferencePlane, OptionSetGridScale, OptionSetGridAutoScale], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'cmd_id': FieldInfo(annotation=ModelingCmdId, required=True), 'type': FieldInfo(annotation=Literal['modeling_cmd_req'], required=False, default='modeling_cmd_req')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d3d1b50,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f75574d41b0,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "modeling_cmd_req",                                             },                                             expected_py: None,                                             name: "literal['modeling_cmd_req']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "cmd": SerField {                         key_py: Py(                             0x00007f755a34ca50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Prebuilt(                                 PrebuiltSerializer {                                     schema_serializer: Py(                                         0x00007f7556357120,                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "cmd_id": SerField {                         key_py: Py(                             0x00007f7556dd3b40,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 3,             },         ),         has_extra: false,         root_model: false,         name: "OptionModelingCmdReq",     }, ), definitions=[Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75567601b0) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556761180) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556f1fb30) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75565e3120) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f75565e1e70) }), Enum(EnumSerializer { class: Py(0x564f1c8f2600), serializer: Some(Str(StrSerializer)) }), Prebuilt(PrebuiltSerializer { schema_serializer: Py(0x7f7556712e40) })])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionModelingCmdReq", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "cmd",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "cmd",                                         py_key: Py(                                             0x00007f7556fa19b0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755a34ca50,                         ),                         validator: Prebuilt(                             PrebuiltValidator {                                 schema_validator: Py(                                     0x0000564f1d17c170,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "cmd_id",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "cmd_id",                                         py_key: Py(                                             0x00007f7556fa1680,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f7556dd3b40,                         ),                         validator: FunctionAfter(                             FunctionAfterValidator {                                 validator: Str(                                     StrValidator {                                         strict: false,                                         coerce_numbers_to_str: false,                                     },                                 ),                                 func: Py(                                     0x0000564f1cd071d0,                                 ),                                 config: Py(                                     0x00007f755605a600,                                 ),                                 name: "function-after[ModelingCmdId(), str]",                                 field_name: None,                                 info_arg: false,                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa17d0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f75574d41b0,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "modeling_cmd_req": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556059fc0,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f75574d41b0,                                                 ),                                             ],                                         },                                         expected_repr: "'modeling_cmd_req'",                                         name: "literal['modeling_cmd_req']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['modeling_cmd_req']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionModelingCmdReq",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d3d1b50,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionModelingCmdReq",     }, ), definitions=[StrEnum(EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py(0x564f1c8f2600), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some({"none": 0, "arrow": 1}), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some(Py(0x7f755605a580)), values: [Py(0x7f7556749a30), Py(0x7f7556749d30)] }, missing: None, expected_repr: "'none' or 'arrow'", strict: false, class_repr: "AnnotationLineEnd", name: "str-enum[AnnotationLineEnd]" }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1c93d9d0) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cc86ec0) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cdb8500) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1d08fe90) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cda5a20) }), Prebuilt(PrebuiltValidator { schema_validator: Py(0x564f1cd02920) })], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, cmd: pydantic.root_model.RootModel[Annotated[Union[OptionEngineUtilEvaluatePath, OptionStartPath, OptionMovePathPen, OptionExtendPath, OptionExtrude, OptionTwistExtrude, OptionSweep, OptionRevolve, OptionSolid3dShellFace, OptionRevolveAboutEdge, OptionLoft, OptionClosePath, OptionCameraDragStart, OptionCameraDragMove, OptionCameraDragEnd, OptionDefaultCameraGetSettings, OptionDefaultCameraGetView, OptionDefaultCameraSetView, OptionDefaultCameraLookAt, OptionDefaultCameraPerspectiveSettings, OptionDefaultCameraZoom, OptionExport2d, OptionExport3d, OptionExport, OptionEntityGetParentId, OptionEntityGetNumChildren, OptionEntityGetChildUuid, OptionEntityGetAllChildUuids, OptionEntityGetSketchPaths, OptionEntityGetDistance, OptionEntityClone, OptionEntityLinearPatternTransform, OptionEntityLinearPattern, OptionEntityCircularPattern, OptionEntityMakeHelix, OptionEntityMakeHelixFromParams, OptionEntityMakeHelixFromEdge, OptionEntityMirror, OptionEntityMirrorAcrossEdge, OptionSelectWithPoint, OptionSelectAdd, OptionSelectRemove, OptionSceneClearAll, OptionSelectReplace, OptionHighlightSetEntity, OptionHighlightSetEntities, OptionNewAnnotation, OptionUpdateAnnotation, OptionEdgeLinesVisible, OptionObjectVisible, OptionObjectBringToFront, OptionObjectSetMaterialParamsPbr, OptionGetEntityType, OptionSolid3dGetAllEdgeFaces, OptionSolid2dAddHole, OptionSolid3dGetAllOppositeEdges, OptionSolid3dGetOppositeEdge, OptionSolid3dGetNextAdjacentEdge, OptionSolid3dGetPrevAdjacentEdge, OptionSolid3dGetCommonEdge, OptionSolid3dFilletEdge, OptionFaceIsPlanar, OptionFaceGetPosition, OptionFaceGetCenter, OptionFaceGetGradient, OptionSendObject, OptionEntitySetOpacity, OptionEntityFade, OptionMakePlane, OptionPlaneSetColor, OptionSetTool, OptionMouseMove, OptionMouseClick, OptionSketchModeDisable, OptionGetSketchModePlane, OptionCurveSetConstraint, OptionEnableSketchMode, OptionEnableDryRun, OptionDisableDryRun, OptionSetBackgroundColor, OptionSetCurrentToolProperties, OptionSetDefaultSystemProperties, OptionCurveGetType, OptionCurveGetControlPoints, OptionProjectEntityToPlane, OptionProjectPointsToPlane, OptionTakeSnapshot, OptionMakeAxesGizmo, OptionPathGetInfo, OptionPathGetCurveUuidsForVertices, OptionPathGetCurveUuid, OptionPathGetVertexUuids, OptionPathGetSketchTargetUuid, OptionHandleMouseDragStart, OptionHandleMouseDragMove, OptionHandleMouseDragEnd, OptionRemoveSceneObjects, OptionPlaneIntersectAndProject, OptionCurveGetEndPoints, OptionReconfigureStream, OptionImportFiles, OptionSetSceneUnits, OptionMass, OptionDensity, OptionVolume, OptionCenterOfMass, OptionSurfaceArea, OptionDefaultCameraFocusOn, OptionSetSelectionType, OptionSetSelectionFilter, OptionDefaultCameraSetOrthographic, OptionDefaultCameraSetPerspective, OptionDefaultCameraCenterToSelection, OptionDefaultCameraCenterToScene, OptionZoomToFit, OptionOrientToFace, OptionViewIsometric, OptionSolid3dGetExtrusionFaceInfo, OptionSolid3dGetAdjacencyInfo, OptionSelectClear, OptionSelectGet, OptionGetNumObjects, OptionSetObjectTransform, OptionBooleanUnion, OptionBooleanIntersection, OptionBooleanSubtract, OptionMakeOffsetPath, OptionAddHoleFromOffset, OptionSetGridReferencePlane, OptionSetGridScale, OptionSetGridAutoScale], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], cmd_id: kittycad.models.modeling_cmd_id.ModelingCmdId, type: Literal['modeling_cmd_req'] = 'modeling_cmd_req') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

cmd: RootModel[Annotated[Union[OptionEngineUtilEvaluatePath, OptionStartPath, OptionMovePathPen, OptionExtendPath, OptionExtrude, OptionTwistExtrude, OptionSweep, OptionRevolve, OptionSolid3dShellFace, OptionRevolveAboutEdge, OptionLoft, OptionClosePath, OptionCameraDragStart, OptionCameraDragMove, OptionCameraDragEnd, OptionDefaultCameraGetSettings, OptionDefaultCameraGetView, OptionDefaultCameraSetView, OptionDefaultCameraLookAt, OptionDefaultCameraPerspectiveSettings, OptionDefaultCameraZoom, OptionExport2d, OptionExport3d, OptionExport, OptionEntityGetParentId, OptionEntityGetNumChildren, OptionEntityGetChildUuid, OptionEntityGetAllChildUuids, OptionEntityGetSketchPaths, OptionEntityGetDistance, OptionEntityClone, OptionEntityLinearPatternTransform, OptionEntityLinearPattern, OptionEntityCircularPattern, OptionEntityMakeHelix, OptionEntityMakeHelixFromParams, OptionEntityMakeHelixFromEdge, OptionEntityMirror, OptionEntityMirrorAcrossEdge, OptionSelectWithPoint, OptionSelectAdd, OptionSelectRemove, OptionSceneClearAll, OptionSelectReplace, OptionHighlightSetEntity, OptionHighlightSetEntities, OptionNewAnnotation, OptionUpdateAnnotation, OptionEdgeLinesVisible, OptionObjectVisible, OptionObjectBringToFront, OptionObjectSetMaterialParamsPbr, OptionGetEntityType, OptionSolid3dGetAllEdgeFaces, OptionSolid2dAddHole, OptionSolid3dGetAllOppositeEdges, OptionSolid3dGetOppositeEdge, OptionSolid3dGetNextAdjacentEdge, OptionSolid3dGetPrevAdjacentEdge, OptionSolid3dGetCommonEdge, OptionSolid3dFilletEdge, OptionFaceIsPlanar, OptionFaceGetPosition, OptionFaceGetCenter, OptionFaceGetGradient, OptionSendObject, OptionEntitySetOpacity, OptionEntityFade, OptionMakePlane, OptionPlaneSetColor, OptionSetTool, OptionMouseMove, OptionMouseClick, OptionSketchModeDisable, OptionGetSketchModePlane, OptionCurveSetConstraint, OptionEnableSketchMode, OptionEnableDryRun, OptionDisableDryRun, OptionSetBackgroundColor, OptionSetCurrentToolProperties, OptionSetDefaultSystemProperties, OptionCurveGetType, OptionCurveGetControlPoints, OptionProjectEntityToPlane, OptionProjectPointsToPlane, OptionTakeSnapshot, OptionMakeAxesGizmo, OptionPathGetInfo, OptionPathGetCurveUuidsForVertices, OptionPathGetCurveUuid, OptionPathGetVertexUuids, OptionPathGetSketchTargetUuid, OptionHandleMouseDragStart, OptionHandleMouseDragMove, OptionHandleMouseDragEnd, OptionRemoveSceneObjects, OptionPlaneIntersectAndProject, OptionCurveGetEndPoints, OptionReconfigureStream, OptionImportFiles, OptionSetSceneUnits, OptionMass, OptionDensity, OptionVolume, OptionCenterOfMass, OptionSurfaceArea, OptionDefaultCameraFocusOn, OptionSetSelectionType, OptionSetSelectionFilter, OptionDefaultCameraSetOrthographic, OptionDefaultCameraSetPerspective, OptionDefaultCameraCenterToSelection, OptionDefaultCameraCenterToScene, OptionZoomToFit, OptionOrientToFace, OptionViewIsometric, OptionSolid3dGetExtrusionFaceInfo, OptionSolid3dGetAdjacencyInfo, OptionSelectClear, OptionSelectGet, OptionGetNumObjects, OptionSetObjectTransform, OptionBooleanUnion, OptionBooleanIntersection, OptionBooleanSubtract, OptionMakeOffsetPath, OptionAddHoleFromOffset, OptionSetGridReferencePlane, OptionSetGridScale, OptionSetGridAutoScale], FieldInfo(annotation=NoneType, required=True, discriminator='type')]]

cmd_id: ModelingCmdId

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'cmd': FieldInfo(annotation=RootModel[Annotated[Union[OptionEngineUtilEvaluatePath, OptionStartPath, OptionMovePathPen, OptionExtendPath, OptionExtrude, OptionTwistExtrude, OptionSweep, OptionRevolve, OptionSolid3dShellFace, OptionRevolveAboutEdge, OptionLoft, OptionClosePath, OptionCameraDragStart, OptionCameraDragMove, OptionCameraDragEnd, OptionDefaultCameraGetSettings, OptionDefaultCameraGetView, OptionDefaultCameraSetView, OptionDefaultCameraLookAt, OptionDefaultCameraPerspectiveSettings, OptionDefaultCameraZoom, OptionExport2d, OptionExport3d, OptionExport, OptionEntityGetParentId, OptionEntityGetNumChildren, OptionEntityGetChildUuid, OptionEntityGetAllChildUuids, OptionEntityGetSketchPaths, OptionEntityGetDistance, OptionEntityClone, OptionEntityLinearPatternTransform, OptionEntityLinearPattern, OptionEntityCircularPattern, OptionEntityMakeHelix, OptionEntityMakeHelixFromParams, OptionEntityMakeHelixFromEdge, OptionEntityMirror, OptionEntityMirrorAcrossEdge, OptionSelectWithPoint, OptionSelectAdd, OptionSelectRemove, OptionSceneClearAll, OptionSelectReplace, OptionHighlightSetEntity, OptionHighlightSetEntities, OptionNewAnnotation, OptionUpdateAnnotation, OptionEdgeLinesVisible, OptionObjectVisible, OptionObjectBringToFront, OptionObjectSetMaterialParamsPbr, OptionGetEntityType, OptionSolid3dGetAllEdgeFaces, OptionSolid2dAddHole, OptionSolid3dGetAllOppositeEdges, OptionSolid3dGetOppositeEdge, OptionSolid3dGetNextAdjacentEdge, OptionSolid3dGetPrevAdjacentEdge, OptionSolid3dGetCommonEdge, OptionSolid3dFilletEdge, OptionFaceIsPlanar, OptionFaceGetPosition, OptionFaceGetCenter, OptionFaceGetGradient, OptionSendObject, OptionEntitySetOpacity, OptionEntityFade, OptionMakePlane, OptionPlaneSetColor, OptionSetTool, OptionMouseMove, OptionMouseClick, OptionSketchModeDisable, OptionGetSketchModePlane, OptionCurveSetConstraint, OptionEnableSketchMode, OptionEnableDryRun, OptionDisableDryRun, OptionSetBackgroundColor, OptionSetCurrentToolProperties, OptionSetDefaultSystemProperties, OptionCurveGetType, OptionCurveGetControlPoints, OptionProjectEntityToPlane, OptionProjectPointsToPlane, OptionTakeSnapshot, OptionMakeAxesGizmo, OptionPathGetInfo, OptionPathGetCurveUuidsForVertices, OptionPathGetCurveUuid, OptionPathGetVertexUuids, OptionPathGetSketchTargetUuid, OptionHandleMouseDragStart, OptionHandleMouseDragMove, OptionHandleMouseDragEnd, OptionRemoveSceneObjects, OptionPlaneIntersectAndProject, OptionCurveGetEndPoints, OptionReconfigureStream, OptionImportFiles, OptionSetSceneUnits, OptionMass, OptionDensity, OptionVolume, OptionCenterOfMass, OptionSurfaceArea, OptionDefaultCameraFocusOn, OptionSetSelectionType, OptionSetSelectionFilter, OptionDefaultCameraSetOrthographic, OptionDefaultCameraSetPerspective, OptionDefaultCameraCenterToSelection, OptionDefaultCameraCenterToScene, OptionZoomToFit, OptionOrientToFace, OptionViewIsometric, OptionSolid3dGetExtrusionFaceInfo, OptionSolid3dGetAdjacencyInfo, OptionSelectClear, OptionSelectGet, OptionGetNumObjects, OptionSetObjectTransform, OptionBooleanUnion, OptionBooleanIntersection, OptionBooleanSubtract, OptionMakeOffsetPath, OptionAddHoleFromOffset, OptionSetGridReferencePlane, OptionSetGridScale, OptionSetGridAutoScale], FieldInfo(annotation=NoneType, required=True, discriminator='type')]], required=True), 'cmd_id': FieldInfo(annotation=ModelingCmdId, required=True), 'type': FieldInfo(annotation=Literal['modeling_cmd_req'], required=False, default='modeling_cmd_req')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['modeling_cmd_req']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionPing(**data)

The client-to-server Ping to ensure the WebSocket stays alive.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['ping']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 60

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionPing'>, 'config': {'title': 'OptionPing'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionPing'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionPing:94897792939200', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {}, 'schema': {'default': 'ping', 'schema': {'expected': ['ping'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPing', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['ping'], required=False, default='ping')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d3cf4c0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f75576c2040,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "ping",                                             },                                             expected_py: None,                                             name: "literal['ping']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 1,             },         ),         has_extra: false,         root_model: false,         name: "OptionPing",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPing", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa1a70,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f75576c2040,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "ping": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f7556058900,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f75576c2040,                                                 ),                                             ],                                         },                                         expected_repr: "'ping'",                                         name: "literal['ping']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['ping']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionPing",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d3cf4c0,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionPing",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, type: Literal['ping'] = 'ping') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'type': FieldInfo(annotation=Literal['ping'], required=False, default='ping')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['ping']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionSdpOffer(**data)

The SDP offer request.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_config': 'ClassVar[ConfigDict]', 'offer': <class 'kittycad.models.rtc_session_description.RtcSessionDescription'>, 'type': typing.Literal['sdp_offer']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 24

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionSdpOffer'>, 'config': {'title': 'OptionSdpOffer'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionSdpOffer'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionSdpOffer:94897793169488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'offer': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.rtc_session_description.RtcSessionDescription'>, 'config': {'title': 'RtcSessionDescription'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.rtc_session_description.RtcSessionDescription'>>]}, 'ref': 'kittycad.models.rtc_session_description.RtcSessionDescription:94897792489248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'sdp': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'cls': <enum 'RtcSdpType'>, 'members': [RtcSdpType.UNSPECIFIED, RtcSdpType.OFFER, RtcSdpType.PRANSWER, RtcSdpType.ANSWER, RtcSdpType.ROLLBACK], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.rtc_sdp_type.RtcSdpType:94897790868144', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'RtcSessionDescription', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'sdp_offer', 'schema': {'expected': ['sdp_offer'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSdpOffer', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'offer': FieldInfo(annotation=RtcSessionDescription, required=True), 'type': FieldInfo(annotation=Literal['sdp_offer'], required=False, default='sdp_offer')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d407850,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "offer": SerField {                         key_py: Py(                             0x00007f7556de0120,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Prebuilt(                                 PrebuiltSerializer {                                     schema_serializer: Py(                                         0x00007f7556115d00,                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f7556057f30,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "sdp_offer",                                             },                                             expected_py: None,                                             name: "literal['sdp_offer']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 2,             },         ),         has_extra: false,         root_model: false,         name: "OptionSdpOffer",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSdpOffer", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "offer",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "offer",                                         py_key: Py(                                             0x00007f7556fa1740,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f7556de0120,                         ),                         validator: Prebuilt(                             PrebuiltValidator {                                 schema_validator: Py(                                     0x0000564f1d1d5360,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa18c0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f7556057f30,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "sdp_offer": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f755605b000,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f7556057f30,                                                 ),                                             ],                                         },                                         expected_repr: "'sdp_offer'",                                         name: "literal['sdp_offer']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['sdp_offer']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionSdpOffer",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d407850,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionSdpOffer",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, offer: kittycad.models.rtc_session_description.RtcSessionDescription, type: Literal['sdp_offer'] = 'sdp_offer') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'offer': FieldInfo(annotation=RtcSessionDescription, required=True), 'type': FieldInfo(annotation=Literal['sdp_offer'], required=False, default='sdp_offer')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

offer: RtcSessionDescription

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['sdp_offer']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self

class kittycad.models.web_socket_request.OptionTrickleIce(**data)

The trickle ICE candidate request.

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.

__abstractmethods__ = frozenset({})

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__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_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'candidate': <class 'kittycad.models.rtc_ice_candidate_init.RtcIceCandidateInit'>, 'model_config': 'ClassVar[ConfigDict]', 'type': typing.Literal['trickle_ice']}

classmethod __class_getitem__(typevar_values)

Return type:

type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

__delattr__(item)

Implement delattr(self, name).

Return type:

Any

__dict__

__eq__(other)

Return self==value.

Return type:

bool

property __fields_set__: set[str]

__firstlineno__ = 14

classmethod __get_pydantic_core_schema__(source, handler, /)

Return type:

Union[InvalidSchema, 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, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, 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, ArgumentsV3Schema, 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)

Return type:

Any

__getstate__()

Helper for pickle.

Return type:

dict[Any, Any]

__hash__ = None

__init__(**data)

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__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__module__ = 'kittycad.models.web_socket_request'

__pretty__(fmt, **kwargs)

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

Return type:

Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}

Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True

Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.web_socket_request.OptionTrickleIce'>, 'config': {'title': 'OptionTrickleIce'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.web_socket_request.OptionTrickleIce'>>]}, 'ref': 'kittycad.models.web_socket_request.OptionTrickleIce:94897794160416', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'candidate': {'metadata': {}, 'schema': {'cls': <class 'kittycad.models.rtc_ice_candidate_init.RtcIceCandidateInit'>, 'config': {'title': 'RtcIceCandidateInit'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.rtc_ice_candidate_init.RtcIceCandidateInit'>>]}, 'ref': 'kittycad.models.rtc_ice_candidate_init.RtcIceCandidateInit:94897791363024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'candidate': {'metadata': {}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'sdpMLineIndex': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'sdpMid': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'usernameFragment': {'metadata': {}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'RtcIceCandidateInit', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {}, 'schema': {'default': 'trickle_ice', 'schema': {'expected': ['trickle_ice'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTrickleIce', 'type': 'model-fields'}, 'type': 'model'}

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False

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={})

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

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'candidate': FieldInfo(annotation=RtcIceCandidateInit, required=True), 'type': FieldInfo(annotation=Literal['trickle_ice'], required=False, default='trickle_ice')}

A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}

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)

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

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None

The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model(     ModelSerializer {         class: Py(             0x0000564f1d4f9720,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "type": SerField {                         key_py: Py(                             0x00007f755b111b50,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f75563e5170,                                         ),                                     ),                                     serializer: Literal(                                         LiteralSerializer {                                             expected_int: {},                                             expected_str: {                                                 "trickle_ice",                                             },                                             expected_py: None,                                             name: "literal['trickle_ice']",                                         },                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                     "candidate": SerField {                         key_py: Py(                             0x00007f755a7d4270,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Prebuilt(                                 PrebuiltSerializer {                                     schema_serializer: Py(                                         0x00007f7556115b90,                                     ),                                 },                             ),                         ),                         required: true,                         serialize_by_alias: None,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 2,             },         ),         has_extra: false,         root_model: false,         name: "OptionTrickleIce",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}

__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionTrickleIce", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "candidate",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "candidate",                                         py_key: Py(                                             0x00007f755605b6f0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755a7d4270,                         ),                         validator: Prebuilt(                             PrebuiltValidator {                                 schema_validator: Py(                                     0x0000564f1d26d980,                                 ),                             },                         ),                         frozen: false,                     },                     Field {                         name: "type",                         lookup_key_collection: LookupKeyCollection {                             by_name: Simple(                                 LookupPath {                                     first_item: PathItemString {                                         key: "type",                                         py_key: Py(                                             0x00007f7556fa13b0,                                         ),                                     },                                     rest: [],                                 },                             ),                             by_alias: None,                             by_alias_then_name: None,                         },                         name_py: Py(                             0x00007f755b111b50,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f75563e5170,                                     ),                                 ),                                 on_error: Raise,                                 validator: Literal(                                     LiteralValidator {                                         lookup: LiteralLookup {                                             expected_bool: None,                                             expected_int: None,                                             expected_str: Some(                                                 {                                                     "trickle_ice": 0,                                                 },                                             ),                                             expected_py_dict: None,                                             expected_py_values: None,                                             expected_py_primitives: Some(                                                 Py(                                                     0x00007f755605bd00,                                                 ),                                             ),                                             values: [                                                 Py(                                                     0x00007f75563e5170,                                                 ),                                             ],                                         },                                         expected_repr: "'trickle_ice'",                                         name: "literal['trickle_ice']",                                     },                                 ),                                 validate_default: false,                                 copy_default: false,                                 name: "default[literal['trickle_ice']]",                                 undefined: Py(                                     0x00007f7558d6e410,                                 ),                             },                         ),                         frozen: false,                     },                 ],                 model_name: "OptionTrickleIce",                 extra_behavior: Ignore,                 extras_validator: None,                 extras_keys_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,                 validate_by_alias: None,                 validate_by_name: None,             },         ),         class: Py(             0x0000564f1d4f9720,         ),         generic_origin: None,         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f7558d6e410,         ),         name: "OptionTrickleIce",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__replace__(**changes)

Return type:

Self

__repr__()

Return repr(self).

Return type:

str

__repr_args__()

Return type:

Iterable[tuple[str | None, Any]]

__repr_name__()

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

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

__repr_str__(join_str)

Return type:

str

__rich_repr__()

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)

Implement setattr(self, name, value).

Return type:

None

__setstate__(state)

Return type:

None

__signature__: ClassVar[Signature] = <Signature (*, candidate: kittycad.models.rtc_ice_candidate_init.RtcIceCandidateInit, type: Literal['trickle_ice'] = 'trickle_ice') -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')

__static_attributes__ = ()

__str__()

Return str(self).

Return type:

str

_abc_impl = <_abc._abc_data object>

_calculate_keys(*args, **kwargs)

Return type:

Any

_copy_and_set_values(*args, **kwargs)

Return type:

Any

classmethod _get_value(*args, **kwargs)

Return type:

Any

_iter(*args, **kwargs)

Return type:

Any

_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Return type:

Optional[Callable[[BaseModel, str, Any], None]]

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

candidate: RtcIceCandidateInit

classmethod construct(_fields_set=None, **values)

Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

classmethod from_orm(obj)

Return type:

Self

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)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

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)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], 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, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – 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].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• 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

Get extra fields set during validation.

Returns:

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

model_fields = {'candidate': FieldInfo(annotation=RtcIceCandidateInit, required=True), 'type': FieldInfo(annotation=Literal['trickle_ice'], required=False, default='trickle_ice')}

property model_fields_set: set[str]

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')

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)

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(context, /)

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)

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 (Mapping[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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

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, by_alias=None, by_name=None)

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.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

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

Return type:

Self

classmethod parse_obj(obj)

Return type:

Self

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

Return type:

Self

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

Return type:

Dict[str, Any]

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

Return type:

str

type: Literal['trickle_ice']

classmethod update_forward_refs(**localns)

Return type:

None

classmethod validate(value)

Return type:

Self