TypingStyle

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.

Method to clone the style and override some values

Returns a copy of the model.

!!! warning "Deprecated" This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

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

Args: 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.

Method to fill the background color and icon color if they are not set.

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy

Returns a copy of the model.

Args: update: 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: Set to True to make a deep copy of the model.

Returns: New model instance.

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump

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

Args: mode: 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: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field's alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError]. serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns: A dictionary representation of the model.

Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json

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

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

Returns: A JSON string representation of the model.

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.

Convert a ModelDTO to a json dictionary. This dict can be serialized.

Convert a ModelDTO to a json string.

Create a ModelDTO from a json.

Create a list of ModelDTO from a list of json.

Create a ModelDTO from a string json.

Create a dict of ModelDTO from a dict of json.

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.

Args: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.

Returns: A new instance of the Model class with validated data.

Generates a JSON schema for a model class.

Args: by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications mode: The mode in which to generate the schema.

Returns: The JSON schema for the given model class.

Compute the class name for parametrizations of generic classes.

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

Args: params: 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.

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.

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.

Args: force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to 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.

Validate a pydantic model instance.

Args: obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.

Raises: ValidationError: If the object could not be validated.

Returns: The validated model instance.

Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args: json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.

Returns: The validated Pydantic model.

Raises: ValueError: If json_data is not a JSON string.

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

Args: obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.

Returns: The validated Pydantic model.

Use an icon from the community icon library. List of available icons are here : https://constellab.community/icons

            If not defined the color is calculated based on background color to be visible. defaults to None

Use an image from the community image library. List of available images are here : https://constellab.community/icons

Use an icon from the material icon library. List of available icons are here : https://fonts.google.com/icons?icon.set=Material+Icons

            If not defined the color is calculated based on background color to be visible. defaults to None