_license#

Models for the license object.

Classes#

`Issuer`	Issuer object.
`LicenseType`	License type object.
`License`	License object.

class Issuer(**data)#

Bases: pydantic.BaseModel

Issuer object.

Methods#
`model_computed_fields()`	Get the computed fields of this model instance.
`model_extra()`	Get extra fields set during validation.
`model_fields_set()`	Returns the set of fields that have been explicitly set on this model instance.
`model_construct()`	Creates a new instance of the Model class with validated data.
`model_copy()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
`model_dump()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
`model_dump_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
`model_json_schema()`	Generates a JSON schema for a model class.
`model_parametrized_name()`	Compute the class name for parametrizations of generic classes.
`model_post_init()`	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild()`	Try to rebuild the pydantic-core schema for the model.
`model_validate()`	Validate a pydantic model instance.
`model_validate_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
`model_validate_strings()`	Validate the given object contains string data against the Pydantic model.
`__get_pydantic_core_schema__()`	Hook into generating the model’s CoreSchema.
`__get_pydantic_json_schema__()`	Hook into generating the model’s JSON schema.
`__pydantic_init_subclass__()`	This is intended to behave just like __init_subclass__, but is called by ModelMetaclass
`__copy__()`	Returns a shallow copy of the model.
`__deepcopy__()`	Returns a deep copy of the model.
`__delattr__()`	Implement delattr(self, name).
`__eq__()`	Return self==value.
`__iter__()`	So dict(model) works.
`copy()`	Returns a copy of the model.

Parameters: data (Any) –

__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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Parameters: data (Any) –
Return type: None

property model_computed_fields#

Get the computed fields of this model instance.

Returns: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
Return type: dict[str, pydantic.fields.ComputedFieldInfo]

property model_extra#

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to “allow”.
Return type: dict[str, Any] | None

property model_fields_set#

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.

Return type

set[str]

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. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

_fields_set (set[str] | None) – The set of field names accepted for the Model instance.
values (Any) – Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

Return type

Model

model_copy(*, update=None, deep=False)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters

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

Returns

New model instance.

Return type

Model

model_dump(*, mode='python', include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

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

Parameters

mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.
include (IncEx) – A list of fields to include in the output.
exclude (IncEx) – A list of fields to exclude from the output.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value from the output.
exclude_none (bool) – Whether to exclude fields that have a value of None from the output.
round_trip (bool) – Whether to enable serialization and deserialization round-trip support.
warnings (bool) – Whether to log warnings when invalid fields are encountered.

Returns

A dictionary representation of the model.

Return type

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

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

Parameters

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx) – Field(s) to include in the JSON output. Can take either a string or set of strings.
exclude (IncEx) – Field(s) to exclude from the JSON output. Can take either a string or set of strings.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that have the default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – Whether to use serialization/deserialization between JSON and class instance.
warnings (bool) – Whether to show any warnings that occurred during serialization.

Returns

A JSON string representation of the model.

Return type

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=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[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

Return type

dict[str, Any]

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], Ellipsis]) – 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.
Return type: str

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.

Parameters: __context (Any) –
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 (dict[str, Any] | None) – 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.

Return type

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)#

Validate a pydantic model instance.

Parameters

obj (Any) – The object to validate.
strict (bool | None) – Whether to raise an exception on invalid fields.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (dict[str, Any] | None) – Additional context to pass to the validator.

Raises

ValidationError – If the object could not be validated.

Returns

The validated model instance.

Return type

Model

classmethod model_validate_json(json_data, *, strict=None, context=None)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters

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

Returns

The validated Pydantic model.

Raises

ValueError – If json_data is not a JSON string.

Return type

Model

classmethod model_validate_strings(obj, *, strict=None, context=None)#

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

Parameters

obj (Any) – The object contains string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (dict[str, Any] | None) – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Return type

Model

classmethod __get_pydantic_core_schema__(__source, __handler)#

Hook into generating the model’s CoreSchema.

Parameters

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

Returns

A pydantic-core CoreSchema.

Return type

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(__core_schema, __handler)#

Hook into generating the model’s JSON schema.

Parameters

__core_schema (pydantic_core.CoreSchema) – 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 (pydantic.annotated_handlers.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.

Returns

A JSON schema, as a Python object.

Return type

pydantic.json_schema.JsonSchemaValue

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 keyword arguments passed to the class definition that aren’t used internally by pydantic.
kwargs (Any) –

Return type

None

__copy__()#

Returns a shallow copy of the model.

Return type: Model

__deepcopy__(memo=None)#

Returns a deep copy of the model.

Parameters: memo (dict[int, Any] | None) –
Return type: Model

__delattr__(item)#

Implement delattr(self, name).

Parameters: item (str) –
Return type: Any

__eq__(other)#

Return self==value.

Parameters: other (Any) –
Return type: bool

__iter__()#

So dict(model) works.

Return type: TupleGenerator

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:

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

Parameters

include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – 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.

Return type

Model

class LicenseType(**data)#

Bases: pydantic.BaseModel

License type object.

Methods#
`model_computed_fields()`	Get the computed fields of this model instance.
`model_extra()`	Get extra fields set during validation.
`model_fields_set()`	Returns the set of fields that have been explicitly set on this model instance.
`model_construct()`	Creates a new instance of the Model class with validated data.
`model_copy()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
`model_dump()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
`model_dump_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
`model_json_schema()`	Generates a JSON schema for a model class.
`model_parametrized_name()`	Compute the class name for parametrizations of generic classes.
`model_post_init()`	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild()`	Try to rebuild the pydantic-core schema for the model.
`model_validate()`	Validate a pydantic model instance.
`model_validate_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
`model_validate_strings()`	Validate the given object contains string data against the Pydantic model.
`__get_pydantic_core_schema__()`	Hook into generating the model’s CoreSchema.
`__get_pydantic_json_schema__()`	Hook into generating the model’s JSON schema.
`__pydantic_init_subclass__()`	This is intended to behave just like __init_subclass__, but is called by ModelMetaclass
`__copy__()`	Returns a shallow copy of the model.
`__deepcopy__()`	Returns a deep copy of the model.
`__delattr__()`	Implement delattr(self, name).
`__eq__()`	Return self==value.
`__iter__()`	So dict(model) works.
`copy()`	Returns a copy of the model.

Parameters: data (Any) –

__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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Parameters: data (Any) –
Return type: None

property model_computed_fields#

Get the computed fields of this model instance.

Returns: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
Return type: dict[str, pydantic.fields.ComputedFieldInfo]

property model_extra#

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to “allow”.
Return type: dict[str, Any] | None

property model_fields_set#

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.

Return type

set[str]

classmethod model_construct(_fields_set=None, **values)#

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

Parameters

_fields_set (set[str] | None) – The set of field names accepted for the Model instance.
values (Any) – Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

Return type

Model

model_copy(*, update=None, deep=False)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters

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

Returns

New model instance.

Return type

Model

model_dump(*, mode='python', include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

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

Parameters

mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.
include (IncEx) – A list of fields to include in the output.
exclude (IncEx) – A list of fields to exclude from the output.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value from the output.
exclude_none (bool) – Whether to exclude fields that have a value of None from the output.
round_trip (bool) – Whether to enable serialization and deserialization round-trip support.
warnings (bool) – Whether to log warnings when invalid fields are encountered.

Returns

A dictionary representation of the model.

Return type

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

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

Parameters

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx) – Field(s) to include in the JSON output. Can take either a string or set of strings.
exclude (IncEx) – Field(s) to exclude from the JSON output. Can take either a string or set of strings.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that have the default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – Whether to use serialization/deserialization between JSON and class instance.
warnings (bool) – Whether to show any warnings that occurred during serialization.

Returns

A JSON string representation of the model.

Return type

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=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[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

Return type

dict[str, Any]

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], Ellipsis]) – 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.
Return type: str

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.

Parameters: __context (Any) –
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 (dict[str, Any] | None) – 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.

Return type

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)#

Validate a pydantic model instance.

Parameters

obj (Any) – The object to validate.
strict (bool | None) – Whether to raise an exception on invalid fields.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (dict[str, Any] | None) – Additional context to pass to the validator.

Raises

ValidationError – If the object could not be validated.

Returns

The validated model instance.

Return type

Model

classmethod model_validate_json(json_data, *, strict=None, context=None)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters

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

Returns

The validated Pydantic model.

Raises

ValueError – If json_data is not a JSON string.

Return type

Model

classmethod model_validate_strings(obj, *, strict=None, context=None)#

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

Parameters

obj (Any) – The object contains string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (dict[str, Any] | None) – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Return type

Model

classmethod __get_pydantic_core_schema__(__source, __handler)#

Hook into generating the model’s CoreSchema.

Parameters

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

Returns

A pydantic-core CoreSchema.

Return type

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(__core_schema, __handler)#

Hook into generating the model’s JSON schema.

Parameters

__core_schema (pydantic_core.CoreSchema) – 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 (pydantic.annotated_handlers.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.

Returns

A JSON schema, as a Python object.

Return type

pydantic.json_schema.JsonSchemaValue

classmethod __pydantic_init_subclass__(**kwargs)#

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 keyword arguments passed to the class definition that aren’t used internally by pydantic.
kwargs (Any) –

Return type

None

__copy__()#

Returns a shallow copy of the model.

Return type: Model

__deepcopy__(memo=None)#

Returns a deep copy of the model.

Parameters: memo (dict[int, Any] | None) –
Return type: Model

__delattr__(item)#

Implement delattr(self, name).

Parameters: item (str) –
Return type: Any

__eq__(other)#

Return self==value.

Parameters: other (Any) –
Return type: bool

__iter__()#

So dict(model) works.

Return type: TupleGenerator

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:

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

Parameters

include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – 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.

Return type

Model

class License(**data)#

Bases: pydantic.BaseModel

License object.

Methods#
`generate_private_key()`	Generates a private key for the license.
`generate_public_key()`	Generates a public key for the license.
`generate_signature()`	Generates a signature for the license.
`check_license()`	Validates the license in TUMULT_ANALYTICS_PRO_LICENSE.
`verify_signature()`	Verifies the signature of the license.
`model_computed_fields()`	Get the computed fields of this model instance.
`model_extra()`	Get extra fields set during validation.
`model_fields_set()`	Returns the set of fields that have been explicitly set on this model instance.
`model_construct()`	Creates a new instance of the Model class with validated data.
`model_copy()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
`model_dump()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
`model_dump_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
`model_json_schema()`	Generates a JSON schema for a model class.
`model_parametrized_name()`	Compute the class name for parametrizations of generic classes.
`model_post_init()`	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild()`	Try to rebuild the pydantic-core schema for the model.
`model_validate()`	Validate a pydantic model instance.
`model_validate_json()`	Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
`model_validate_strings()`	Validate the given object contains string data against the Pydantic model.
`__get_pydantic_core_schema__()`	Hook into generating the model’s CoreSchema.
`__get_pydantic_json_schema__()`	Hook into generating the model’s JSON schema.
`__pydantic_init_subclass__()`	This is intended to behave just like __init_subclass__, but is called by ModelMetaclass
`__copy__()`	Returns a shallow copy of the model.
`__deepcopy__()`	Returns a deep copy of the model.
`__delattr__()`	Implement delattr(self, name).
`__eq__()`	Return self==value.
`__iter__()`	So dict(model) works.
`copy()`	Returns a copy of the model.

Parameters: data (Any) –

__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.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Parameters: data (Any) –
Return type: None

static generate_private_key()#

Generates a private key for the license.

Return type: str

static generate_public_key(private_key)#

Generates a public key for the license.

Parameters: private_key (str) –
Return type: str

generate_signature(private_key)#

Generates a signature for the license.

Parameters: private_key (str) –

static check_license()#: Validates the license in TUMULT_ANALYTICS_PRO_LICENSE.

verify_signature()#: Verifies the signature of the license.

property model_computed_fields#

Get the computed fields of this model instance.

Returns: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
Return type: dict[str, pydantic.fields.ComputedFieldInfo]

property model_extra#

Get extra fields set during validation.

Returns: A dictionary of extra fields, or None if config.extra is not set to “allow”.
Return type: dict[str, Any] | None

property model_fields_set#

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.

Return type

set[str]

classmethod model_construct(_fields_set=None, **values)#

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

Parameters

_fields_set (set[str] | None) – The set of field names accepted for the Model instance.
values (Any) – Trusted or pre-validated data dictionary.

Returns

A new instance of the Model class with validated data.

Return type

Model

model_copy(*, update=None, deep=False)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters

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

Returns

New model instance.

Return type

Model

model_dump(*, mode='python', include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

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

Parameters

mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.
include (IncEx) – A list of fields to include in the output.
exclude (IncEx) – A list of fields to exclude from the output.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value from the output.
exclude_none (bool) – Whether to exclude fields that have a value of None from the output.
round_trip (bool) – Whether to enable serialization and deserialization round-trip support.
warnings (bool) – Whether to log warnings when invalid fields are encountered.

Returns

A dictionary representation of the model.

Return type

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

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

Parameters

indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (IncEx) – Field(s) to include in the JSON output. Can take either a string or set of strings.
exclude (IncEx) – Field(s) to exclude from the JSON output. Can take either a string or set of strings.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that have the default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – Whether to use serialization/deserialization between JSON and class instance.
warnings (bool) – Whether to show any warnings that occurred during serialization.

Returns

A JSON string representation of the model.

Return type

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=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[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns

The JSON schema for the given model class.

Return type

dict[str, Any]

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], Ellipsis]) – 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.
Return type: str

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.

Parameters: __context (Any) –
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 (dict[str, Any] | None) – 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.

Return type

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)#

Validate a pydantic model instance.

Parameters

obj (Any) – The object to validate.
strict (bool | None) – Whether to raise an exception on invalid fields.
from_attributes (bool | None) – Whether to extract data from object attributes.
context (dict[str, Any] | None) – Additional context to pass to the validator.

Raises

ValidationError – If the object could not be validated.

Returns

The validated model instance.

Return type

Model

classmethod model_validate_json(json_data, *, strict=None, context=None)#

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters

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

Returns

The validated Pydantic model.

Raises

ValueError – If json_data is not a JSON string.

Return type

Model

classmethod model_validate_strings(obj, *, strict=None, context=None)#

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

Parameters

obj (Any) – The object contains string data to validate.
strict (bool | None) – Whether to enforce types strictly.
context (dict[str, Any] | None) – Extra variables to pass to the validator.

Returns

The validated Pydantic model.

Return type

Model

classmethod __get_pydantic_core_schema__(__source, __handler)#

Hook into generating the model’s CoreSchema.

Parameters

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

Returns

A pydantic-core CoreSchema.

Return type

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(__core_schema, __handler)#

Hook into generating the model’s JSON schema.

Parameters

__core_schema (pydantic_core.CoreSchema) – 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 (pydantic.annotated_handlers.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.

Returns

A JSON schema, as a Python object.

Return type

pydantic.json_schema.JsonSchemaValue

classmethod __pydantic_init_subclass__(**kwargs)#

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 keyword arguments passed to the class definition that aren’t used internally by pydantic.
kwargs (Any) –

Return type

None

__copy__()#

Returns a shallow copy of the model.

Return type: Model

__deepcopy__(memo=None)#

Returns a deep copy of the model.

Parameters: memo (dict[int, Any] | None) –
Return type: Model

__delattr__(item)#

Implement delattr(self, name).

Parameters: item (str) –
Return type: Any

__eq__(other)#

Return self==value.

Parameters: other (Any) –
Return type: bool

__iter__()#

So dict(model) works.

Return type: TupleGenerator

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:

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

Parameters

include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (bool) – 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.

Return type

Model

Exceptions#

exception LicenseException(message)#

Bases: Exception

Exception raised for errors in the license.

Tumult Analytics Pro

_license#

Classes#

Exceptions#