DatalabCreateInputAdditionalMetadata

class openapi_client.models.datalab_create_input_additional_metadata.DatalabCreateInputAdditionalMetadata(**data)

Bases: BaseModel

keywords: Optional[List[StrictStr]]

cost_tags: Optional[List[FileAddDeleteFileLevelTagsInner]]

is_sessions_enabled: Optional[StrictBool]

is_auto_terminate_enabled: Optional[StrictBool]

auto_termination_time: Optional[StrictStr]

lifecycle_configuration: Optional[StrictStr]

r_studio_access_status: Optional[StrictStr]

studio_advanced_config: Optional[DatalabCreateInputAdditionalMetadataStudioAdvancedConfig]

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True}

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

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of DatalabCreateInputAdditionalMetadata from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of DatalabCreateInputAdditionalMetadata from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'auto_termination_time': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='AutoTerminationTime', alias_priority=2), 'cost_tags': FieldInfo(annotation=Union[List[FileAddDeleteFileLevelTagsInner], NoneType], required=False, alias='CostTags', alias_priority=2), 'is_auto_terminate_enabled': FieldInfo(annotation=Union[Annotated[bool, Strict(strict=True)], NoneType], required=False, alias='IsAutoTerminateEnabled', alias_priority=2), 'is_sessions_enabled': FieldInfo(annotation=Union[Annotated[bool, Strict(strict=True)], NoneType], required=False, alias='IsSessionsEnabled', alias_priority=2), 'keywords': FieldInfo(annotation=Union[List[Annotated[str, Strict(strict=True)]], NoneType], required=False, alias='Keywords', alias_priority=2), 'lifecycle_configuration': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='LifecycleConfiguration', alias_priority=2), 'r_studio_access_status': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='RStudioAccessStatus', alias_priority=2), 'studio_advanced_config': FieldInfo(annotation=Union[DatalabCreateInputAdditionalMetadataStudioAdvancedConfig, NoneType], required=False, alias='StudioAdvancedConfig', alias_priority=2)}

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

This replaces Model.__fields__ from Pydantic V1.

model_post_init(__context)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• __context (Any) – The context.

Return type:

None