DatalabCreateInput

class openapi_client.models.datalab_create_input.DatalabCreateInput(**data)[source]

Bases: BaseModel

datalab_name: Optional[StrictStr]
datalab_type: Optional[StrictStr]
description: Optional[StrictStr]
compute_metadata: Optional[DatalabCreateInputComputeMetadata]
resource_access_metadata: Optional[DatalabCreateInputResourceAccessMetadata]
additional_metadata: Optional[DatalabCreateInputAdditionalMetadata]
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()[source]

Returns the string representation of the model using alias

Return type:

str

to_json()[source]

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)[source]

Create an instance of DatalabCreateInput from a JSON string

Return type:

Optional[Self]

to_dict()[source]

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)[source]

Create an instance of DatalabCreateInput from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'additional_metadata': FieldInfo(annotation=Union[DatalabCreateInputAdditionalMetadata, NoneType], required=False, alias='AdditionalMetadata', alias_priority=2), 'compute_metadata': FieldInfo(annotation=Union[DatalabCreateInputComputeMetadata, NoneType], required=False, alias='ComputeMetadata', alias_priority=2), 'datalab_name': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DatalabName', alias_priority=2), 'datalab_type': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DatalabType', alias_priority=2), 'description': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='Description', alias_priority=2), 'resource_access_metadata': FieldInfo(annotation=Union[DatalabCreateInputResourceAccessMetadata, NoneType], required=False, alias='ResourceAccessMetadata', 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