DatasourceFlowsUpdate

class openapi_client.models.datasource_flows_update.DatasourceFlowsUpdate(**data)

Bases: BaseModel

dataflow_name: Optional[StrictStr]

process_type: Optional[StrictStr]

cdc_tracking_method: Optional[StrictStr]

target_location: Optional[StrictStr]

dataflow_type: Optional[StrictStr]

create_dataset: Optional[StrictBool]

dataset_details: Optional[List[DatasourceFlowsUpdateDatasetDetailsInner]]

data_format: Optional[StrictStr]

item_configs: Optional[List[DatasourceFlowsUpdateItemConfigsInner]]

dataflow_config: Optional[DatasourceFlowsUpdateDataflowConfig]

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 DatasourceFlowsUpdate 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 DatasourceFlowsUpdate from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'cdc_tracking_method': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='CDCTrackingMethod', alias_priority=2), 'create_dataset': FieldInfo(annotation=Union[Annotated[bool, Strict(strict=True)], NoneType], required=False, alias='CreateDataset', alias_priority=2), 'data_format': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataFormat', alias_priority=2), 'dataflow_config': FieldInfo(annotation=Union[DatasourceFlowsUpdateDataflowConfig, NoneType], required=False, alias='DataflowConfig', alias_priority=2), 'dataflow_name': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataflowName', alias_priority=2), 'dataflow_type': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DataflowType', alias_priority=2), 'dataset_details': FieldInfo(annotation=Union[List[DatasourceFlowsUpdateDatasetDetailsInner], NoneType], required=False, alias='DatasetDetails', alias_priority=2), 'item_configs': FieldInfo(annotation=Union[List[DatasourceFlowsUpdateItemConfigsInner], NoneType], required=False, alias='ItemConfigs', alias_priority=2, description='Configuration for ArcGIS items to be updated'), 'process_type': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='ProcessType', alias_priority=2), 'target_location': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='TargetLocation', 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