HclsResourceResponse

class openapi_client.models.hcls_resource_response.HclsResourceResponse(**data)

Bases: BaseModel

resource_type: StrictStr

type: Optional[StrictStr]

entry: Optional[List[Dict[str, Any]]]

additional_properties: Dict[str, Any]

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 HclsResourceResponse 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.

• Fields in self.additional_properties are added to the output dict.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of HclsResourceResponse from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'additional_properties': FieldInfo(annotation=Dict[str, Any], required=False, default={}), 'entry': FieldInfo(annotation=Union[List[Dict[str, Any]], NoneType], required=False), 'resource_type': FieldInfo(annotation=str, required=True, alias='resourceType', alias_priority=2, metadata=[Strict(strict=True)]), 'type': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False)}

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