delete_studio_lifecycle_config ¶

def delete_studio_lifecycle_config(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    studio_lifecycle_config_name: Optional[
        "capo_sagemaker.types.studio_lifecycle_config_name.StudioLifecycleConfigName"
    ] = None,
) -> None:
    """<p>Deletes the Amazon SageMaker AI Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.</p>

    Args:
        studio_lifecycle_config_name: <p>The name of the Amazon SageMaker AI Studio Lifecycle Configuration to delete.</p>

    Raises:
        capo_sagemaker.errors.resource_in_use.ResourceInUse: <p>Resource being accessed is in use.</p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.delete_studio_lifecycle_config_request.DeleteStudioLifecycleConfigRequest]",
    ) -> OperationResponse[None]:
        import capo_sagemaker._operations.sage_maker.delete_studio_lifecycle_config

        output, http_response = (
            capo_sagemaker._operations.sage_maker.delete_studio_lifecycle_config.delete_studio_lifecycle_config(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DeleteStudioLifecycleConfig")
    )
    input_: capo_sagemaker.types.delete_studio_lifecycle_config_request.DeleteStudioLifecycleConfigRequest = {}
    if studio_lifecycle_config_name is not None:
        input_["studio_lifecycle_config_name"] = studio_lifecycle_config_name

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output