put_ml_configuration ¶

def put_ml_configuration(
    self,
    membership_identifier: "capo_cleanroomsml.types.uuid.UUID",
    default_output_location: "capo_cleanroomsml.types.ml_output_configuration.MLOutputConfiguration",
    *,
    config_overrides: Optional[CleanRoomsMLClientConfig] = None,
) -> None:
    """<p>Assigns information about an ML configuration.</p>

    Args:
        membership_identifier: <p>The membership ID of the member that is being configured.</p>
        default_output_location: <p>The default Amazon S3 location where ML output is stored for the specified member.</p>

    Raises:
        capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
        capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_cleanroomsml.types.put_ml_configuration_request.PutMLConfigurationRequest]",
    ) -> OperationResponse[None]:
        import capo_cleanroomsml._operations.aws_stark_control_service.put_ml_configuration

        output, http_response = (
            capo_cleanroomsml._operations.aws_stark_control_service.put_ml_configuration.put_ml_configuration(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("PutMLConfiguration")
    )
    input_: capo_cleanroomsml.types.put_ml_configuration_request.PutMLConfigurationRequest = {
        "membership_identifier": membership_identifier,
        "default_output_location": default_output_location,
    }

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