def METHOD_NAME(args):
    log.setLevel(logging.DEBUG)
    service = figure_out_service_name(args)
    api = client.get_paasta_oapi_client(cluster=args.cluster, http_res=True)
    if not api:
        print("Could not connect to paasta api. Maybe you misspelled the cluster?")
        return 1
    try:
        if args.set is None:
            log.debug("Getting the current autoscaler count...")
            res, status, _ = api.autoscaler.get_autoscaler_count(
                service=service, instance=args.instance, _return_http_data_only=False
            )
        else:
            log.debug(f"Setting desired instances to {args.set}.")
            msg = paastamodels.AutoscalerCountMsg(desired_instances=int(args.set))
            res, status, _ = api.autoscaler.update_autoscaler_count(
                service=service,
                instance=args.instance,
                autoscaler_count_msg=msg,
                _return_http_data_only=False,
            )
            _log_audit(
                action="manual-scale",
                action_details=str(msg),
                service=service,
                instance=args.instance,
                cluster=args.cluster,
            )
    except api.api_error as exc:
        status = exc.status
    if not 200 <= status <= 299:
        print(
            PaastaColors.red(
                f"ERROR: '{args.instance}' is not configured to autoscale, "
                f"so paasta autoscale could not scale it up on demand. "
                f"If you want to be able to boost this service, please configure autoscaling for the service "
                f"in its config file by setting min and max instances. Example: \n"
                f"{args.instance}:\n"
                f"     min_instances: 5\n"
                f"     max_instances: 50"
            )
        )
        return 0
    log.debug(f"Res: {res} Http: {status}")
    print(res.desired_instances)
    return 0