ComponentUtil.getJobManager().findJobByUniqueOf(LaJobUnique.of(getId())).ifPresent(job -> {
            job.launchNow();
        }).orElse(() -> {
            throw new JobNotFoundException(this);
        });
    }

    public void stop() {
        ComponentUtil.getJobManager().findJobByUniqueOf(LaJobUnique.of(getId())).ifPresent(job -> {
            job.stopNow();
        }).orElse(() -> {
            throw new JobNotFoundException(this);

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  const char* first_device_name =
      "/job:localhost/replica:0/task:0/device:CPU:0";
  const char* second_device_name =
      "/job:localhost/replica:0/task:0/device:CPU:1";
  std::array<const char*, 2> underlying_devices{first_device_name,

namespace {

using ::tensorflow::string;

void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index) {
  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,

errors.docid_not_found=Not found Doc ID:{0}
errors.document_not_found=Not found URL of Doc ID:{0}
errors.not_load_from_server=Could not load from this server: {0}
errors.failed_to_start_job=Failed to start job {0}.
errors.failed_to_stop_job=Failed to stop job {0}.
errors.failed_to_download_synonym_file=Failed to download the Synonym file.
errors.failed_to_upload_synonym_file=Failed to upload the Synonym file.

- Continue with the backup job creation.  On the Summary screen, check the Run the Job when I click Finish checkbox and click the Finish button. The backup job will start immediately.  This will create an Active Full backup of the VMs within the backup job.

- Since we selected Copy mode when creating the SOBR, the backup will be copied to the capacity tier as soon as it is created on the performance tier.

        "alertname": "MinIOClusterTolerance",
        "instance": "localhost:9000",
        "job": "minio-job-node",
        "pool": "0",
        "server": "127.0.0.1:9000",
        "set": "0",
        "severity": "critical"
      },
      "annotations": {
        "description": "MinIO instance 127.0.0.1:9000 of job minio-job has tolerance <=0 for more than 5 minutes.",

          "type": "prometheus",
          "uid": "${DS_PROMETHEUS}"
        },
        "definition": "label_values(job)",
        "hide": 0,
        "includeAll": false,
        "multi": false,
        "name": "scrape_jobs",
        "options": [],
        "query": {
          "query": "label_values(job)",
          "refId": "StandardVariableQuery"
        },
        "refresh": 1,
        "regex": "",

    token: Bearer xxxxx # optional authentication token for the notification endpoint
  
  retry:
    attempts: 10 # number of retries for the job before giving up
    delay: 500ms # least amount of delay between each retry
`
	var job BatchJobRequest
	err := yaml.UnmarshalStrict([]byte(expireYaml), &job)
	if err != nil {

            resultBuf.append(e.getMessage()).append("\n");
        }

        // purge job logs
        try {
            final int days = ComponentUtil.getFessConfig().getPurgeJobLogDay();
            if (days >= 0) {
                jobLogService.deleteBefore(days);
            } else {
                resultBuf.append("Skipped to purge job logs.\n");
            }
        } catch (final Exception e) {

}

tensorflow::ServerDef GetServerDef(const string& job_name, int num_tasks) {
  tensorflow::ServerDef server_def;
  server_def.set_protocol("grpc");
  server_def.set_job_name(job_name);
  server_def.set_task_index(0);
  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->add_job();
  job_def->set_name(job_name);
  for (int i = 0; i < num_tasks; i++) {