code: def submit_batch_jobs(
      clients: List[JobSubmissionClient],
      num_jobs: int,
      timeout_s: int = 10 * 60,
      retry_interval_s: int = 1,
  ) -> bool:
      job_ids = []
      for i in range(num_jobs):
          # Cycle through clients arbitrarily
          client = clients[i % len(clients)]
          job_id = client.submit_job(
              entrypoint="echo hello",
          )
          job_ids.append(job_id)
          print(f"submitted job: {job_id}")
  
      for job_id in job_ids:
          client = clients[job_ids.index(job_id) % len(clients)]
          status = wait_until_finish(
              client, job_id, timeout_s, retry_interval_s
          )
          if status != JobStatus.SUCCEEDED:
              print(
                  f"Info for failed/timed-out job {job_id}: {client.get_job_info(job_id)}"
              )
              print(
                  f"Logs for failed/timed-out job {job_id}: {client.get_job_logs(job_id)}"
              )
              print(
                  f"Job {job_id} failed with status {status} (`None` indicates timeout)"
              )
              return False
      return True
  
file_path: ray/release/long_running_tests/workloads/long_running_many_jobs.py
imports: import argparse
  import os
  import time
  import random
  from typing import List, Optional
  from ray.dashboard.modules.job.common import JobStatus
  from ray.dashboard.modules.job.pydantic_models import JobDetails
  import ray
  from ray.job_submission import JobSubmissionClient
  from ray._private.test_utils import safe_write_to_results_json
label_desc: Use image node_id 2 for calling a global function with example usage: submit_batch_jobs(clients, num_jobs, timeout_s, retry_interval_s) and returns: True, False