code: def start_actor(num_actors, num_actors_per_nodes, job):
      resources = {"node": floor(1.0 / num_actors_per_nodes)}
      submission_cost, actors = measure(
          lambda: [
              SimpleActor.options(
                  resources=resources, num_cpus=0
              ).remote(job)
              for _ in range(num_actors)
          ]
      )
      ready_cost, _ = measure(
          lambda: ray.get([actor.ready.remote() for actor in actors])
      )
      actor_job_cost, _ = measure(
          lambda: ray.get([actor.do_job.remote() for actor in actors])
      )
      return (submission_cost, ready_cost, actor_job_cost)
  
file_path: ray/release/benchmarks/distributed/test_scheduling.py
imports: import ray
  import argparse
  from time import time, sleep
  from math import floor
  from ray._private.test_utils import safe_write_to_results_json
  import ray._private.test_utils
label_desc: Use image node_id 4 for calling a global function with example usage: start_actor(num_actors, num_actors_per_nodes, job) and returns: submission_cost,  ready_cost,  actor_job_cost