code: def cli_start_worker(scheduler_file_name):
      command = (
          "DASK_DISTRIBUTED__WORKER__DAEMON=False "
          "dask-worker --nthreads 1 --lifetime 35 --memory-limit 0 "
          f"--scheduler-file {scheduler_file_name}"
      )
      proc = subprocess.run(
          command,
          stdout=subprocess.PIPE,
          stderr=subprocess.STDOUT,
          shell=True,
      )
      while proc.returncode is None:
          time.sleep(1)
  
file_path: auto-sklearn/examples/60_search/example_parallel_manual_spawning_cli.py
imports: import multiprocessing
  import subprocess
  import time
  import dask.distributed
  import sklearn.datasets
  import sklearn.metrics
  from autosklearn.classification import AutoSklearnClassifier
  from autosklearn.constants import MULTICLASS_CLASSIFICATION
label_desc: Use image node_id 2 for calling a global function with example usage: cli_start_worker(scheduler_file_name) without return types