code: def test_multiple_jobs(tmpdir: py.path.local, n_jobs: int) -> None:
      tracking_uri = f"file:{tmpdir}"
      study_name = "my_study"
      # The race-condition usually happens after first trial for each job.
      n_trials = n_jobs * 2
  
      mlflc = MLflowCallback(tracking_uri=tracking_uri)
      study = optuna.create_study(study_name=study_name)
      study.optimize(
          _objective_func,
          n_trials=n_trials,
          callbacks=[mlflc],
          n_jobs=n_jobs,
      )
  
      mlfl_client = MlflowClient(tracking_uri)
      experiments = mlfl_client.search_experiments()
      assert len(experiments) == 1
  
      experiment_id = experiments[0].experiment_id
      runs = mlfl_client.search_runs([experiment_id])
      assert len(runs) == n_trials
  
imports: from typing import Callable
  from typing import List
  from typing import Optional
  from typing import Tuple
  from typing import Union
  import numpy
  import py
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration.mlflow import MLflowCallback
label_desc: Use image node_id 12 for calling a global function with example usage: test_multiple_jobs(tmpdir, n_jobs) without return types