def METHOD_NAME(make_dummy_ta: Callable[..., TargetFunctionRunner]) -> None:
    """Make sure because there are 2 workers, the trials are launched close in time"""
    single_runner = make_dummy_ta(target_delayed, n_workers=2)
    runner = DaskParallelRunner(single_worker=single_runner)
    assert runner.count_available_workers() == 2
    trial_info = TrialInfo(config=2, instance="test", seed=0, budget=0.0)
    runner.submit_trial(trial_info)
    assert runner.count_available_workers() == 1
    trial_info = TrialInfo(config=3, instance="test", seed=0, budget=0.0)
    runner.submit_trial(trial_info)
    assert runner.count_available_workers() == 0
    # At this stage, we submitted 2 jobs, that are running in remote workers.
    # We have to wait for each one of them to complete. The runner provides a
    # wait() method to do so, yet it can only wait for a single job to be completed.
    # It does internally via dask wait(<list of futures>) so we take wait for the
    # first job to complete, and take it out
    runner.wait()
    first = next(runner.iter_results(), None)
    # The iter results could have freed up two so we only check it's one or greater
    assert first is not None
    assert runner.count_available_workers() >= 1
    # Again, the runs might take slightly different time depending if we have
    # heterogeneous workers, so calling wait 2 times is a guarantee that 2
    # jobs are finished
    runner.wait()
    second = next(runner.iter_results(), None)
    assert second is not None
    assert runner.count_available_workers() == 2
    # To make it is parallel, we just make sure that the start of the second run is
    # earlier than the end of the first run
    _, first_run_value = first
    _, second_run_value = second
    assert int(first_run_value.starttime) <= int(second_run_value.endtime)