code: def prefer_parallel_execution(  # noqa
      functions_to_be_called,
  ):  # pragma: no cover
      try:
          import multiprocessing
  
          import joblib
      except ImportError:
          print("Joblib not installed, switching to serial execution")
          [run_function(fn) for fn in functions_to_be_called]
      else:
          try:
              import tqdm
          except ImportError:
              inputs = functions_to_be_called
          else:
              inputs = tqdm.tqdm(functions_to_be_called)
          n_jobs = multiprocessing.cpu_count()
          print("Parallelizing execution using Joblib")
          joblib.Parallel(n_jobs=n_jobs)(
              joblib.delayed(run_function)(fn) for fn in inputs
          )
  
imports: import os
  import numpy
  import pandas
  import nilearn
  from nilearn import datasets
  from nilearn.glm.first_level import FirstLevelModel, first_level_from_bids
  from nilearn.glm.first_level.design_matrix import make_first_level_design_matrix
  from nilearn.glm.second_level import SecondLevelModel
  from nilearn.image import resample_to_img
  from nilearn.interfaces.fsl import get_design_from_fslmat
  from nilearn.maskers import NiftiSpheresMasker
  from nilearn.reporting import make_glm_report
label_desc: Use image node_id 10 for calling a global function with example usage: prefer_parallel_execution(functions_to_be_called) without return types