code: def _make_flm(data_dir):  # pragma: no cover
      task_label = "stopsignal"
      space_label = "MNI152NLin2009cAsym"
      derivatives_folder = "derivatives/fmriprep"
      (
          models,
          models_run_imgs,
          models_events,
          models_confounds,
      ) = first_level_from_bids(
          data_dir,
          task_label,
          space_label,
          smoothing_fwhm=5.0,
          derivatives_folder=derivatives_folder,
      )
  
      model, imgs, _, _ = (
          models[0],
          models_run_imgs[0],
          models_events[0],
          models_confounds[0],
      )
      subject = f"sub-{model.subject_label}"
      design_matrix = _make_design_matrix_for_bids_feature(
          data_dir, subject
      )
      model.fit(imgs, design_matrices=[design_matrix])
      return model, subject
  
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 3 for calling a global function with example usage: _make_flm(data_dir) and returns: model,  subject