code: def report_flm_fiac():  # pragma: no cover # noqa
      data = datasets.func.fetch_fiac_first_level()
      fmri_img = [data["func1"], data["func2"]]
  
      from nilearn.image import mean_img
  
      mean_img_ = mean_img(fmri_img[0])
  
      design_files = [data["design_matrix1"], data["design_matrix2"]]
      design_matrices = [
          pd.DataFrame(np.load(df)["X"]) for df in design_files
      ]
  
      fmri_glm = FirstLevelModel(
          mask_img=data["mask"], minimize_memory=True
      )
      fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
  
      n_columns = design_matrices[0].shape[1]
  
      contrasts = {
          "SStSSp_minus_DStDSp": _pad_vector([1, 0, 0, -1], n_columns),
          "DStDSp_minus_SStSSp": _pad_vector([-1, 0, 0, 1], n_columns),
          "DSt_minus_SSt": _pad_vector([-1, -1, 1, 1], n_columns),
          "DSp_minus_SSp": _pad_vector([-1, 1, -1, 1], n_columns),
          "DSt_minus_SSt_for_DSp": _pad_vector(
              [0, -1, 0, 1], n_columns
          ),
          "DSp_minus_SSp_for_DSt": _pad_vector(
              [0, 0, -1, 1], n_columns
          ),
          "Deactivation": _pad_vector([-1, -1, -1, -1, 4], n_columns),
          "Effects_of_interest": np.eye(n_columns)[:5],
      }
      report = make_glm_report(
          fmri_glm,
          contrasts,
          bg_img=mean_img_,
          height_control="fdr",
      )
      output_filename = "generated_report_flm_fiac.html"
      output_filepath = os.path.join(REPORTS_DIR, output_filename)
      report.save_as_html(output_filepath)
      report.get_iframe()
  
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 7 for calling a global function with example usage: report_flm_fiac() without return types