code: def report_slm_oasis():  # pragma: no cover # noqa
      n_subjects = 5  # more subjects requires more memory
      oasis_dataset = nilearn.datasets.fetch_oasis_vbm(
          n_subjects=n_subjects
      )
      # Resample the images, since this mask has a different resolution
      mask_img = resample_to_img(
          nilearn.datasets.fetch_icbm152_brain_gm_mask(),
          oasis_dataset.gray_matter_maps[0],
          interpolation="nearest",
      )
      design_matrix = _make_design_matrix_slm_oasis(
          oasis_dataset, n_subjects
      )
      second_level_model = SecondLevelModel(
          smoothing_fwhm=2.0, mask_img=mask_img
      )
      second_level_model.fit(
          oasis_dataset.gray_matter_maps, design_matrix=design_matrix
      )
  
      contrast = [[1, 0, 0], [0, 1, 0]]
      report = make_glm_report(
          model=second_level_model,
          contrasts=contrast,
          bg_img=nilearn.datasets.fetch_icbm152_2009()["t1"],
          height_control=None,
      )
      output_filename = "generated_report_slm_oasis.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 9 for calling a global function with example usage: report_slm_oasis() without return types