code: def test_second_lvl_dataframe_computation(tmp_path):
      """Check that contrast can be computed when using dataframes as input.
  
      See bug https://github.com/nilearn/nilearn/issues/3871
      """
      shape = (7, 8, 9, 1)
      file_path = write_fake_bold_img(
          file_path=tmp_path / "img.nii.gz", shape=shape
      )
  
      dfcols = ["subject_label", "map_name", "effects_map_path"]
      dfrows = [
          ["01", "a", file_path],
          ["02", "a", file_path],
          ["03", "a", file_path],
      ]
      niidf = pd.DataFrame(dfrows, columns=dfcols)
  
      model = SecondLevelModel().fit(niidf)
      model.compute_contrast(first_level_contrast="a")
  
imports: import os
  from pathlib import Path
  import numpy
  import pandas
  import pytest
  from nibabel import Nifti1Image, load
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from scipy import stats
  from nilearn._utils import testing
  from nilearn._utils.data_gen import generate_fake_fmri_data_and_design, write_fake_bold_img, write_fake_fmri_data_and_design
  from nilearn.glm.first_level import FirstLevelModel, run_glm
  from nilearn.glm.second_level import SecondLevelModel, non_parametric_inference
  from nilearn.glm.second_level.second_level import _check_confounds, _check_design_matrix, _check_effect_maps, _check_first_level_contrast, _check_input_as_first_level_model, _check_output_type, _check_second_level_input, _infer_effect_maps, _process_second_level_input_as_dataframe, _process_second_level_input_as_firstlevelmodels, _sort_input_dataframe
  from nilearn.image import concat_imgs, get_data, new_img_like, smooth_img
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 50 for calling a global function with example usage: test_second_lvl_dataframe_computation(tmp_path) without return types