code: def test_fmri_inputs_pandas_errors():
      # test wrong input for list and pandas requirements
      nii_img = ["01", "02", "03"]
      with pytest.raises(ValueError, match="File not found: "):
          SecondLevelModel().fit(nii_img)
  
      nii_series = pd.Series(nii_img)
      with pytest.raises(ValueError, match="File not found: "):
          SecondLevelModel().fit(nii_series)
  
      # test dataframe requirements
      dfcols = [
          "not_the_right_column_name",
      ]
      dfrows = [["01"], ["02"], ["03"]]
      niidf = pd.DataFrame(dfrows, columns=dfcols)
      with pytest.raises(
          ValueError,
          match=(
              "second_level_input DataFrame must have "
              "columns subject_label, map_name and effects_map_path."
          ),
      ):
          SecondLevelModel().fit(niidf)
  
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 30 for calling a global function with example usage: test_fmri_inputs_pandas_errors() without return types