code: def test_fmri_img_inputs_errors(tmp_path, confounds):
      # prepare correct input
      shapes = (SHAPE,)
      _, FUNCFILE, _ = write_fake_fmri_data_and_design(
          shapes, file_path=tmp_path
      )
      FUNCFILE = FUNCFILE[0]
  
      # test niimgs requirements
      niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
      with pytest.raises(ValueError, match="require a design matrix"):
          SecondLevelModel().fit(niimgs)
      with pytest.raises(
          TypeError,
          match="Elements of second_level_input must be of the same type.",
      ):
          SecondLevelModel().fit(niimgs + [[]], confounds)
  
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 32 for calling a global function with example usage: test_fmri_img_inputs_errors(tmp_path, confounds) without return types