code: def test_inputs(tmp_path, image_type):
      """Test multiple images as input."""
      # generate files
      files = []
      for i in range(2):  # gifti edge case
          nii, _ = create_tmp_filepath(
              tmp_path,
              bids_fields={
                  "entities": {
                      "sub": f"test{i + 1}",
                      "ses": "test",
                      "task": "testimg",
                      "run": "01",
                  }
              },
              image_type=image_type,
              copy_confounds=True,
              copy_json=True,
          )
          files.append(nii)
  
      if image_type == "ica_aroma":
          conf, _ = load_confounds(files, strategy=("ica_aroma",))
      else:
          conf, _ = load_confounds(files)
      assert len(conf) == 2
  
imports: import re
  import numpy
  import pandas
  import pytest
  from nibabel import Nifti1Image
  from scipy.stats import pearsonr
  from sklearn.preprocessing import scale
  from nilearn._utils.data_gen import create_fake_bids_dataset
  from nilearn._utils.fmriprep_confounds import to_camel_case
  from nilearn.conftest import _rng
  from nilearn.interfaces.bids import get_bids_files
  from nilearn.interfaces.fmriprep import load_confounds
  from nilearn.interfaces.fmriprep.load_confounds import _check_strategy, _load_single_confounds_file
  from nilearn.interfaces.fmriprep.tests._testing import create_tmp_filepath, get_legal_confound
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 24 for calling a global function with example usage: test_inputs(tmp_path, image_type) without return types