code: def test_check_parameters_transform(test_image_2, rng):
      # single confound
      confounds = rng.standard_normal(size=(10, 3))
      # Tests to check whether imgs, confounds returned are
      # list or not. Pre-check in parameters to work for list
      # of multi images and multi confounds
      imgs, confounds, single_subject = _check_parameters_transform(
          test_image_2, confounds
      )
  
      assert isinstance(imgs, (list, tuple))
      assert isinstance(confounds, (list, tuple))
      assert single_subject, True
  
      # confounds as pandas DataFrame
      imgs, confounds, single_subject = _check_parameters_transform(
          test_image_2, pd.DataFrame(np.array(confounds)[0])
      )
  
      assert isinstance(confounds, (list, tuple))
  
      # multi images
      fmri_imgs = [test_image_2] * 3
      confounds_list = [confounds] * 3
      imgs, confounds, _ = _check_parameters_transform(
          fmri_imgs, confounds_list
      )
  
      assert imgs == fmri_imgs
      assert confounds_list == confounds
  
      # Test the error when length of images and confounds are not same
      msg = (
          "Number of confounds given does not match with the "
          "given number of images"
      )
      not_match_confounds_list = [confounds] * 2
      with pytest.raises(ValueError, match=msg):
          _check_parameters_transform(
              fmri_imgs, not_match_confounds_list
          )
  
imports: import warnings
  import numpy
  import pandas
  import pytest
  from nibabel import Nifti1Image
  from nilearn.conftest import _affine_eye
  from nilearn.regions.parcellations import Parcellations, _check_parameters_transform
label_desc: Use image node_id 12 for calling a global function with example usage: test_check_parameters_transform(test_image_2, rng) without return types