code: def test_parcellations_transform_with_multi_confounds_multi_images(
      method, n_parcel, test_image_2, rng
  ):
      fmri_imgs = [test_image_2] * 3
      confounds = rng.standard_normal(size=(10, 3))
      confounds_list = [confounds] * 3
  
      parcellator = Parcellations(
          method=method, n_parcels=n_parcel, verbose=0
      )
  
      parcellator.fit(fmri_imgs)
      signals = parcellator.transform(
          fmri_imgs, confounds=confounds_list
      )
  
      assert isinstance(signals, list)
      # n_parcels=5, length of data=10
      assert signals[0].shape == (10, n_parcel)
  
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 13 for calling a global function with example usage: test_parcellations_transform_with_multi_confounds_multi_images(method, n_parcel, test_image_2, rng) without return types