code: def _check_parameters_transform(imgs, confounds):
      """Check the parameters and prepare for processing as a list."""
      imgs = stringify_path(imgs)
      confounds = stringify_path(confounds)
      if not isinstance(imgs, (list, tuple)) or isinstance(imgs, str):
          imgs = [imgs]
          single_subject = True
      elif len(imgs) == 1:
          single_subject = True
      else:
          single_subject = False
  
      if confounds is None and isinstance(imgs, (list, tuple)):
          confounds = [None] * len(imgs)
  
      if confounds is not None and (
          not isinstance(confounds, (list, tuple))
          or isinstance(confounds, str)
      ):
          confounds = [confounds]
  
      if len(confounds) != len(imgs):
          raise ValueError(
              "Number of confounds given does not match with "
              "the given number of images."
          )
      return imgs, confounds, single_subject
  
imports: import warnings
  import numpy
  from joblib import Memory, Parallel, delayed
  from sklearn.base import clone
  from sklearn.feature_extraction import image
  from nilearn.maskers import NiftiLabelsMasker
  from .._utils import fill_doc, stringify_path
  from .._utils.niimg import safe_get_data
  from .._utils.niimg_conversions import iter_check_niimg
  from ..decomposition._multi_pca import _MultiPCA
  from .hierarchical_kmeans_clustering import HierarchicalKMeans
  from .rena_clustering import ReNA
label_desc: Use image node_id 2 for calling a global function with example usage: _check_parameters_transform(imgs, confounds) and returns: imgs,  confounds,  single_subject