code: def _check_imgs_list_array(imgs):
      """
      Check input images if they are list of arrays.
      In this case returned images are a list of list of arrays
      where element i,j of the array is a numpy array of
      shape [n_voxels, n_timeframes] that contains the data of subject i
      collected during session j.
  
      Parameters
      ----------
  
      imgs : array of str, shape=[n_subjects, n_sessions]
              imgs is a list of arrays where element i of the array is
              a numpy array of shape [n_voxels, n_timeframes] that contains the
              data of subject i (number of sessions is implicitly 1)
              n_timeframes and n_voxels are assumed to be the same across
              subjects
              n_timeframes can vary across sessions
              Each voxel's timecourse is assumed to have mean 0 and variance 1
  
      Returns
      -------
      shapes: array
          Shape of input images
      new_imgs: list of list of array of shape [n_voxels, n_components]
      """
      n_subjects = len(imgs)
      n_sessions = 1
      shapes = np.zeros((n_subjects, n_sessions, 2))
      new_imgs = []
      for i in range(len(imgs)):
          assert_array_2axis(imgs[i], "imgs[%i]" % i)
          shapes[i, 0, :] = imgs[i].shape
          new_imgs.append([imgs[i]])
  
      return new_imgs, shapes
  
imports: import hashlib
  import logging
  import os
  import numpy
  import scipy
  from joblib import Parallel, delayed
  from brainiak.funcalign.srm import DetSRM
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.exceptions import NotFittedError
  import uuid
label_desc: Use image node_id 9 for calling a global function with example usage: _check_imgs_list_array(imgs) and returns: new_imgs,  shapes