code: def _check_imgs_array(imgs):
      """Check input image if it is an array
  
      Parameters
      ----------
      imgs : array of str, shape=[n_subjects, n_sessions]
              Element i, j of the array is a path to the data of subject i
              collected during session j.
              Data are loaded with numpy.load and expected
              shape is [n_voxels, n_timeframes]
              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
      """
      assert_array_2axis(imgs, "imgs")
      n_subjects, n_sessions = imgs.shape
  
      shapes = np.zeros((n_subjects, n_sessions, 2))
      for i in range(n_subjects):
          for j in range(n_sessions):
              if not (
                  isinstance(imgs[i, j], str)
                  or isinstance(imgs[i, j], np.str_)
                  or isinstance(imgs[i, j], str)
              ):
                  raise ValueError(
                      "imgs[%i, %i] is stored using "
                      "type %s which is not a str"
                      % (i, j, type(imgs[i, j]))
                  )
              shapes[i, j, :] = get_shape(imgs[i, j])
      return 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 10 for calling a global function with example usage: _check_imgs_array(imgs) and returns: shapes