code: def _compute_and_save_subject_basis(
      subject_number, sessions, temp_dir
  ):
      """computes correlation matrix for all sessions
  
      Parameters
      ----------
  
      subject_number: int
          Number that identifies the subject. Basis will be stored in
           [temp_dir]/basis_[subject_number].npy
  
      sessions : array of str
          Element i of the array is a path to the data collected during
          session i.
          Data are loaded with numpy.load and expected shape is
          [n_timeframes, n_voxels]
          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
  
      temp_dir : str or None
          path to dir where temporary results are stored
          if None temporary results will be stored in memory. This
          can results in memory errors when the number of subjects
          and / or sessions is large
  
      Returns
      -------
  
      basis: array, shape=[n_component, n_voxels] or str
          basis of subject [subject_number] or path to this basis
      """
      corr_mat = None
      for session in sessions:
          name = safe_encode(session)
          path = os.path.join(temp_dir, "corr_mat_" + name + ".npy")
          if corr_mat is None:
              corr_mat = np.load(path)
          else:
              corr_mat += np.load(path)
          os.remove(path)
      basis_i = _compute_subject_basis(corr_mat)
      path = os.path.join(temp_dir, "basis_%i" % subject_number)
      np.save(path, basis_i)
      return path + ".npy"
  
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 27 for calling a global function with example usage: _compute_and_save_subject_basis(subject_number, sessions, temp_dir) and returns: unknown