code: def _cross_validation_for_one_voxel(
      clf, vid, num_folds, subject_data, labels
  ):
      """Score classifier on data using cross validation."""
      # no shuffling in cv
      skf = model_selection.StratifiedKFold(
          n_splits=num_folds, shuffle=False
      )
      scores = model_selection.cross_val_score(
          clf, subject_data, y=labels, cv=skf, n_jobs=1
      )
      logger.debug("cross validation for voxel %d is done" % vid)
      return (vid, scores.mean())
  
imports: import numpy
  import time
  from mpi4py import MPI
  from scipy.stats.mstats import zscore
  from sklearn import model_selection
  import sklearn
  from .None import fcma_extension
  from .None import cython_blas
  from ..utils.utils import usable_cpu_count
  import logging
  import multiprocessing
label_desc: Use image node_id 1 for calling a global function with example usage: _cross_validation_for_one_voxel(clf, vid, num_folds, subject_data, labels) and returns: vid,  scores