class_docstr: Group iterator.
  
  Provides group of features for search_light loop
  that may be used with Parallel.
  
  Parameters
  ----------
  n_features : int
      Total number of features
  %(n_jobs)s
class_docstr_tok: ["Group","iterator",".","Provides","group","of","features","for","search_light","loop","that","may","be","used","with","Parallel",".","Parameters","--","--","--","--","--","n_features",":","int","Total","number","of","features","%","(","n_jobs",")","s"]
code: def __init__(self, n_features, n_jobs=1):
      self.n_features = n_features
      if n_jobs == -1:
          n_jobs = cpu_count()
      self.n_jobs = n_jobs
  
imports: import sys
  import time
  import warnings
  import numpy
  from joblib import Parallel, cpu_count, delayed
  from sklearn import svm
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.model_selection import cross_val_score
  from nilearn.maskers.nifti_spheres_masker import _apply_mask_and_get_affinity
  from ..None import masking
  from .._utils import check_niimg_4d, fill_doc
  from ..image.resampling import coord_transform
label_desc: Use image node_id 1 to create a new GroupIterator object  with example: obj = GroupIterator(n_features, n_jobs)