class_docstr: K-Folds cross validation iterator:
  Provides train/test indexes to split data in train test sets
code: def __iter__(self):
      n = self.n
      k = self.k
      j = int(np.ceil(n / k))
  
      for i in range(k):
          test_index = np.zeros(n, dtype=bool)
          if i < k - 1:
              test_index[i * j : (i + 1) * j] = True
          else:
              test_index[i * j :] = True
          train_index = np.logical_not(test_index)
          yield train_index, test_index
  
imports: from statsmodels.compat.python import lrange
  import numpy
  from itertools import combinations
label_desc: Use image node_id 2 for calling the KFold obj's underlying member method code with example usage: obj.__iter__() without return types