class_docstr: Leave-One-Label_Out cross-validation iterator:
  Provides train/test indexes to split data in train test sets
code: def __iter__(self):
      # We make a copy here to avoid side-effects during iteration
      labels = np.array(self.labels, copy=True)
      for i in np.unique(labels):
          test_index = np.zeros(len(labels), dtype=bool)
          test_index[labels == i] = 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 LeaveOneLabelOut obj's underlying member method code with example usage: obj.__iter__() without return types