code: def split(self, X, y):
      if isinstance(self._splitter, RepeatedStratifiedKFold):
          # FIXME: There is a bug in sklearn that causes an incorrect ValueError if performing stratification and all classes have fewer than n_splits samples.
          #  This is hacked by adding a dummy class with n_splits samples, performing the kfold split, then removing the dummy samples from all resulting indices.
          #  This is very inefficient and complicated and ideally should be fixed in sklearn.
          with warning_filter():
              try:
                  out = [
                      [train_index, test_index]
                      for train_index, test_index in self._splitter.split(
                          X, y
                      )
                  ]
              except:
                  y_dummy = pd.concat(
                      [y, pd.Series([-1] * self.n_splits)],
                      ignore_index=True,
                  )
                  X_dummy = pd.concat(
                      [X, X.head(self.n_splits)], ignore_index=True
                  )
                  invalid_index = set(
                      list(y_dummy.tail(self.n_splits).index)
                  )
                  out = [
                      [train_index, test_index]
                      for train_index, test_index in self._splitter.split(
                          X_dummy, y_dummy
                      )
                  ]
                  len_out = len(out)
                  for i in range(len_out):
                      train_index, test_index = out[i]
                      out[i][0] = [
                          index
                          for index in train_index
                          if index not in invalid_index
                      ]
                      out[i][1] = [
                          index
                          for index in test_index
                          if index not in invalid_index
                      ]
          return out
      else:
          return [
              [train_index, test_index]
              for train_index, test_index in self._splitter.split(
                  X, y, groups=self.groups
              )
          ]
  
imports: import logging
  import math
  import pickle
  import random
  import sys
  import time
  from typing import Callable, List, Tuple, Union
  import numpy
  import pandas
  import scipy.stats
  from numpy.typing import ArrayLike
  from pandas import DataFrame, Series
  from sklearn.model_selection import LeaveOneGroupOut, RepeatedKFold, RepeatedStratifiedKFold, train_test_split
  from autogluon.common.utils.resource_utils import ResourceManager
  from ..constants import BINARY, LARGE_DATA_THRESHOLD, MULTICLASS, MULTICLASS_UPPER_LIMIT, QUANTILE, REGRESS_THRESHOLD_LARGE_DATA, REGRESS_THRESHOLD_SMALL_DATA, REGRESSION, SOFTCLASS
  from ..metrics import Scorer, accuracy, pinball_loss, root_mean_squared_error
  from .miscs import warning_filter
label_desc: Use image node_id 4 for calling the CVSplitter obj's underlying member method code with example usage: obj.split(X, y) and returns: out, unknown