docstr: Split reference and test data in train and test folds used by the classifier.
  
  Parameters
  ----------
  x_ref
      Data used as reference distribution.
  x
      Batch of instances.
  return_splits
      Whether to return the splits.
  
  Returns
  -------
  Combined reference and test instances with labels and optionally a list with tuples of         train and test indices for optionally different folds.
docstr_tok: ["Split","reference","and","test","data","in","train","and","test","folds","used","by","the","classifier",".","Parameters","--","--","--","--","--","x_ref","Data","used","as","reference","distribution",".","x","Batch","of","instances",".","return_splits","Whether","to","return","the","splits",".","Returns","--","--","--","-","Combined","reference","and","test","instances","with","labels","and","optionally","a","list","with","tuples","of","train","and","test","indices","for","optionally","different","folds","."]
code: def get_splits(
      self,
      x_ref: Union[np.ndarray, list],
      x: Union[np.ndarray, list],
      return_splits: bool = True,
  ) -> Union[
      Tuple[Union[np.ndarray, list], np.ndarray],
      Tuple[
          Union[np.ndarray, list],
          np.ndarray,
          Optional[List[Tuple[np.ndarray, np.ndarray]]],
      ],
  ]:
      """
          Split reference and test data in train and test folds used by the classifier.
  
          Parameters
          ----------
          x_ref
              Data used as reference distribution.
          x
              Batch of instances.
          return_splits
              Whether to return the splits.
  
          Returns
          -------
          Combined reference and test instances with labels and optionally a list with tuples of \
          train and test indices for optionally different folds.
          """
      # create dataset and labels
      y = np.concatenate(
          [np.zeros(len(x_ref)), np.ones(len(x))], axis=0
      ).astype(
          np.int64
      )  # Fix #411
      if isinstance(x_ref, np.ndarray) and isinstance(x, np.ndarray):
          x = np.concatenate([x_ref, x], axis=0)
      else:  # add 2 lists
          x = x_ref + x
  
      if not return_splits:
          return x, y
  
      # random shuffle if stratified folds are not used
      n_tot = len(x)
      if self.skf is None:
          idx_shuffle = np.random.choice(
              np.arange(n_tot), size=n_tot, replace=False
          )
          n_tr = int(n_tot * self.train_size)
          idx_tr, idx_te = idx_shuffle[:n_tr], idx_shuffle[n_tr:]
          splits = [(idx_tr, idx_te)]
      else:  # use stratified folds
          splits = self.skf.split(np.zeros(n_tot), y)
      return x, y, splits
  
imports: import logging
  from abc import abstractmethod
  from typing import Callable, Dict, List, Optional, Tuple, Union, Any
  import numpy
  from alibi_detect.base import BaseDetector, concept_drift_dict, DriftConfigMixin
  from alibi_detect.cd.utils import get_input_shape, update_reference
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow
  from alibi_detect.utils.statstest import fdr
  from scipy.stats import binom_test, ks_2samp
  from sklearn.model_selection import StratifiedKFold
label_desc: Use image node_id 3 for calling the BaseClassifierDrift obj's underlying member method code with example usage: obj.get_splits(x_ref, x, return_splits) and returns: x,  y,  splits, x,  y