code: def _score(
      self, x: Union[np.ndarray, list]
  ) -> Tuple[
      float,
      float,
      np.ndarray,
      np.ndarray,
      Union[np.ndarray, list],
      Union[np.ndarray, list],
  ]:
      x_ref, x = self.preprocess(x)
      x, y, splits = self.get_splits(x_ref, x, return_splits=True)  # type: ignore
  
      # iterate over folds: train a new model for each fold and make out-of-fold (oof) predictions
      probs_oof_list, idx_oof_list = [], []
      for idx_tr, idx_te in splits:
          y_tr = y[idx_tr]
          if isinstance(x, np.ndarray):
              x_tr, x_te = x[idx_tr], x[idx_te]
          elif isinstance(x, list):
              x_tr, x_te = [x[_] for _ in idx_tr], [
                  x[_] for _ in idx_te
              ]
          else:
              raise TypeError(
                  f"x needs to be of type np.ndarray or list and not {type(x)}."
              )
          self.model.fit(x_tr, y_tr)
          probs = self.model.aux_predict_proba(x_te)
          probs_oof_list.append(probs)
          idx_oof_list.append(idx_te)
      probs_oof = np.concatenate(probs_oof_list, axis=0)
      idx_oof = np.concatenate(idx_oof_list, axis=0)
      y_oof = y[idx_oof]
      n_cur = y_oof.sum()
      n_ref = len(y_oof) - n_cur
      p_val, dist = self.test_probs(y_oof, probs_oof, n_ref, n_cur)
      idx_sort = np.argsort(idx_oof)
      probs_sort = probs_oof[idx_sort]
      if isinstance(x, np.ndarray):
          x_oof = x[idx_oof]
          x_sort = x_oof[idx_sort]
      else:
          x_oof = [x[_] for _ in idx_oof]
          x_sort = [x_oof[_] for _ in idx_sort]
      return (
          p_val,
          dist,
          probs_sort[:n_ref, 1],
          probs_sort[n_ref:, 1],
          x_sort[:n_ref],
          x_sort[n_ref:],
      )
  
file_path: alibi-detect/alibi_detect/cd/sklearn/classifier.py
imports: import logging
  import numpy
  from functools import partial
  from typing import Callable, Dict, Optional, Tuple, Union
  from sklearn.base import clone, ClassifierMixin
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.exceptions import NotFittedError
  from sklearn.ensemble import RandomForestClassifier
  from alibi_detect.cd.base import BaseClassifierDrift
  from alibi_detect.utils.warnings import deprecated_alias
  from alibi_detect.utils.frameworks import Framework
label_desc: Use image node_id 5 for calling the ClassifierDriftSklearn obj's underlying member method code with example usage: obj._score(x) and returns: p_val,  dist,  probs_sort,  probs_sort,  x_sort,  x_sort