code: def get_out_of_folds(self):
      if self.oof_predictions is not None:
          return self.oof_predictions.copy(deep=True)
  
      if self._oof_predictions_fname is not None:
          self.oof_predictions = pd.read_csv(
              self._oof_predictions_fname
          )
          return self.oof_predictions.copy(deep=True)
  
      early_stopping = self.callbacks.get("early_stopping")
      if early_stopping is None:
          return None
      self.oof_predictions = early_stopping.best_y_oof
  
      ###############################################################
      # in case of one-hot coded multiclass target
      target_cols = [
          c
          for c in self.oof_predictions.columns.tolist()
          if "target" in c
      ]
      if len(target_cols) > 1:
          target = self.oof_predictions[target_cols[0]].copy()
          target.name = "target"
          for i, t in enumerate(target_cols):
              target[self.oof_predictions[t] == 1] = i
          self.oof_predictions.drop(target_cols, axis=1, inplace=True)
  
          self.oof_predictions.insert(0, "target", np.array(target))
  
      return early_stopping.best_y_oof
  
imports: import copy
  import gc
  import json
  import logging
  import os
  import sys
  import time
  import uuid
  import zipfile
  import numpy
  import pandas
  from supervised.algorithms.factory import AlgorithmFactory
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.callbacks.callback_list import CallbackList
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.preprocessing import Preprocessing
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.validation.validation_step import ValidationStep
  from supervised.utils.jsonencoder import MLJSONEncoder
  import joblib
  import optuna
  from supervised.tuner.optuna.tuner import OptunaTuner
  from supervised.utils.config import mem
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 8 for calling the ModelFramework obj's underlying member method code with example usage: obj.get_out_of_folds() and returns: early_stopping, self, self, None