class_docstr: Meta ensembling, also known as stacking. See
  http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/
  for more information
  
  Parameters
  ----------
  base_estimators: list or numpy array (n_estimators,)
      A list of base classifiers.
  
  meta_clf : object, optional (default=LogisticRegression)
      The meta classifier to make the final prediction.
  
  n_folds : int, optional (default=2)
      The number of splits of the training sample.
  
  keep_original : bool, optional (default=False)
      If True, keep the original features for training and predicting.
  
  use_proba : bool, optional (default=False)
      If True, use the probability prediction as the new features.
  
  shuffle_data : bool, optional (default=False)
      If True, shuffle the input data.
  
  random_state : int, RandomState or None, optional (default=None)
      If int, random_state is the seed used by the random
      number generator; If RandomState instance, random_state is the random
      number generator; If None, the random number generator is the
      RandomState instance used by `np.random`.
  
  threshold : float in (0, 1), optional (default=None)
      Cut-off value to convert scores into binary labels.
  
  pre_fitted : bool, optional (default=False)
      Whether the base classifiers are trained. If True, `fit`
      process may be skipped.
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docstr: Fit estimator and predict on X
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  y : numpy array of shape (n_samples,), optional (default=None)
      The ground truth of the input samples (labels).
  
  Returns
  -------
  labels : numpy array of shape (n_samples,)
      Class labels for each data sample.
docstr_tok: ["Fit","estimator","and","predict","on","X","Parameters","--","--","--","--","--","X",":","numpy","array","of","shape","(","n_samples",",","n_features",")","The","input","samples",".","y",":","numpy","array","of","shape","(","n_samples",",",")",",","optional","(","default=None",")","The","ground","truth","of","the","input","samples","(","labels",")",".","Returns","--","--","--","-","labels",":","numpy","array","of","shape","(","n_samples",",",")","Class","labels","for","each","data","sample","."]
code: def fit_predict(self, X, y):
      """Fit estimator and predict on X
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      y : numpy array of shape (n_samples,), optional (default=None)
          The ground truth of the input samples (labels).
  
      Returns
      -------
      labels : numpy array of shape (n_samples,)
          Class labels for each data sample.
      """
      raise NotImplementedError(
          "fit_predict should not be used in supervised learning models."
      )
  
file_path: combo/combo/models/classifier_stacking.py
imports: import warnings
  import numpy
  from sklearn.linear_model import LogisticRegression
  from sklearn.utils import shuffle
  from sklearn.utils import check_random_state
  from sklearn.utils import check_array
  from sklearn.utils import check_X_y
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import check_parameter
  from ..utils.utility import list_diff
  from .base import BaseAggregator
label_desc: Use image node_id 6 for calling the Stacking obj's underlying member method code with example usage: obj.fit_predict(X, y) without return types