class_docstr: Abstract class for all combination classes.
  
  Parameters
  ----------
  base_estimators: list, length must be greater than 1
      A list of base estimators. Certain methods must be present, e.g.,
      `fit` and `predict`.
  
  pre_fitted: bool, optional (default=False)
      Whether the base estimators are trained. If True, `fit`
      process may be skipped.
docstr: Predict the class labels for the provided data.
  
  Parameters
  ----------
  X : numpy array of shape (n_samples, n_features)
      The input samples.
  
  Returns
  -------
  labels : numpy array of shape (n_samples,)
      Class labels for each data sample.
docstr_tok: ["Predict","the","class","labels","for","the","provided","data",".","Parameters","--","--","--","--","--","X",":","numpy","array","of","shape","(","n_samples",",","n_features",")","The","input","samples",".","Returns","--","--","--","-","labels",":","numpy","array","of","shape","(","n_samples",",",")","Class","labels","for","each","data","sample","."]
code: def predict(self, X):
      """Predict the class labels for the provided data.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      Returns
      -------
      labels : numpy array of shape (n_samples,)
          Class labels for each data sample.
      """
      pass
  
imports: import warnings
  from collections import defaultdict
  from abc import ABC, abstractmethod
  import numpy
  from numpy import percentile
  from scipy.special import erf
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils import column_or_1d
  from sklearn.utils.validation import check_is_fitted
  from sklearn.utils.multiclass import check_classification_targets
  from .sklearn_base import _pprint
  from inspect import signature
label_desc: Use image node_id 4 for calling the BaseAggregator obj's underlying member method code with example usage: obj.predict(X) without return types