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: Returns the index'th estimator in the ensemble.
docstr_tok: ["Returns","the","index'th","estimator","in","the","ensemble","."]
code: def __getitem__(self, index):
      """Returns the index'th estimator in the ensemble."""
      return self.base_estimators[index]
  
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 12 for calling the BaseAggregator obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self