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: See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
  and sklearn/base.py for more information.
docstr_tok: ["See","http",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.base.BaseEstimator.html","and","sklearn\/base.py","for","more","information","."]
code: def __repr__(self):
      # noinspection PyPep8
      """
      See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
      and sklearn/base.py for more information.
      """
  
      class_name = self.__class__.__name__
      return "%s(%s)" % (
          class_name,
          _pprint(
              self.get_params(deep=False),
              offset=len(class_name),
          ),
      )
  
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 17 for calling the BaseAggregator obj's underlying member method code with example usage: obj.__repr__() and returns: unknown