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: Internal function to set estimator weights.
  
  Parameters
  ----------
  weights : numpy array of shape (n_estimators,)
      Estimator weights. May be used after the alignment.
  
  Returns
  -------
  self
docstr_tok: ["Internal","function","to","set","estimator","weights",".","Parameters","--","--","--","--","--","weights",":","numpy","array","of","shape","(","n_estimators",",",")","Estimator","weights",".","May","be","used","after","the","alignment",".","Returns","--","--","--","-","self"]
code: def _set_weights(self, weights):
      """Internal function to set estimator weights.
  
      Parameters
      ----------
      weights : numpy array of shape (n_estimators,)
          Estimator weights. May be used after the alignment.
  
      Returns
      -------
      self
  
      """
  
      if weights is None:
          self.weights = np.ones([1, self.n_base_estimators_])
      else:
          self.weights = column_or_1d(weights).reshape(1, len(weights))
          assert self.weights.shape[1] == self.n_base_estimators_
  
          # adjust probability by a factor for integrity （added to 1）
          adjust_factor = self.weights.shape[1] / np.sum(weights)
          self.weights = self.weights * adjust_factor
  
          print(self.weights)
      return self
  
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 10 for calling the BaseAggregator obj's underlying member method code with example usage: obj._set_weights(weights) and returns: self