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 calculate key attributes for outlier detection
  combination tasks.
  
  - threshold_: used to decide the binary label
  - labels_: binary labels of training data
  
  Returns
  -------
  self
docstr_tok: ["Internal","function","to","calculate","key","attributes","for","outlier","detection","combination","tasks",".","-","threshold_",":","used","to","decide","the","binary","label","-","labels_",":","binary","labels","of","training","data","Returns","--","--","--","-","self"]
code: def _process_decision_scores(self):
      """Internal function to calculate key attributes for outlier detection
      combination tasks.
  
      - threshold_: used to decide the binary label
      - labels_: binary labels of training data
  
      Returns
      -------
      self
      """
  
      self.threshold_ = percentile(
          self.decision_scores_, 100 * (1 - self.contamination)
      )
      self.labels_ = (
          (self.decision_scores_ > self.threshold_)
          .astype("int")
          .ravel()
      )
  
      # calculate for predict_proba()
  
      self._mu = np.mean(self.decision_scores_)
      self._sigma = np.std(self.decision_scores_)
  
      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 6 for calling the BaseAggregator obj's underlying member method code with example usage: obj._process_decision_scores() and returns: self