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: Set the number of classes if `y` is presented.
  
  Parameters
  ----------
  y : numpy array of shape (n_samples,)
      Ground truth.
  
  Returns
  -------
  self
docstr_tok: ["Set","the","number","of","classes","if","`","y","`","is","presented",".","Parameters","--","--","--","--","--","y",":","numpy","array","of","shape","(","n_samples",",",")","Ground","truth",".","Returns","--","--","--","-","self"]
code: def _set_n_classes(self, y):
      """Set the number of classes if `y` is presented.
  
      Parameters
      ----------
      y : numpy array of shape (n_samples,)
          Ground truth.
  
      Returns
      -------
      self
      """
  
      self._classes = 2  # default as binary classification
      if y is not None:
          check_classification_targets(y)
          self._classes = len(np.unique(y))
  
      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 9 for calling the BaseAggregator obj's underlying member method code with example usage: obj._set_n_classes(y) and returns: self