docstr: Fit ensemble of models
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
      Training data.
  
  y : array-like, optional
      Target data if base estimators are supervised.
  
  Returns
  -------
  self
code: def fit(self, X, y=None, **fit_params):
      """Fit ensemble of models
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
          Training data.
  
      y : array-like, optional
          Target data if base estimators are supervised.
  
      Returns
      -------
      self
      """
      self._check_params()
  
      cv = check_cv(self.cv, X)
      self._fit(X, y, cv, **fit_params)
  
      return self
  
imports: import numbers
  from joblib import Parallel, delayed
  import numpy
  from scipy.stats import kendalltau, rankdata, spearmanr
  from sklearn.base import BaseEstimator, clone
  from sklearn.model_selection import check_cv
  from sklearn.utils._param_validation import Interval, StrOptions
  from .base import _fit_and_score
  from .stacking import Stacking
label_desc: Use image node_id 11 for calling the BaseEnsembleSelection obj's underlying member method code with example usage: obj.fit(X, y) and returns: self