code: def _check_params(self):
      self._validate_params()
  
      if self.n_estimators > len(self.base_estimators):
          raise ValueError(
              f"n_estimators ({self.n_estimators}) must not exceed"
              f" number of base learners ({len(self.base_estimators)})"
          )
  
      if isinstance(self.n_estimators, numbers.Integral):
          self.n_estimators_ = self.n_estimators
      else:
          self.n_estimators_ = max(
              int(self.n_estimators * len(self.base_estimators)), 1
          )
  
      if self.correlation == "pearson":
          self._corr_func = lambda x: np.corrcoef(x, rowvar=0)
      elif self.correlation == "kendall":
          self._corr_func = _corr_kendalltau
      elif self.correlation == "spearman":
          self._corr_func = lambda x: spearmanr(x, axis=0).correlation
  
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 3 for calling the BaseEnsembleSelection obj's underlying member method code with example usage: obj._check_params() without return types