code: def __init__(
      self,
      meta_estimator,
      base_estimators,
      scorer=None,
      n_estimators=0.2,
      min_score=0.66,
      correlation="pearson",
      min_correlation=0.6,
      cv=None,
      n_jobs=1,
      verbose=0,
  ):
      super().__init__(
          meta_estimator=meta_estimator, base_estimators=base_estimators
      )
  
      self.scorer = scorer
      self.n_estimators = n_estimators
      self.min_score = min_score
      self.correlation = correlation
      self.min_correlation = min_correlation
      self.cv = cv
      self.n_jobs = n_jobs
      self.verbose = verbose
  
      self._extra_params.extend(
          [
              "scorer",
              "n_estimators",
              "min_score",
              "min_correlation",
              "cv",
              "n_jobs",
              "verbose",
          ]
      )
  
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 1 to create a new BaseEnsembleSelection object from inherited base classes: Stacking with example: obj = BaseEnsembleSelection(meta_estimator, base_estimators, scorer, n_estimators, min_score, correlation, min_correlation, cv, n_jobs, verbose)