class_docstr: Base class for all ensemble classes.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
  
  Parameters
  ----------
  estimator : object
      The base estimator from which the ensemble is built.
  
  n_estimators : int, default=10
      The number of estimators in the ensemble.
  
  estimator_params : list of str, default=tuple()
      The list of attributes to use as parameters when instantiating a
      new base estimator. If none are given, default parameters are used.
  
  Attributes
  ----------
  estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
  estimators_ : list of estimators
      The collection of fitted base estimators.
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docstr: Return the number of estimators in the ensemble.
docstr_tok: ["Return","the","number","of","estimators","in","the","ensemble","."]
code: def __len__(self):
      """Return the number of estimators in the ensemble."""
      return len(self.estimators_)
  
imports: from abc import ABCMeta, abstractmethod
  from typing import List
  import numpy
  from joblib import effective_n_jobs
  from ..base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier, is_regressor
  from ..utils import Bunch, _print_elapsed_time, check_random_state
  from ..utils._tags import _safe_tags
  from ..utils.metaestimators import _BaseComposition
label_desc: Use image node_id 4 for calling the BaseEnsemble obj's underlying member method code with example usage: obj.__len__() and returns: len