class_docstr: Meta estimator that combines multiple base learners.
  
  By default, base estimators' output corresponds to the array returned
  by `predict_proba`. If `predict_proba` is not available or `probabilities = False`,
  the output of `predict` is used.
  
  Parameters
  ----------
  meta_estimator : instance of estimator
      The estimator that is used to combine the output of different
      base estimators.
  
  base_estimators : list
      List of (name, estimator) tuples (implementing fit/predict) that are
      part of the ensemble.
  
  probabilities : bool, optional, default: True
      Whether to allow using `predict_proba` method of base learners, if available.
  
  Attributes
  ----------
  estimators_ : list of estimators
      The elements of the estimators parameter, having been fitted on the
      training data.
  
  named_estimators_ : dict
      Attribute to access any fitted sub-estimators by name.
  
  final_estimator_ : estimator
      The estimator which combines the output of `estimators_`.
  
  n_features_in_ : int
      Number of features seen during ``fit``.
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during ``fit``. Defined only when `X`
      has feature names that are all strings.
  
  unique_times_ : array of shape = (n_unique_times,)
      Unique time points.
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docstr: Perform prediction.
  
  Only available if the meta estimator has a predict_proba method.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
      Data with samples to predict.
  
  Returns
  -------
  prediction : ndarray, shape = (n_samples, n_dim)
      Prediction of meta estimator that combines
      predictions of base estimators. `n_dim` depends
      on the return value of meta estimator's `predict`
      method.
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code: def predict_proba(self, X):
      """Perform prediction.
  
      Only available if the meta estimator has a predict_proba method.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
          Data with samples to predict.
  
      Returns
      -------
      prediction : ndarray, shape = (n_samples, n_dim)
          Prediction of meta estimator that combines
          predictions of base estimators. `n_dim` depends
          on the return value of meta estimator's `predict`
          method.
      """
      Xt = self._predict_estimators(X)
      return self.final_estimator_.predict_proba(Xt)
  
imports: import numpy
  from sklearn.base import MetaEstimatorMixin, clone
  from sklearn.utils._param_validation import HasMethods
  from sklearn.utils.metaestimators import _BaseComposition, available_if
  from ..base import SurvivalAnalysisMixin
  from ..util import property_available_if
label_desc: Use image node_id 11 for calling the Stacking obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self