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: Get the parameters of an estimator from the ensemble.
  
  Returns the parameters given in the constructor as well as the
  estimators contained within the `estimators` parameter.
  
  Parameters
  ----------
  deep : bool, default=True
      Setting it to True gets the various estimators and the parameters
      of the estimators as well.
  
  Returns
  -------
  params : dict
      Parameter and estimator names mapped to their values or parameter
      names mapped to their values.
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code: def get_params(self, deep=True):
      """
      Get the parameters of an estimator from the ensemble.
  
      Returns the parameters given in the constructor as well as the
      estimators contained within the `estimators` parameter.
  
      Parameters
      ----------
      deep : bool, default=True
          Setting it to True gets the various estimators and the parameters
          of the estimators as well.
  
      Returns
      -------
      params : dict
          Parameter and estimator names mapped to their values or parameter
          names mapped to their values.
      """
      return super()._get_params("base_estimators", deep=deep)
  
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 4 for calling the Stacking obj's underlying member method code with example usage: obj.get_params(deep) and returns: super