class_docstr: Base class for stacking method.
docstr: Predict target for X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training vectors, where `n_samples` is the number of samples and
      `n_features` is the number of features.
  
  **predict_params : dict of str -> obj
      Parameters to the `predict` called by the `final_estimator`. Note
      that this may be used to return uncertainties from some estimators
      with `return_std` or `return_cov`. Be aware that it will only
      accounts for uncertainty in the final estimator.
  
  Returns
  -------
  y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)
      Predicted targets.
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code: def predict(self, X, **predict_params):
      """Predict target for X.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vectors, where `n_samples` is the number of samples and
          `n_features` is the number of features.
  
      **predict_params : dict of str -> obj
          Parameters to the `predict` called by the `final_estimator`. Note
          that this may be used to return uncertainties from some estimators
          with `return_std` or `return_cov`. Be aware that it will only
          accounts for uncertainty in the final estimator.
  
      Returns
      -------
      y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)
          Predicted targets.
      """
  
      check_is_fitted(self)
      return self.final_estimator_.predict(
          self.transform(X), **predict_params
      )
  
imports: from abc import ABCMeta, abstractmethod
  from copy import deepcopy
  from numbers import Integral
  import numpy
  import scipy.sparse
  from ..base import ClassifierMixin, RegressorMixin, TransformerMixin, _fit_context, clone, is_classifier, is_regressor
  from ..exceptions import NotFittedError
  from ..linear_model import LogisticRegression, RidgeCV
  from ..model_selection import check_cv, cross_val_predict
  from ..preprocessing import LabelEncoder
  from ..utils import Bunch
  from ..utils._estimator_html_repr import _VisualBlock
  from ..utils._param_validation import HasMethods, StrOptions
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import check_classification_targets, type_of_target
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import _check_feature_names_in, _check_response_method, check_is_fitted, column_or_1d
  from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator
label_desc: Use image node_id 9 for calling the _BaseStacking obj's underlying member method code with example usage: obj.predict(X) and returns: self