class_docstr: Base class for stacking method.
docstr: Concatenate and return the predictions of the estimators.
docstr_tok: ["Concatenate","and","return","the","predictions","of","the","estimators","."]
code: def _transform(self, X):
      """Concatenate and return the predictions of the estimators."""
      check_is_fitted(self)
      predictions = [
          getattr(est, meth)(X)
          for est, meth in zip(self.estimators_, self.stack_method_)
          if est != "drop"
      ]
      return self._concatenate_predictions(X, predictions)
  
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 7 for calling the _BaseStacking obj's underlying member method code with example usage: obj._transform(X) and returns: self