class_docstr: Base class for stacking method.
code: def _sk_visual_block_with_final_estimator(self, final_estimator):
      names, estimators = zip(*self.estimators)
      parallel = _VisualBlock(
          "parallel", estimators, names=names, dash_wrapped=False
      )
  
      # final estimator is wrapped in a parallel block to show the label:
      # 'final_estimator' in the html repr
      final_block = _VisualBlock(
          "parallel",
          [final_estimator],
          names=["final_estimator"],
          dash_wrapped=False,
      )
      return _VisualBlock(
          "serial", (parallel, final_block), dash_wrapped=False
      )
  
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 10 for calling the _BaseStacking obj's underlying member method code with example usage: obj._sk_visual_block_with_final_estimator(final_estimator) and returns: _VisualBlock