class_docstr: A 'Stacking Cross-Validation' regressor for scikit-learn estimators.
  
  Parameters
  ----------
  regressors : array-like, shape = [n_regressors]
      A list of regressors.
      Invoking the `fit` method on the `StackingCVRegressor` will fit clones
      of these original regressors that will
      be stored in the class attribute `self.regr_`.
  
  meta_regressor : object
      The meta-regressor to be fitted on the ensemble of
      regressor
  
  cv : int, cross-validation generator or iterable, optional (default: 5)
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
      - None, to use the default 5-fold cross validation,
      - integer, to specify the number of folds in a `KFold`,
      - An object to be used as a cross-validation generator.
      - An iterable yielding train, test splits.
      For integer/None inputs, it will use `KFold` cross-validation
  
  shuffle : bool (default: True)
      If True,  and the `cv` argument is integer, the training data will
      be shuffled at fitting stage prior to cross-validation. If the `cv`
      argument is a specific cross validation technique, this argument is
      omitted.
  
  random_state : int, RandomState instance or None, optional (default: None)
      Constrols the randomness of the cv splitter. Used when `cv` is
      integer and `shuffle=True`. New in v0.16.0.
  
  verbose : int, optional (default=0)
      Controls the verbosity of the building process. New in v0.16.0
  
  refit : bool (default: True)
      Clones the regressors for stacking regression if True (default)
      or else uses the original ones, which will be refitted on the dataset
      upon calling the `fit` method. Setting refit=False is
      recommended if you are working with estimators that are supporting
      the scikit-learn fit/predict API interface but are not compatible
      to scikit-learn's `clone` function.
  
  use_features_in_secondary : bool (default: False)
      If True, the meta-regressor will be trained both on
      the predictions of the original regressors and the
      original dataset.
      If False, the meta-regressor will be trained only on
      the predictions of the original regressors.
  
  store_train_meta_features : bool (default: False)
      If True, the meta-features computed from the training data
      used for fitting the
      meta-regressor stored in the `self.train_meta_features_` array,
      which can be
      accessed after calling `fit`.
  
  n_jobs : int or None, optional (default=None)
      The number of CPUs to use to do the computation.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details. New in v0.16.0.
  
  pre_dispatch : int, or string, optional
      Controls the number of jobs that get dispatched during parallel
      execution. Reducing this number can be useful to avoid an
      explosion of memory consumption when more jobs get dispatched
      than CPUs can process. This parameter can be:
          - None, in which case all the jobs are immediately
            created and spawned. Use this for lightweight and
            fast-running jobs, to avoid delays due to on-demand
            spawning of the jobs
          - An int, giving the exact number of total jobs that are
            spawned
          - A string, giving an expression as a function of n_jobs,
            as in '2*n_jobs'
  
  multi_output : bool (default: False)
      If True, allow multi-output targets, but forbid nan or inf values.
      If False, `y` will be checked to be a vector. (New in v0.19.0.)
  
  Attributes
  ----------
  train_meta_features : numpy array, shape = [n_samples, n_regressors]
      meta-features for training data, where n_samples is the
      number of samples
      in training data and len(self.regressors) is the number of regressors.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/
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code: def get_params(self, deep=True):
      #
      # Return estimator parameter names for GridSearch support.
      #
      return self._get_params("named_regressors", deep=deep)
  
file_path: mlxtend/mlxtend/regressor/stacking_cv_regression.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import RegressorMixin, TransformerMixin, clone
  from sklearn.model_selection import cross_val_predict
  from sklearn.model_selection._split import check_cv
  from sklearn.utils import check_X_y
  from ..externals.estimator_checks import check_is_fitted
  from ..externals.name_estimators import _name_estimators
  from ..utils.base_compostion import _BaseXComposition
label_desc: Use image node_id 6 for calling the StackingCVRegressor obj's underlying member method code with example usage: obj.get_params(deep) and returns: self