class_docstr: Base class for hyper parameter search with cross-validation.
docstr: Call ``predict()`` on the estimator with the best found parameters
  (according the the ``refit`` parameter). See :meth:`AlgoBase.predict()
  <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`.
  
  Only available if ``refit`` is not ``False``.
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code: def predict(self, *args):
      """Call ``predict()`` on the estimator with the best found parameters
      (according the the ``refit`` parameter). See :meth:`AlgoBase.predict()
      <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`.
  
      Only available if ``refit`` is not ``False``.
      """
  
      if not self.refit:
          raise ValueError("refit is False, cannot use predict()")
  
      return self.best_estimator[self.refit].predict(*args)
  
imports: from abc import ABC, abstractmethod
  from itertools import product
  import numpy
  from joblib import delayed, Parallel
  from ..dataset import DatasetUserFolds
  from ..utils import get_rng
  from .split import get_cv
  from .validation import fit_and_score
label_desc: Use image node_id 5 for calling the BaseSearchCV obj's underlying member method code with example usage: obj.predict() and returns: self