class_docstr: Automated TimeSeries Estimator for time series forecasting task, which supports
  TSDataset and customized data creator as data input on built-in model (only
  "lstm", "tcn", "seq2seq" for now) and 3rd party model.
  
  Only backend="torch" is supported for now. Customized data creator has not been
  fully supported by TSPipeline.
  
  >>> # Here is a use case example:
  >>> # prepare train/valid/test tsdataset
  >>> autoest = AutoTSEstimator(model="lstm",
  >>>                           search_space=search_space,
  >>>                           past_seq_len=6,
  >>>                           future_seq_len=1)
  >>> tsppl = autoest.fit(data=tsdata_train,
  >>>                     validation_data=tsdata_valid)
  >>> tsppl.predict(tsdata_test)
  >>> tsppl.save("my_tsppl")
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docstr: For internal use only.
  
  :return: the best automl model instance
docstr_tok: ["For","internal","use","only",".",":","return",":","the","best","automl","model","instance"]
code: def _get_best_automl_model(self):
      """
      For internal use only.
  
      :return: the best automl model instance
      """
      return self.model._get_best_automl_model()
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/autotsestimator.py
imports: import types
  from zoo.orca.automl.auto_estimator import AutoEstimator
  from zoo.chronos.data import TSDataset
  import zoo.orca.automl.hp
  from zoo.chronos.autots.model import AutoModelFactory
  from zoo.chronos.autots.tspipeline import TSPipeline
label_desc: Use image node_id 4 for calling the AutoTSEstimator obj's underlying member method code with example usage: obj._get_best_automl_model() and returns: self