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) or 3rd party model.
  
  >>> # 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: Get the best configuration
  
  :return: A dictionary of best hyper parameters
docstr_tok: ["Get","the","best","configuration",":","return",":","A","dictionary","of","best","hyper","parameters"]
code: def get_best_config(self):
      """
      Get the best configuration
  
      :return: A dictionary of best hyper parameters
      """
      return self.model.get_best_config()
  
file_path: BigDL/python/chronos/src/bigdl/chronos/autots/autotsestimator.py
imports: import types
  from bigdl.orca.automl.auto_estimator import AutoEstimator
  from bigdl.chronos.data import TSDataset
  import bigdl.orca.automl.hp
  from bigdl.chronos.autots.model import AutoModelFactory
  from bigdl.chronos.autots.utils import recalculate_n_sampling
label_desc: Use image node_id 5 for calling the AutoTSEstimator obj's underlying member method code with example usage: obj.get_best_config() and returns: self