class_docstr: Example:
      >>> auto_est = AutoEstimator.from_torch(model_creator=model_creator,
                                              optimizer=get_optimizer,
                                              loss=nn.BCELoss(),
                                              logs_dir="/tmp/zoo_automl_logs",
                                              resources_per_trial={"cpu": 2},
                                              name="test_fit")
      >>> auto_est.fit(data=data,
                       validation_data=validation_data,
                       search_space=create_linear_search_space(),
                       n_sampling=4,
                       epochs=1,
                       metric="accuracy")
      >>> best_model = auto_est.get_best_model()
docstr: Return the best config found by the AutoEstimator
  
  :return: A dictionary of best hyper parameters
docstr_tok: ["Return","the","best","config","found","by","the","AutoEstimator",":","return",":","A","dictionary","of","best","hyper","parameters"]
code: def get_best_config(self):
      """
      Return the best config found by the AutoEstimator
  
      :return: A dictionary of best hyper parameters
      """
      if not self.best_trial:
          self.best_trial = self.searcher.get_best_trial()
      best_config = self.best_trial.config
      return best_config
  
file_path: BigDL/python/orca/src/bigdl/orca/automl/auto_estimator.py
imports: from bigdl.orca.automl.search import SearchEngineFactory
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import ndarray
  from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union, Any
label_desc: Use image node_id 6 for calling the AutoEstimator obj's underlying member method code with example usage: obj.get_best_config() and returns: best_config