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()
code: def __init__(
      self,
      model_builder,
      logs_dir="/tmp/auto_estimator_logs",
      resources_per_trial=None,
      remote_dir=None,
      name=None,
  ):
      self.model_builder = model_builder
      self.searcher = SearchEngineFactory.create_engine(
          backend="ray",
          logs_dir=logs_dir,
          resources_per_trial=resources_per_trial,
          remote_dir=remote_dir,
          name=name,
      )
      self._fitted = False
  
file_path: analytics-zoo/pyzoo/zoo/orca/automl/auto_estimator.py
imports: from zoo.orca.automl.search import SearchEngineFactory
label_desc: Use image node_id 1 to create a new AutoEstimator object  with example: obj = AutoEstimator(model_builder, logs_dir, resources_per_trial, remote_dir, name)