class_docstr: Tune driver
class_docstr_tok: ["Tune","driver"]
docstr: Constructor
  :param logs_dir: local dir to save training results
  :param resources_per_trial: resources for each trial
  :param name: searcher name
  :param remote_dir: checkpoint will be uploaded to remote_dir in hdfs
docstr_tok: ["Constructor",":","param","logs_dir",":","local","dir","to","save","training","results",":","param","resources_per_trial",":","resources","for","each","trial",":","param","name",":","searcher","name",":","param","remote_dir",":","checkpoint","will","be","uploaded","to","remote_dir","in","hdfs"]
code: def __init__(
      self,
      logs_dir="",
      resources_per_trial=None,
      name="",
      remote_dir=None,
  ):
      """
      Constructor
      :param logs_dir: local dir to save training results
      :param resources_per_trial: resources for each trial
      :param name: searcher name
      :param remote_dir: checkpoint will be uploaded to remote_dir in hdfs
      """
      self.train_func = None
      self.resources_per_trial = resources_per_trial
      self.trials = None
      self.name = name
      self.remote_dir = (
          remote_dir or RayTuneSearchEngine.get_default_remote_dir(name)
      )
      self.logs_dir = os.path.abspath(os.path.expanduser(logs_dir))
  
imports: import ray
  from ray import tune
  import os
  from zoo.orca.automl.search.base import SearchEngine, TrialOutput, GoodError
  from zoo.orca.automl.search.parameters import DEFAULT_LOGGER_NAME, DEFAULT_METRIC_NAME
  from zoo.orca.automl.search.ray_tune.utils import convert_bayes_configs
  from zoo.orca.automl.search.utils import get_ckpt_hdfs, put_ckpt_hdfs
  from zoo.orca.automl.search import TensorboardLogger
  from ray.tune import Stopper
  from zoo.orca.automl.model.abstract import ModelBuilder
label_desc: Use image node_id 1 to create a new RayTuneSearchEngine object from inherited base classes: SearchEngine with example: obj = RayTuneSearchEngine(logs_dir, resources_per_trial, name, remote_dir)