class_docstr: Hyper Parameter Searcher. A Tuner-like class.
class_docstr_tok: ["Hyper","Parameter","Searcher",".","A","Tuner-like","class","."]
docstr: `_run` wrapper to set the proper state during tuning,        as this can be called multiple times.
docstr_tok: ["`","_run","`","wrapper","to","set","the","proper","state","during","tuning",",","as","this","can","be","called","multiple","times","."]
code: def _run(self, *args: Any, **kwargs: Any) -> None:
      """`_run` wrapper to set the proper state during tuning,\
          as this can be called multiple times."""
      # last `_run` call might have set it to `FINISHED`
      self.trainer.state.status = TrainerStatus.RUNNING
      self.trainer.training = True
      self.trainer.state.fn = TrainerFn.FITTING
      if self.num_process > 1:
          self.trainer.fit(*args, **kwargs)
      else:
          self.trainer._run(*args, **kwargs)
      self.trainer.tuning = True
  
imports: from typing import Any
  import pytorch_lightning
  import copy
  import math
  from pytorch_lightning.trainer.states import TrainerFn, TrainerStatus
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.automl.hpo.backend import create_hpo_backend, SamplerType
  from .objective import Objective
  from ._helper import ResetCallback, CustomEvaluationLoop
  from bigdl.nano.automl.utils.parallel import run_parallel
  from bigdl.nano.automl.hpo.search import _search_summary, _end_search, _create_study, _validate_args, _prepare_args
label_desc: Use image node_id 8 for calling the HPOSearcher obj's underlying member method code with example usage: obj._run() without return types