class_docstr: Hyper Parameter Searcher. A Tuner-like class.
class_docstr_tok: ["Hyper","Parameter","Searcher",".","A","Tuner-like","class","."]
code: def _create_objective(
      self,
      model,
      target_metric,
      mode,
      create_kwargs,
      acceleration,
      input_sample,
      fit_kwargs,
  ):
      # target_metric = self._fix_target_metric(target_metric, search_kwargs)
      isprune = True if create_kwargs.get("pruner", None) else False
      direction = create_kwargs.get("direction", None)
      directions = create_kwargs.get("directions", None)
      self.objective = Objective(
          searcher=self,
          model=model._model_build,
          target_metric=target_metric,
          mode=mode,
          pruning=isprune,
          direction=direction,
          directions=directions,
          acceleration=acceleration,
          input_sample=input_sample,
          **fit_kwargs,
      )
  
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 2 for calling the HPOSearcher obj's underlying member method code with example usage: obj._create_objective(model, target_metric, mode, create_kwargs, acceleration, input_sample, fit_kwargs) without return types