class_docstr: Hyper Parameter Searcher. A Tuner-like class.
class_docstr_tok: ["Hyper","Parameter","Searcher",".","A","Tuner-like","class","."]
code: def _run_search_n_procs(self, n_procs=4):
      new_searcher = copy.deepcopy(self)
      n_trials = new_searcher.run_kwargs.get("n_trials", None)
      if n_trials:
          subp_n_trials = math.ceil(n_trials / n_procs)
          new_searcher.run_kwargs["n_trials"] = subp_n_trials
      run_parallel(
          func=new_searcher._run_search, kwargs={}, n_procs=n_procs
      )
  
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 4 for calling the HPOSearcher obj's underlying member method code with example usage: obj._run_search_n_procs(n_procs) without return types