code: def _hp_search(
      self,
      input_df,
      validation_df,
      metric,
      recipe,
      mc,
      resources_per_trial,
      remote_dir,
  ):
      model_builder = self.get_model_builder()
  
      self.metric = metric
      self._detach_recipe(recipe)
  
      # prepare parameters for search engine
      auto_est = AutoEstimator(
          model_builder,
          logs_dir=self.logs_dir,
          resources_per_trial=resources_per_trial,
          name=self.name,
          remote_dir=remote_dir,
      )
      auto_est.fit(
          data=input_df,
          validation_data=validation_df,
          search_space=self.search_space,
          n_sampling=self.num_samples,
          epochs=self.epochs,
          metric_threshold=self.metric_threshold,
          search_alg=self.search_alg,
          search_alg_params=self.search_alg_params,
          scheduler=self.scheduler,
          scheduler_params=self.scheduler_params,
          metric=metric,
      )
  
      best_model = auto_est._get_best_automl_model()
      pipeline = TimeSequencePipeline(name=self.name, model=best_model)
      return pipeline
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/deprecated/regression/base_predictor.py
imports: import pandas
  import os
  from abc import abstractmethod
  from zoo.orca.automl.metrics import Evaluator
  from zoo.chronos.autots.deprecated.pipeline.time_sequence import TimeSequencePipeline
  from zoo.orca.automl.search.utils import process
  from zoo.chronos.autots.deprecated.config.recipe import *
  from zoo.ray import RayContext
  from zoo.orca.automl.auto_estimator import AutoEstimator
label_desc: Use image node_id 9 for calling the BasePredictor obj's underlying member method code with example usage: obj._hp_search(input_df, validation_df, metric, recipe, mc, resources_per_trial, remote_dir) and returns: pipeline