docstr: Hyperparameter tuning strategy and kwargs (for example, how many HPO trials to run).
  If None, then hyperparameter tuning will not be performed.
  Valid preset values:
      'auto': Uses the 'random' preset.
      'random': Performs HPO via random search using local scheduler.
  The 'searcher' key is required when providing a dict.
docstr_tok: ["Hyperparameter","tuning","strategy","and","kwargs","(","for","example",",","how","many","HPO","trials","to","run",")",".","If","None",",","then","hyperparameter","tuning","will","not","be","performed",".","Valid","preset","values",":","'auto","'",":","Uses","the","'random","'","preset",".","'random","'",":","Performs","HPO","via","random","search","using","local","scheduler",".","The","'searcher","'","key","is","required","when","providing","a","dict","."]
code: def hyperparameter_tune_kwargs(
      self, hyperparameter_tune_kwargs: Union[str, dict]
  ) -> ConfigBuilder:
      """
      Hyperparameter tuning strategy and kwargs (for example, how many HPO trials to run).
      If None, then hyperparameter tuning will not be performed.
      Valid preset values:
          'auto': Uses the 'random' preset.
          'random': Performs HPO via random search using local scheduler.
      The 'searcher' key is required when providing a dict.
      """
      valid_str_values = scheduler_factory._scheduler_presets.keys()
      if isinstance(hyperparameter_tune_kwargs, str):
          assert (
              hyperparameter_tune_kwargs in valid_str_values
          ), f"{hyperparameter_tune_kwargs} string must be one of {valid_str_values}"
      elif not isinstance(hyperparameter_tune_kwargs, dict):
          raise ValueError(
              f"hyperparameter_tune_kwargs must be either str: {valid_str_values} or dict"
          )
      self.config[
          "hyperparameter_tune_kwargs"
      ] = hyperparameter_tune_kwargs
  
      return self
  
imports: from __future__ import annotations
  import copy
  from typing import Union
  from sklearn.base import BaseEstimator
  from autogluon.core.scheduler import scheduler_factory
  from autogluon.features import AutoMLPipelineFeatureGenerator
  from autogluon.tabular.configs.hyperparameter_configs import hyperparameter_config_dict
  from autogluon.tabular.configs.presets_configs import tabular_presets_dict
  from autogluon.tabular.trainer.model_presets.presets import MODEL_TYPES
label_desc: Use image node_id 11 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.hyperparameter_tune_kwargs(hyperparameter_tune_kwargs) and returns: self