docstr: Fraction of train_data to holdout as tuning data for optimizing hyperparameters (ignored unless `tuning_data = None`, ignored if `num_bag_folds != 0` unless `use_bag_holdout == True`).
  Default value (if None) is selected based on the number of rows in the training data. Default values range from 0.2 at 2,500 rows to 0.01 at 250,000 rows.
  Default value is doubled if `hyperparameter_tune_kwargs` is set, up to a maximum of 0.2.
  Disabled if `num_bag_folds >= 2` unless `use_bag_holdout == True`.
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code: def holdout_frac(self, holdout_frac: float) -> ConfigBuilder:
      """
      Fraction of train_data to holdout as tuning data for optimizing hyperparameters (ignored unless `tuning_data = None`, ignored if `num_bag_folds != 0` unless `use_bag_holdout == True`).
      Default value (if None) is selected based on the number of rows in the training data. Default values range from 0.2 at 2,500 rows to 0.01 at 250,000 rows.
      Default value is doubled if `hyperparameter_tune_kwargs` is set, up to a maximum of 0.2.
      Disabled if `num_bag_folds >= 2` unless `use_bag_holdout == True`.
      """
      assert (holdout_frac >= 0) & (
          holdout_frac <= 1
      ), "holdout_frac must be between 0 and 1"
      self.config["holdout_frac"] = holdout_frac
      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 9 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.holdout_frac(holdout_frac) and returns: self