docstr: Number of repeats of kfold bagging to perform (values must be >= 1). Total number of models trained during bagging = `num_bag_folds * num_bag_sets`.
  Defaults to 1 if `time_limit` is not specified, otherwise 20 (always disabled if `num_bag_folds` is not specified).
  Values greater than 1 will result in superior predictive performance, especially on smaller problems and with stacking enabled (reduces overall variance).
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code: def num_bag_sets(self, num_bag_sets: int) -> ConfigBuilder:
      """
      Number of repeats of kfold bagging to perform (values must be >= 1). Total number of models trained during bagging = `num_bag_folds * num_bag_sets`.
      Defaults to 1 if `time_limit` is not specified, otherwise 20 (always disabled if `num_bag_folds` is not specified).
      Values greater than 1 will result in superior predictive performance, especially on smaller problems and with stacking enabled (reduces overall variance).
      """
      assert num_bag_sets > 0, "num_bag_sets must be greater than zero"
      self.config["num_bag_sets"] = num_bag_sets
      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 7 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.num_bag_sets(num_bag_sets) and returns: self