docstr: Whether to retrain all models on all of the data (training + validation) after the normal training procedure.
  This is equivalent to calling `predictor.refit_full(model=refit_full)` after fit.
  If `refit_full=True`, it will be treated as `refit_full='all'`.
  If `refit_full=False`, refitting will not occur.
  Valid str values:
      `all`: refits all models.
      `best`: refits only the best model (and its ancestors if it is a stacker model).
      `{model_name}`: refits only the specified model (and its ancestors if it is a stacker model).
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code: def refit_full(
      self, refit_full: Union[bool, str] = True
  ) -> ConfigBuilder:
      """
      Whether to retrain all models on all of the data (training + validation) after the normal training procedure.
      This is equivalent to calling `predictor.refit_full(model=refit_full)` after fit.
      If `refit_full=True`, it will be treated as `refit_full='all'`.
      If `refit_full=False`, refitting will not occur.
      Valid str values:
          `all`: refits all models.
          `best`: refits only the best model (and its ancestors if it is a stacker model).
          `{model_name}`: refits only the specified model (and its ancestors if it is a stacker model).
      """
      self.config["refit_full"] = refit_full
      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 17 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.refit_full(refit_full) and returns: self