docstr: If True and the problem_type is classification, temperature scaling will be used to calibrate the Predictor's estimated class probabilities
  (which may improve metrics like log_loss) and will train a scalar parameter on the validation set.
  If True and the problem_type is quantile regression, conformalization will be used to calibrate the Predictor's estimated quantiles
  (which may improve the prediction interval coverage, and bagging could further improve it) and will compute a set of scalar parameters on the validation set.
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code: def calibrate(self, calibrate=True) -> ConfigBuilder:
      """
      If True and the problem_type is classification, temperature scaling will be used to calibrate the Predictor's estimated class probabilities
      (which may improve metrics like log_loss) and will train a scalar parameter on the validation set.
      If True and the problem_type is quantile regression, conformalization will be used to calibrate the Predictor's estimated quantiles
      (which may improve the prediction interval coverage, and bagging could further improve it) and will compute a set of scalar parameters on the validation set.
      """
      self.config["calibrate"] = calibrate
      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 22 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.calibrate(calibrate) and returns: self