docstr: List of preset configurations for various arguments in `fit()`. Can significantly impact predictive accuracy, memory-footprint, and inference latency of trained models, and various other properties of the returned `predictor`.
  It is recommended to specify presets and avoid specifying most other `fit()` arguments or model hyperparameters prior to becoming familiar with AutoGluon.
  Available Presets: ['best_quality', 'high_quality', 'good_quality', 'medium_quality', 'optimize_for_deployment', 'ignore_text']
  It is recommended to only use one `quality` based preset in a given call to `fit()` as they alter many of the same arguments and are not compatible with each-other.
  If there is an overlap in presets keys, the latter presets will override the earlier ones.
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code: def presets(self, presets: Union[str, list, dict]) -> ConfigBuilder:
      """
      List of preset configurations for various arguments in `fit()`. Can significantly impact predictive accuracy, memory-footprint, and inference latency of trained models, and various other properties of the returned `predictor`.
      It is recommended to specify presets and avoid specifying most other `fit()` arguments or model hyperparameters prior to becoming familiar with AutoGluon.
      Available Presets: ['best_quality', 'high_quality', 'good_quality', 'medium_quality', 'optimize_for_deployment', 'ignore_text']
      It is recommended to only use one `quality` based preset in a given call to `fit()` as they alter many of the same arguments and are not compatible with each-other.
      If there is an overlap in presets keys, the latter presets will override the earlier ones.
      """
      valid_keys = list(tabular_presets_dict.keys())
      if isinstance(presets, str):
          presets = [presets]
  
      if isinstance(presets, list):
          unknown_keys = [k for k in presets if k not in valid_keys]
          assert (
              len(unknown_keys) == 0
          ), f"The following presets are not recognized: {unknown_keys} - use one of the valid presets: {valid_keys}"
  
      self.config["presets"] = presets
      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 2 for calling the ConfigBuilder obj's underlying member method code with example usage: obj.presets(presets) and returns: self