class_docstr: Weighted ensemble meta-model that implements Ensemble Selection: https://www.cs.cornell.edu/~alexn/papers/shotgun.icml04.revised.rev2.pdf
  
  A :class:`autogluon.core.models.GreedyWeightedEnsembleModel` must be specified as the `model_base` to properly function.
docstr: This model can generate out-of-fold (oof) predictions by predicting directly on the training data.
  This will make the result slightly overfit, but the weighted ensemble has limited degrees of freedom intentionally, making the overfitting negligible.
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code: def _more_tags(self):
      """
      This model can generate out-of-fold (oof) predictions by predicting directly on the training data.
      This will make the result slightly overfit, but the weighted ensemble has limited degrees of freedom intentionally, making the overfitting negligible.
      """
      tags = {
          "can_get_oof_from_train": True,
          "print_weights": True,
      }
      return tags
  
file_path: autogluon/core/src/autogluon/core/models/ensemble/weighted_ensemble_model.py
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from ..greedy_ensemble.greedy_weighted_ensemble_model import GreedyWeightedEnsembleModel
  from .stacker_ensemble_model import StackerEnsembleModel
label_desc: Use image node_id 6 for calling the WeightedEnsembleModel obj's underlying member method code with example usage: obj._more_tags() and returns: tags