code: def convert_pred_probas_df_to_list(self, pred_probas_df) -> list:
      pred_probas = []
      for i, model in enumerate(self.base_model_names):
          index_start = i * self.num_pred_cols_per_model
          index_end = (i + 1) * self.num_pred_cols_per_model
          model_cols = self.features[index_start:index_end]
          pred_proba = pred_probas_df[model_cols].values
          if (
              self.num_pred_cols_per_model == 1
              and self.problem_type != QUANTILE
          ):
              pred_proba = pred_proba.flatten()
          pred_probas.append(pred_proba)
      return pred_probas
  
imports: import logging
  from autogluon.common.features.types import S_STACK
  from ...constants import MULTICLASS, QUANTILE, SOFTCLASS
  from ..abstract.abstract_model import AbstractModel
  from .ensemble_selection import EnsembleSelection, SimpleWeightedEnsemble
label_desc: Use image node_id 7 for calling the GreedyWeightedEnsembleModel obj's underlying member method code with example usage: obj.convert_pred_probas_df_to_list(pred_probas_df) and returns: pred_probas