code: def _get_max(models, alpha, best_models):
      """Method to get the model with the highest alpha weighted metric.
  
      Parameters
      ----------
      models : pd.DataFrame
          DataFrame with the results for the given dataset split.
      alpha : float
          Alpha value to use for the bootstrap samples.
      best_models : dict
          Dictionary with the indexes of the best models for each alpha value.
      """
      max_index = next(
          idx for idx in best_models[alpha] if idx in models.index
      )
      return_val = models.loc[max_index]
      return return_val
  
file_path: aequitas/src/aequitas/fairflow/utils/evaluation.py
imports: from typing import Literal, Union
  import numpy
  import pandas
  from ..evaluation import Result
label_desc: Use image node_id 6 for calling a global function with example usage: _get_max(models, alpha, best_models) and returns: return_val