code: def model_performance_sweep(models_dict, X_test, y_test, A_test):
      """Compute the equalized_odds_difference and balanced_error_rate for a
      given list of inner models learned by the ExponentiatedGradient algorithm.
      Return a DataFrame containing the epsilon level of the model, the index
      of the model, the equalized_odds_difference score and the balanced_error
      for the model.
  
      Parameters
      ----------
      models_dict: Dictionary mapping model ids to a model.
      X_test: The data features of the testing data set
      y_test: The target labels of the testing data set
      A_test: The sensitive feature of the testing data set.
  
      Returns
      -------
      DataFrame where each row represents a model (epsilon, index) and its
      performance metrics
      """
      performances = []
      for eps, models in models_dict.items():
          eq_odds_difference = aggregate_predictor_performances(
              models, equalized_odds_difference, X_test, y_test, A_test
          )
          bal_acc_score = aggregate_predictor_performances(
              models, balanced_accuracy_score, X_test, y_test
          )
          for i, score in enumerate(eq_odds_difference):
              performances.append(
                  (eps, i, score, (1 - bal_acc_score[i]))
              )
      performances_df = pd.DataFrame.from_records(
          performances,
          columns=[
              "epsilon",
              "index",
              "equalized_odds",
              "balanced_error",
          ],
      )
      return performances_df
  
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import lightgbm
  from sklearn.metrics import balanced_accuracy_score, roc_auc_score
  from sklearn.metrics import confusion_matrix
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from fairlearn.metrics import MetricFrame
  from fairlearn.metrics import count, selection_rate, equalized_odds_difference, false_positive_rate, false_negative_rate
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.reductions import ExponentiatedGradient
  from fairlearn.reductions import EqualizedOdds
  from sklearn.model_selection import train_test_split
  import warnings
label_desc: Use image node_id 12 for calling a global function with example usage: model_performance_sweep(models_dict, X_test, y_test, A_test) and returns: performances_df