code: def aggregate_predictor_performances(
      predictors, metric, X_test, Y_test, A_test=None
  ):
      """Compute the specified metric for all classifiers in predictors.
      If no sensitive features are present, the metric is computed without
      disaggregation.
  
      Parameters
      ----------
      predictors: A set of classifiers to generate predictions from.
      metric: The metric (callable) to compute for each classifier in predictor
      X_test: The data features of the testing data set
      Y_test: The target labels of the teting data set
      A_test: The sensitive feature of the testing data set.
  
      Returns
      -------
      List of performance scores for each classifier in predictors, for the
      given metric.
      """
      all_predictions = [
          predictor.predict(X_test) for predictor in predictors
      ]
      if A_test is not None:
          return [
              metric(Y_test, Y_sweep, sensitive_features=A_test)
              for Y_sweep in all_predictions
          ]
      else:
          return [
              metric(Y_test, Y_sweep) for Y_sweep in all_predictions
          ]
  
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 11 for calling a global function with example usage: aggregate_predictor_performances(predictors, metric, X_test, Y_test, A_test) and returns: unknown, unknown