code: def evaluate_performance(
      y_true: pd.Series,
      y_pred: pd.Series,
  ) -> dict:
      """Evaluates performance of a machine learning model.
  
      To utilize the Aequitas framework, we set the sensitive attribute to a constant.
  
      Parameters
      ----------
      y_true : pd.Series
          The true class labels.
      y_pred : pd.Series
          The binarized predictions.
  
      Returns
      -------
      dict
          Dictionary with performance metrics.
      """
      result = {}
  
      sensitive_attribute = pd.Series(
          [1] * len(y_true), index=y_true.index
      ).astype(str)
      fairness_results = evaluate_fairness(
          y_true,
          y_pred,
          sensitive_attribute,
          return_groupwise_metrics=True,
      )
  
      for metric in METRICS:
          result[metric] = fairness_results[f"{metric}_1"]
  
      return result
  
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_performance(y_true, y_pred) and returns: result