code: def get_metric_result(cv_results):
      results = pd.DataFrame.from_dict(cv_results)
      results = results[results["status"] == "Success"]
      cols = [
          "rank_test_scores",
          "param_classifier:__choice__",
          "mean_test_score",
      ]
      cols.extend(
          [
              key
              for key in cv_results.keys()
              if key.startswith("metric_")
          ]
      )
      return results[cols]
  
file_path: auto-sklearn/examples/40_advanced/example_calc_multiple_metrics.py
imports: import autosklearn.classification
  import numpy
  import pandas
  import sklearn.datasets
  import sklearn.metrics
  from autosklearn.metrics import balanced_accuracy, precision, recall, f1
label_desc: Use image node_id 2 for calling a global function with example usage: get_metric_result(cv_results) and returns: results