code: def bench(clfs):
      for (
          name,
          clf,
          iter_range,
          train_losses,
          train_scores,
          test_scores,
          durations,
      ) in clfs:
          print("training %s" % name)
          clf_type = type(clf)
          clf_params = clf.get_params()
  
          for n_iter in iter_range:
              gc.collect()
  
              train_loss, train_score, test_score, duration = bench_one(
                  name, clf_type, clf_params, n_iter
              )
  
              train_losses.append(train_loss)
              train_scores.append(train_score)
              test_scores.append(test_score)
              durations.append(duration)
              print("classifier: %s" % name)
              print("train_loss: %.8f" % train_loss)
              print("train_score: %.8f" % train_score)
              print("test_score: %.8f" % test_score)
              print("time for fit: %.8f seconds" % duration)
              print("")
  
          print("")
      return clfs
  
file_path: scikit-learn/benchmarks/bench_rcv1_logreg_convergence.py
imports: import gc
  import time
  import matplotlib.pyplot
  import numpy
  from joblib import Memory
  from sklearn.datasets import fetch_rcv1
  from sklearn.linear_model import LogisticRegression, SGDClassifier
  from sklearn.linear_model._sag import get_auto_step_size
label_desc: Use image node_id 3 for calling a global function with example usage: bench(clfs) and returns: clfs