text: import json
  from datetime import datetime
  
  import pandas as pd
  from autogluon import TabularPrediction as task
  
  data_path = "./data/cholesterol/cholesterol"
  
  label_column = "chol"
  
  fold1 = pd.read_csv(data_path + "-fold1.csv")
  fold2 = pd.read_csv(data_path + "-fold2.csv")
  fold3 = pd.read_csv(data_path + "-fold3.csv")
  fold4 = pd.read_csv(data_path + "-fold4.csv")
  fold5 = pd.read_csv(data_path + "-fold5.csv")
  fold6 = pd.read_csv(data_path + "-fold6.csv")
  fold7 = pd.read_csv(data_path + "-fold7.csv")
  fold8 = pd.read_csv(data_path + "-fold8.csv")
  fold9 = pd.read_csv(data_path + "-fold9.csv")
  fold10 = pd.read_csv(data_path + "-fold10.csv")
  
  folds = [fold1, fold2, fold3, fold4, fold5, fold6, fold7, fold8, fold9, fold10]
  
  
  leaderboards = []
  metrics = []
  
  for x in range(0, 10):
      fold_folder = "./data/cholesterol/autogluon/fold" + str(x + 1)
      folds = [fold1, fold2, fold3, fold4, fold5,
               fold6, fold7, fold8, fold9, fold10]
      test_df = folds[x]
      del folds[x]
      train_df = pd.concat(folds)
      train_data = task.Dataset(train_df)
      start = datetime.now().strftime("%H:%M:%S")
      predictor = task.fit(
          train_data=train_data,
          label=label_column,
          eval_metric="mean_absolute_error",
          num_bagging_folds=5,
          output_directory=fold_folder,
      )
      end = datetime.now().strftime("%H:%M:%S")
      predictor.leaderboard().to_csv(fold_folder + "/leaderboard.csv")
      test_data = task.Dataset(test_df)
      y_test = test_data[label_column]
      y_pred = predictor.predict(test_data)
      perf = predictor.evaluate_predictions(
          y_true=y_test.to_numpy(), y_pred=y_pred, auxiliary_metrics=True
      )
      perf = dict(perf)
      perf["start"] = start
      perf["end"] = end
      perf = json.dumps(perf)
      f = open(fold_folder + "/perf.json", "w")
      f.write(perf)
      f.close()
  