code: def test_tune_only_default(self):
      X = np.random.rand(self.rows, 3)
      X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)])
      y = np.random.randint(0, 2, self.rows)
  
      automl = AutoML(
          results_path=self.automl_dir,
          total_time_limit=3,
          algorithms=["Decision Tree"],
          explain_level=0,
          train_ensemble=False,
      )
      automl.fit(X, y)
  
      # Get number of starting models
      n1 = len(
          [x for x in os.listdir(self.automl_dir) if x[0].isdigit()]
      )
  
      progress = json.load(
          open(os.path.join(self.automl_dir, "progress.json"), "r")
      )
      progress["fit_level"] = "default_algorithms"
  
      with open(
          os.path.join(self.automl_dir, "progress.json"), "w"
      ) as fout:
          fout.write(json.dumps(progress, indent=4))
  
      automl = AutoML(
          results_path=self.automl_dir,
          total_time_limit=3,
          algorithms=["Decision Tree", "Xgboost"],
          explain_level=0,
          train_ensemble=False,
      )
      automl.fit(X, y)
      # Get number of models after second fit
      n2 = len(
          [x for x in os.listdir(self.automl_dir) if x[0].isdigit()]
      )
      # number of models should be equal
      # user cannot overwrite parameters
      self.assertEqual(n2, n1)
  
file_path: mljar-supervised/tests/tests_automl/test_restore.py
imports: import os
  import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import shutil
  from supervised import AutoML
  from numpy.testing import assert_almost_equal
  from sklearn import datasets
  from supervised.exceptions import AutoMLException
  from supervised.algorithms.xgboost import additional
label_desc: Use image node_id 2 for calling the AutoMLRestoreTest obj's underlying member method code with example usage: obj.test_tune_only_default() without return types