code: def test_repeated_split(self):
      REPEATS = 3
      FOLDS = 1
  
      a = AutoML(
          results_path=self.automl_dir,
          total_time_limit=10,
          algorithms=["Random Forest"],
          train_ensemble=False,
          validation_strategy={
              "validation_type": "split",
              "repeats": REPEATS,
              "shuffle": True,
              "stratify": True,
          },
          start_random_models=1,
      )
  
      X, y = datasets.make_classification(
          n_samples=100,
          n_features=5,
          n_informative=4,
          n_redundant=1,
          n_classes=2,
          n_clusters_per_class=3,
          n_repeated=0,
          shuffle=False,
          random_state=0,
      )
      X = pd.DataFrame(X, columns=[f"f_{i}" for i in range(X.shape[1])])
  
      a.fit(X, y)
  
      result_files = os.listdir(
          os.path.join(self.automl_dir, "1_Default_RandomForest")
      )
      cnt = 0
      for repeat in range(REPEATS):
          for fold in range(FOLDS):
              learner_name = construct_learner_name(
                  fold, repeat, REPEATS
              )
              self.assertTrue(
                  f"{learner_name}.random_forest" in result_files
              )
              self.assertTrue(
                  f"{learner_name}_training.log" in result_files
              )
              cnt += 1
      self.assertTrue(cnt, 3)
  
file_path: mljar-supervised/tests/tests_automl/test_repeated_validation.py
imports: import os
  import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import shutil
  from sklearn import datasets
  from supervised import AutoML
  from supervised.utils.common import construct_learner_name
  from supervised.algorithms.random_forest import additional
  from supervised.algorithms.xgboost import additional
label_desc: Use image node_id 3 for calling the AutoMLRepeatedValidationTest obj's underlying member method code with example usage: obj.test_repeated_split() without return types