code: def check_model_selection_order_does_not_matter(model, dataset):
      best_params = []
      permutations = list(itertools.permutations(model.models))
      datasets = itertools.tee(dataset, len(permutations))
  
      for permutation, dataset in zip(permutations, datasets):
          models = [model.clone() for model in permutation]
          clone = model.clone(new_params={"models": models})
          for x, y in dataset:
              clone.predict_one(x)
              clone.learn_one(x, y)
          best_params.append(clone.best_model._get_params())
  
      # Check that the best params are always the same
      assert all(params == best_params[0] for params in best_params)
  
label_desc: Use image node_id 1 for calling a global function with example usage: check_model_selection_order_does_not_matter(model, dataset) without return types