code: def test_existing_save_path_but_empty_folder(save_path):
      dataset = PetFinderDataset()
      hyperparameters = {
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
          "model.names": ["timm_image", "hf_text", "fusion_mlp"],
          "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
          "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
      }
  
      abs_path = os.path.abspath(os.path.expanduser(save_path))
      if os.path.exists(abs_path):
          shutil.rmtree(abs_path)
      os.makedirs(abs_path, exist_ok=True)
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
          path=save_path,
      )
      predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=10,
      )
  
      shutil.rmtree(abs_path)
      os.makedirs(abs_path)
  
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
      predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          save_path=save_path,
          time_limit=10,
      )
  
      shutil.rmtree(abs_path)
  
file_path: autogluon/multimodal/tests/unittests/others/test_save_path.py
imports: import os
  import shutil
  import pytest
  from autogluon.multimodal import MultiModalPredictor
  from ..utils.unittest_datasets import PetFinderDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_existing_save_path_but_empty_folder(save_path) without return types