code: def test_predictor_skip_final_val():
      download_dir = "./"
      save_path = "petfinder_checkpoint"
      train_df, tune_df = shopee_dataset(download_dir=download_dir)
      if os.path.isdir(save_path):
          shutil.rmtree(save_path)
      predictor = MultiModalPredictor(label="label", path=save_path)
      hyperparameters = {
          "model.names": ["timm_image"],
          "model.timm_image.checkpoint_name": "ghostnet_100",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
          "optimization.top_k_average_method": "best",
          "optimization.val_check_interval": 1.0,
          "optimization.skip_final_val": True,
      }
      predictor.fit(
          train_data=train_df,
          tuning_data=tune_df,
          hyperparameters=hyperparameters,
          time_limit=5,
      )
      predictor_new = MultiModalPredictor.load(path=save_path)
      assert isinstance(
          predictor_new._learner._model, TimmAutoModelForImagePrediction
      )
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_predictor_advanced.py
imports: import os
  import shutil
  import numpy.testing
  import pytest
  from datasets import load_dataset
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import BIT_FIT, IA3, IA3_BIAS, IA3_LORA, LORA_BIAS, LORA_NORM, NORM_FIT
  from autogluon.multimodal.models.timm_image import TimmAutoModelForImagePrediction
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..utils.unittest_datasets import AmazonReviewSentimentCrossLingualDataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_predictor_skip_final_val() without return types