code: def test_image_predictor_regression(fit_helper):
      from autogluon.multimodal.utils.misc import shopee_dataset
  
      download_dir = "./automm_shopee_data_regression"
      train_data, test_data = shopee_dataset(download_dir)
      train_data = train_data.sample(
          100, random_state=0
      )  # Subsample for faster test
      feature_metadata = FeatureMetadata.from_df(
          train_data
      ).add_special_types({"image": ["image_path"]})
      predictor = TabularPredictor(
          label="label", problem_type="regression"
      ).fit(
          train_data=train_data,
          hyperparameters={
              "AG_IMAGE_NN": {"optimization.max_epochs": 1}
          },
          feature_metadata=feature_metadata,
      )
      leaderboard = predictor.leaderboard(test_data)
      assert len(leaderboard) > 0
  
imports: import pytest
  from autogluon.common.features.feature_metadata import FeatureMetadata
  from autogluon.tabular import TabularPredictor
label_desc: Use image node_id 2 for calling a global function with example usage: test_image_predictor_regression(fit_helper) without return types