code: def image_regression():
      # Prepare data
      from autogluon.multimodal.utils.misc import shopee_dataset
  
      download_dir = "./ag_automm_tutorial_imgcls"
      train_data_path, test_data_path = shopee_dataset(download_dir)
      print(train_data_path)
  
      # Train
      predictor = MultiModalPredictor(
          problem_type="regression", label="label"
      )
      predictor.fit(
          train_data=train_data_path,
          hyperparameters={
              "optimization.max_epochs": 3,
              "env.batch_size": 8,
          },
      )
  
      # Evaluation on test dataset
      test_result = predictor.predict(test_data_path)
      print("test_result:")
      print(test_result)
  
      test_evaluation = predictor.evaluate(test_data_path)
      print("Evaluation result:", test_evaluation)
  
      result = predictor.predict(test_data_path.iloc[0]["image"])
      print("Prediction result for single image:")
      print(result)
  
label_desc: Use image node_id 1 for calling a global function with example usage: image_regression() without return types