code: def test_predictor_with_bart(checkpoint_name, efficient_finetune):
      train_data = (
          load_dataset("glue", "mrpc")["train"]
          .to_pandas()
          .drop("idx", axis=1)
          .sample(500)
      )
      test_data = (
          load_dataset("glue", "mrpc")["validation"]
          .to_pandas()
          .drop("idx", axis=1)
          .sample(20)
      )
      predictor = MultiModalPredictor(label="label")
      predictor.fit(
          train_data,
          hyperparameters={
              "model.hf_text.checkpoint_name": checkpoint_name,
              "optimization.max_epochs": 1,
              "optimization.efficient_finetune": efficient_finetune,
              "optimization.top_k": 1,
              "optimization.top_k_average_method": "best",
              "env.batch_size": 2,
          },
          time_limit=20,
      )
      predictor.predict(test_data)
  
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 5 for calling a global function with example usage: test_predictor_with_bart(checkpoint_name, efficient_finetune) without return types