code: def test_v0_4_0_checkpoint_hf_text():
      dataset = AmazonReviewSentimentCrossLingualDataset()
      sha1_hash = "4ba096cdf6bd76c06386f2c27140db055e59c91b"
      checkpoint_name = "mdeberta-v3-base-checkpoint"
      save_path = os.path.join(
          get_home_dir(), "checkpoints_0.4.0_backward_compatible"
      )
      file_path = os.path.join(save_path, f"{checkpoint_name}.zip")
      checkpoint_path = os.path.join(save_path, checkpoint_name)
      if os.path.exists(save_path):
          shutil.rmtree(save_path)
      download(
          url=f"s3://automl-mm-bench/unit-tests-0.4/checkpoints/{checkpoint_name}.zip",
          path=file_path,
          sha1_hash=sha1_hash,
      )
      protected_zip_extraction(
          file_path,
          sha1_hash=sha1_hash,
          folder=save_path,
      )
      predictor = MultiModalPredictor.load(checkpoint_path)
      verify_predictor_save_load(
          predictor, dataset.test_df.sample(4), cls=MultiModalPredictor
      )
  
      # continuous training
      predictor.fit(
          dataset.train_df,
          presets="multilingual",
          time_limit=10,
          hyperparameters={
              "optimization.top_k_average_method": "uniform_soup",
              "env.num_gpus": -1,
          },
      )
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_backward_compatibility.py
imports: import os
  import shutil
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.utils import download
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..predictor.test_predictor import verify_predictor_save_load
  from ..utils.unittest_datasets import AmazonReviewSentimentCrossLingualDataset
  from ..utils.utils import get_home_dir, protected_zip_extraction
label_desc: Use image node_id 1 for calling a global function with example usage: test_v0_4_0_checkpoint_hf_text() without return types