code: def test_v0_6_2_checkpoint_timm_image():
      train_df, test_df = shopee_dataset("./ag_automm_tutorial_imgcls")
  
      file_path = (
          "convnext_nano_shopee_0.6.2_backward_compatible_test.zip"
      )
      save_path = os.path.join(
          get_home_dir(), "checkpoints_0.6.2_backward_compatible"
      )
      if os.path.exists(save_path):
          shutil.rmtree(save_path)
      checkpoint_path = os.path.join(save_path, "convnext_nano_shopee")
      sha1_hash = "b0b36fd076b3e0ab599f917c9b2924aef84a02b6"
      download(
          url="https://automl-mm-bench.s3.amazonaws.com/unit-tests/convnext_nano_shopee_0.6.2_backward_compatible_test.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)
      assert (
          predictor._learner._config.model.timm_image.image_size is None
      )
      verify_predictor_save_load(
          predictor, test_df, cls=MultiModalPredictor
      )
  
      predictor.fit(
          train_df,
          hyperparameters={"model.timm_image.image_size": 288},
          time_limit=10,
      )
      assert (
          predictor._learner._config.model.timm_image.image_size == 288
      )
  
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 2 for calling a global function with example usage: test_v0_6_2_checkpoint_timm_image() without return types