code: def test_fusion_model_dump():
      model_dump_path = "./test_fusion_models"
      dataset = ALL_DATASETS["petfinder"]
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
      hyperparameters = {
          "optimization.max_epochs": 1,
          "model.names": ["timm_image", "hf_text", "fusion_mlp"],
          "model.timm_image.checkpoint_name": "ghostnet_100",
          "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
      }
      predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=5,
          seed=42,
      )
      predictor.dump_model(save_path=model_dump_path)
      hf_text_dir = f"{model_dump_path}/hf_text"
      timm_image_dir = f"{model_dump_path}/timm_image"
      assert (
          os.path.exists(hf_text_dir)
          and (len(os.listdir(hf_text_dir)) > 2) == True
      )
      assert (
          os.path.exists(timm_image_dir)
          and (len(os.listdir(timm_image_dir)) == 2) == True
      )
  
file_path: autogluon/multimodal/tests/unittests/others/test_dump_model.py
imports: import os
  import pytest
  import timm
  import transformers
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..utils.unittest_datasets import AEDataset, PetFinderDataset
label_desc: Use image node_id 3 for calling a global function with example usage: test_fusion_model_dump() without return types