code: def test_dump_hf_text():
      model_dump_path = "./hf_text_test"
      base_model_name = "nlpaueb/legal-bert-small-uncased"
      dataset = ALL_DATASETS["ae"]
      predictor_1 = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
      hyperparameters = {
          "optimization.max_epochs": 1,
          "model.hf_text.checkpoint_name": base_model_name,
      }
      predictor_1.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=5,
          seed=42,
      )
      predictor_1.dump_model(save_path=model_dump_path)
  
      model = transformers.AutoModel.from_pretrained(
          f"{model_dump_path}/hf_text"
      )
      assert isinstance(
          model, transformers.models.bert.modeling_bert.BertModel
      )
      predictor_2 = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
      hyperparameters = {
          "optimization.max_epochs": 1,
          "model.hf_text.checkpoint_name": f"{model_dump_path}/hf_text",
      }
      predictor_2.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=5,
          seed=42,
      )
  
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 2 for calling a global function with example usage: test_dump_hf_text() without return types