code: def test_tokenizer_use_fast(
      checkpoint_name, use_fast, tokenizer_type
  ):
      dataset = ALL_DATASETS["ae"]()
      metric_name = dataset.metric
  
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=metric_name,
      )
      hyperparameters = {
          "data.categorical.convert_to_text": True,
          "data.numerical.convert_to_text": True,
          "model.hf_text.checkpoint_name": checkpoint_name,
      }
      if use_fast is not None:
          hyperparameters["model.hf_text.use_fast"] = use_fast
  
      with tempfile.TemporaryDirectory() as save_path:
          if os.path.isdir(save_path):
              shutil.rmtree(save_path)
          predictor.fit(
              train_data=dataset.train_df,
              time_limit=5,
              save_path=save_path,
              hyperparameters=hyperparameters,
          )
      assert isinstance(
          predictor._learner._data_processors[TEXT][0].tokenizer,
          tokenizer_type,
      )
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_data_processors.py
imports: import os
  import shutil
  import tempfile
  import pytest
  from transformers import AlbertTokenizer, AlbertTokenizerFast
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import TEXT
  from ..utils.unittest_datasets import AEDataset, HatefulMeMesDataset, IDChangeDetectionDataset, PetFinderDataset
label_desc: Use image node_id 1 for calling a global function with example usage: test_tokenizer_use_fast(checkpoint_name, use_fast, tokenizer_type) without return types