code: def testHuggingFaceFeaturizer():
      # NOTE: The test depends on the the pretrained vocabulary
      # (seyonec/PubChem10M_SMILES_BPE_60k). If the pretrained vocabulary is modified
      # (which can be since it is an external resource), the test might fail.
      from transformers import RobertaTokenizerFast
  
      hf_tokenizer = RobertaTokenizerFast.from_pretrained(
          "seyonec/PubChem10M_SMILES_BPE_60k"
      )
  
      featurizer = HuggingFaceFeaturizer(tokenizer=hf_tokenizer)
      output = featurizer.featurize(["CC(=O)C", "CC"])
      assert len(output) == 2
      assert output[0]["input_ids"] == [0, 262, 263, 51, 13, 39, 2]
      assert output[0]["attention_mask"] == [1, 1, 1, 1, 1, 1, 1]
  
      assert output[1]["input_ids"] == [0, 262, 2]
      assert output[1]["attention_mask"] == [1, 1, 1]
  
label_desc: Use image node_id 1 for calling a global function with example usage: testHuggingFaceFeaturizer() without return types