code: def test_smiles_featurize():
      """Test the .featurize method, which will convert the dictionary output to an array
  
      Checks that all SMILES are featurized and that each featurization
      contains input_ids and attention_mask
      """
      from deepchem.feat.roberta_tokenizer import RobertaFeaturizer
  
      smiles = [
          "Cn1c(=O)c2c(ncn2C)n(C)c1=O",
          "CC(=O)N1CN(C(C)=O)C(O)C1O",
      ]
      long_molecule_smiles = [
          "CCCCCCCCCCCCCCCCCCCC(=O)OCCCNC(=O)c1ccccc1SSc1ccccc1C(=O)NCCCOC(=O)CCCCCCCCCCCCCCCCCCC"
      ]
      featurizer = RobertaFeaturizer.from_pretrained(
          "seyonec/SMILES_tokenized_PubChem_shard00_160k"
      )
      max_length = 100
      feat_kwargs = {
          "add_special_tokens": True,
          "truncation": True,
          "padding": "max_length",
          "max_length": max_length,
      }
      feats = featurizer.featurize(smiles, **feat_kwargs)
      assert len(feats) == 2
      assert all([len(f) == 2 for f in feats])
      assert all([len(f[0]) == max_length for f in feats])
  
      long_feat = featurizer.featurize(
          long_molecule_smiles, **feat_kwargs
      )
      assert len(long_feat) == 1
      assert len(long_feat[0]) == 2  # the tokens and attention mask
      assert len(long_feat[0][0]) == 100
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_smiles_featurize() without return types