code: def test_tokenization_and_featurization():
      task = MLMPremaskedTask(name="mlm_premasked", path_dict={})
      tokenizer = transformers.RobertaTokenizer.from_pretrained(
          "roberta-base"
      )
      example = task.Example(
          guid=None,
          text="Hi, my name is Bob Roberts.",
          masked_spans=[[15, 18]],
      )
      tokenized_example = example.tokenize(tokenizer=tokenizer)
      assert tokenized_example.masked_tokens == [
          "Hi",
          ",",
          "Ġmy",
          "Ġname",
          "Ġis",
          "Ġ",
          "<mask>",
          "ĠRoberts",
          ".",
      ]
      assert tokenized_example.label_tokens == [
          "<pad>",
          "<pad>",
          "<pad>",
          "<pad>",
          "<pad>",
          "<pad>",
          "Bob",
          "<pad>",
          "<pad>",
      ]
  
      data_row = tokenized_example.featurize(
          tokenizer=tokenizer,
          feat_spec=JiantTransformersModelFactory.build_featurization_spec(
              model_type="roberta",
              max_seq_length=16,
          ),
      )
      assert list(data_row.masked_input_ids) == [
          0,
          30086,
          6,
          127,
          766,
          16,
          1437,
          50264,
          6274,
          4,
          2,
          1,
          1,
          1,
          1,
          1,
      ]
      assert list(data_row.masked_lm_labels) == [
          -100,
          -100,
          -100,
          -100,
          -100,
          -100,
          -100,
          25158,
          -100,
          -100,
          -100,
          -100,
          -100,
          -100,
          -100,
          -100,
      ]
  
imports: import transformers
  import jiant.shared.model_resolution
  from jiant.proj.main.modeling.primary import JiantTransformersModelFactory
  from jiant.tasks.retrieval import MLMPremaskedTask
label_desc: Use image node_id 1 for calling a global function with example usage: test_tokenization_and_featurization() without return types