code: def test_vectorize(self):
      data = (
          "[CLS] I love my dog. [SEP] [CLS] He is the best. [SEP]",
          "[CLS] Dog. [SEP]",
      )
      tokenizer_encoder = BertTokenizer.from_pretrained(
          "bert-base-uncased"
      )
      tokenizer_decoder = KerasTokenizer()
      tokenizer_decoder.fit([data[1]])
      vectorizer = BertVectorizer(
          tokenizer_encoder,
          tokenizer_decoder,
          max_input_len=50,
          max_output_len=3,
      )
  
      data_vectorized = vectorizer(data)
      expected = (
          [
              101,
              1045,
              2293,
              2026,
              3899,
              1012,
              102,
              101,
              2002,
              2003,
              1996,
              2190,
              1012,
              102,
          ],
          [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
          [1, 2, 3],
      )
      self.assertEqual(expected, data_vectorized)
      input_decoded = vectorizer.decode_input(expected[0])
      expected = (
          "[CLS] i love my dog. [SEP] [CLS] he is the best. [SEP]"
      )
      self.assertEqual(expected, input_decoded)
  
imports: import unittest
  from transformers import BertTokenizer
  from headliner.preprocessing.bert_vectorizer import BertVectorizer
  from headliner.preprocessing.keras_tokenizer import KerasTokenizer
label_desc: Use image node_id 1 for calling the TestBertVectorizer obj's underlying member method code with example usage: obj.test_vectorize() without return types