code: def vectorize(
      self, title: str, max_seq_length: int
  ) -> Tuple[np.array, np.array, np.array]:
      tokens = ["[CLS]"] + self.tokenizer.tokenize(title) + ["[SEP]"]
      token_type_ids, input_ids = [0] * len(
          tokens
      ), self.tokenizer.convert_tokens_to_ids(tokens)
      attention_mask, padding = [1] * len(input_ids), [0] * (
          max_seq_length - len(input_ids)
      )
      input_ids, attention_mask, token_type_ids = [
          x + padding
          for x in [input_ids, attention_mask, token_type_ids]
      ]
      return (
          np.array(input_ids),
          np.array(attention_mask),
          np.array(token_type_ids),
      )
  
file_path: transfer-nlp/experiments/bert/bert.py
imports: import logging
  from pathlib import Path
  from typing import Tuple
  import numpy
  import pandas
  from pytorch_pretrained_bert import BertTokenizer, BertForSequenceClassification, BertAdam
  from tqdm import tqdm
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import Vocabulary
  from transfer_nlp.plugins.config import register_plugin, ExperimentConfig
label_desc: Use image node_id 2 for calling the BertVectorizer obj's underlying member method code with example usage: obj.vectorize(title, max_seq_length) and returns: np,  np,  np