class_docstr: Runs end-to-end tokenization: punctuation splitting + wordpiece
class_docstr_tok: ["Runs","end-to-end","tokenization",":","punctuation","splitting","+","wordpiece"]
docstr: Converts a sequence of tokens into ids using the vocab.
docstr_tok: ["Converts","a","sequence","of","tokens","into","ids","using","the","vocab","."]
code: def convert_tokens_to_ids(self, tokens):
      """Converts a sequence of tokens into ids using the vocab."""
      ids = []
      for token in tokens:
          ids.append(self.vocab[token])
      if len(ids) > self.max_len:
          raise ValueError(
              "Token indices sequence length is longer than the specified maximum "
              " sequence length for this BERT model ({} > {}). Running this"
              " sequence through BERT will result in indexing errors".format(
                  len(ids), self.max_len
              )
          )
      return ids
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import unicodedata
  import os
  import logging
  from .file_utils import cached_path
label_desc: Use image node_id 3 for calling the BertTokenizer obj's underlying member method code with example usage: obj.convert_tokens_to_ids(tokens) and returns: ids