class_docstr: Bert Tokenizer built ontop of tensorflow_text.BertTokenizer.
class_docstr_tok: ["Bert","Tokenizer","built","ontop","of","tensorflow_text.BertTokenizer","."]
code: def __init__(self, model_link: str):
      self._model_link = model_link
      self._model = hub.KerasLayer(model_link)
      with eager_mode():
          model = hub.KerasLayer(self._model_link)
          self._vocab_path = (
              model.resolved_object.vocab_file.asset_path.numpy()
          )
          self._do_lower_case = (
              model.resolved_object.do_lower_case.numpy()
          )
  
      with tf.io.gfile.GFile(self._vocab_path, "r") as f:
          lines = f.read().split("\n")
          self._sep_id = lines.index(_SEP)
          self._cls_id = lines.index(_CLS)
          self._pad_id = lines.index(_PAD)
  
file_path: tfx/tfx/examples/bert/utils/bert_tokenizer_utils.py
imports: import os
  import tensorflow
  import tensorflow_hub
  import tensorflow_text
  from tensorflow.python.eager.context import eager_mode
label_desc: Use image node_id 1 to create a new BertPreprocessor object  with example: obj = BertPreprocessor(model_link)