code: def test_fast_bert_tokenizer(
      self,
      text_inputs,
      lower_case_nfd_strip_accents,
      expected_tokens,
      expected_token_ids,
      expected_starts,
      expected_ends,
      vocab=None,
  ):
      text_inputs = constant_op.constant(text_inputs)
      if not vocab:
          vocab = _VOCAB
      # Verify that the tokens are the same.
      tokenizer = fast_bert_tokenizer.FastBertTokenizer(
          vocab=vocab,
          token_out_type=dtypes.string,
          lower_case_nfd_strip_accents=lower_case_nfd_strip_accents,
      )
      result_tokens = tokenizer.tokenize(text_inputs)
      self.assertAllEqual(result_tokens, expected_tokens)
  
      # Verify that the token ids are the same.
      token_id_tokenizer = fast_bert_tokenizer.FastBertTokenizer(
          vocab=vocab,
          token_out_type=dtypes.int64,
          lower_case_nfd_strip_accents=lower_case_nfd_strip_accents,
      )
      result_token_ids = token_id_tokenizer.tokenize(text_inputs)
      self.assertAllEqual(result_token_ids, expected_token_ids)
  
      # Verify that the offsets are the same
      _, starts, ends = token_id_tokenizer.tokenize_with_offsets(
          text_inputs
      )
      self.assertAllEqual(starts, expected_starts)
      self.assertAllEqual(ends, expected_ends)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_text
  from tensorflow.lite.python import interpreter
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.platform import test
  from tensorflow_text.python.ops import fast_bert_tokenizer
label_desc: Use image node_id 2 for calling the FastBertTokenizerTest obj's underlying member method code with example usage: obj.test_fast_bert_tokenizer(text_inputs, lower_case_nfd_strip_accents, expected_tokens, expected_token_ids, expected_starts, expected_ends, vocab) without return types