class_docstr: Benchmarks for tokenizers that require extra preprocessing or inputs.
code: def benchmark_wordpiece_tokenizer(self):
      self.input_data = text_ops.WhitespaceTokenizer().tokenize(
          self.input_data
      )
  
      tokenizer = text_ops.WordpieceTokenizer(
          vocab_lookup_table=self._create_table((_BERT_VOCAB_PATH)),
          unknown_token=None,
          token_out_type=dtypes.int64,
      )
      self._run(tokenizer)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import app
  from absl import flags
  import six
  import tensorflow
  from tensorflow.python.client import session
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.ops.ragged import ragged_functional_ops
  from tensorflow.python.platform import benchmark
  from tensorflow_text.python import ops
  from tensorflow_text.python.benchmarks import benchmark_utils
  from tensorflow_text.python.ops.bert_tokenizer import BasicTokenizer
label_desc: Use image node_id 4 for calling the CustomInputTokenizationBenchmark obj's underlying member method code with example usage: obj.benchmark_wordpiece_tokenizer() without return types