class_docstr: Sentencepiece tokenizer with tf.text interface.
code: def __init__(
      self, model, reverse=False, add_bos=False, add_eos=False
  ):
      converted_model = (
          pywrap_model_converter.convert_sentencepiece_model(model)
      )
      converted_model_detokenizer = pywrap_model_converter.convert_sentencepiece_model_for_decoder(
          model
      )
      # Use uint8 tensor as a buffer for the model to avoid any possible changes,
      # for example truncation by '\0'.
      self._converted_model = tf.constant(
          list(converted_model), dtype=tf.uint8
      )
      self._converted_model_detokenizer = tf.constant(
          list(converted_model_detokenizer), dtype=tf.uint8
      )
      self._vocab_size = pywrap_model_converter.get_vocabulary_size(
          converted_model
      )
      self._reverse = reverse
      self._add_bos = add_bos
      self._add_eos = add_eos
  
file_path: text/tensorflow_text/python/ops/fast_sentencepiece_tokenizer.py
imports: import tensorflow.compat.v2
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow_text.core.pybinds import pywrap_model_converter
  from tensorflow.python.framework import load_library
  from tensorflow.python.platform import resource_loader
label_desc: Use image node_id 1 to create a new FastSentencepieceTokenizer object  with example: obj = FastSentencepieceTokenizer(model, reverse, add_bos, add_eos)