class_docstr: Sentence piece tokenizer.
class_docstr_tok: ["Sentence","piece","tokenizer","."]
code: def __init__(
      self,
      sp_model_path: str = "",
      max_input_text_length: Optional[int] = None,
      use_fb_sentencepiece: Optional[bool] = None,
      lowercase: Optional[bool] = False,
  ):
      self.sp_model_path = sp_model_path
      self.max_input_text_length = max_input_text_length
      self.use_fb_sentencepiece = use_fb_sentencepiece
      self.lowercase = lowercase
      self._load_processor()
      log_class_usage(__class__)
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 1 to create a new SentencePieceTokenizer object from inherited base classes: Tokenizer, CppProcessorMixin with example: obj = SentencePieceTokenizer(sp_model_path, max_input_text_length, use_fb_sentencepiece, lowercase)