class_docstr: Represents an abstract base class for SentencePiece tokenizers.
class_docstr_tok: ["Represents","an","abstract","base","class","for","SentencePiece","tokenizers","."]
docstr: :param pathname:
      The pathname of the SentencePiece model file.
  :param control_symbols:
      The list of control symbols to add to the SentencePiece model.
docstr_tok: [":","param","pathname",":","The","pathname","of","the","SentencePiece","model","file",".",":","param","control_symbols",":","The","list","of","control","symbols","to","add","to","the","SentencePiece","model","."]
code: def __init__(
      self,
      pathname: PathLike,
      control_symbols: Optional[Sequence[StringLike]] = None,
  ) -> None:
      """
      :param pathname:
          The pathname of the SentencePiece model file.
      :param control_symbols:
          The list of control symbols to add to the SentencePiece model.
      """
      self.model = SentencePieceModel(pathname, control_symbols)
  
      vocab_info = vocab_info_from_sentencepiece(self.model)
  
      super().__init__(vocab_info)
  
file_path: fairseq2/src/fairseq2/data/text/sentencepiece.py
imports: from typing import TYPE_CHECKING, List, Optional, Sequence, final
  from fairseq2n import DOC_MODE
  from torch import Tensor
  from fairseq2.data.text.text_tokenizer import TextTokenDecoder, TextTokenEncoder, TextTokenizer
  from fairseq2.data.typing import PathLike, StringLike
  from fairseq2.data.vocabulary_info import VocabularyInfo
  from fairseq2.typing import Device, finaloverride
label_desc: Use image node_id 1 to create a new SentencePieceTokenizerBase object from inherited base classes: TextTokenizer with example: obj = SentencePieceTokenizerBase(pathname, control_symbols)