class_docstr: Runs sentencepiece tokenization. Based on transformers.models.albert.tokenization_albert.AlbertTokenizer.
class_docstr_tok: ["Runs","sentencepiece","tokenization",".","Based","on","transformers.models.albert.tokenization_albert.AlbertTokenizer","."]
code: def preprocess_text(self, inputs):
      if self.remove_space:
          outputs = " ".join(inputs.strip().split())
      else:
          outputs = inputs
      outputs = outputs.replace("``", '"').replace("''", '"')
  
      if not self.keep_accents:
          outputs = unicodedata.normalize("NFKD", outputs)
          outputs = "".join(
              [c for c in outputs if not unicodedata.combining(c)]
          )
      if self.do_lower_case:
          outputs = outputs.lower()
  
      return outputs
  
imports: import collections
  import copy
  import os
  import unicodedata
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...utils import logging
label_desc: Use image node_id 2 for calling the SentencepieceTokenizer obj's underlying member method code with example usage: obj.preprocess_text(inputs) and returns: outputs