class_docstr: Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
class_docstr_tok: ["Construct","a","ChatGLM","tokenizer",".","Based","on","byte-level","Byte-Pair-Encoding",".","Args",":","vocab_file","(","`","str","`",")",":","Path","to","the","vocabulary","file","."]
code: def preprocess_text(self, inputs):
      if self.remove_space:
          outputs = " ".join(inputs.strip().split())
      else:
          outputs = inputs
  
      if self.do_lower_case:
          outputs = outputs.lower()
  
      return outputs
  
imports: import os
  from typing import Dict, List, Optional, Union
  import numpy
  import sentencepiece
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import BatchEncoding, EncodedInput
  from transformers.utils import PaddingStrategy, logging
label_desc: Use image node_id 6 for calling the ChatGLMTokenizer obj's underlying member method code with example usage: obj.preprocess_text(inputs) and returns: outputs