class_docstr: A word-level tokenizer with GloVe 200-dimensional vectors.
  
  Lowercased, since GloVe vectors are lowercased.
class_docstr_tok: ["A","word-level","tokenizer","with","GloVe","200-dimensional","vectors",".","Lowercased",",","since","GloVe","vectors","are","lowercased","."]
docstr: A text input may be a single-input tuple (text,) or multi-input
  tuple (text, text, ...).
  
  In the single-input case, unroll the tuple. In the multi-input
  case, raise an error.
docstr_tok: ["A","text","input","may","be","a","single-input","tuple","(","text",",",")","or","multi-input","tuple","(","text",",","text",",","...",")",".","In","the","single-input","case",",","unroll","the","tuple",".","In","the","multi-input","case",",","raise","an","error","."]
code: def _process_text(self, text_input):
      """A text input may be a single-input tuple (text,) or multi-input
      tuple (text, text, ...).
  
      In the single-input case, unroll the tuple. In the multi-input
      case, raise an error.
      """
      if isinstance(text_input, tuple):
          if len(text_input) > 1:
              raise ValueError(
                  "Cannot use `GloveTokenizer` to encode multiple inputs"
              )
          text_input = text_input[0]
      return text_input
  
file_path: TextAttack/textattack/models/tokenizers/glove_tokenizer.py
imports: import json
  import tempfile
  import tokenizers
label_desc: Use image node_id 2 for calling the GloveTokenizer obj's underlying member method code with example usage: obj._process_text(text_input) and returns: text_input