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: The batch equivalent of ``encode``.
docstr_tok: ["The","batch","equivalent","of","``","encode","``","."]
code: def batch_encode(self, input_text_list):
      """The batch equivalent of ``encode``."""
      input_text_list = list(map(self._process_text, input_text_list))
      encodings = self.encode_batch(
          input_text_list,
          add_special_tokens=False,
      )
      return [x.ids for x in encodings]
  
file_path: TextAttack/textattack/models/tokenizers/glove_tokenizer.py
imports: import json
  import tempfile
  import tokenizers
label_desc: Use image node_id 4 for calling the GloveTokenizer obj's underlying member method code with example usage: obj.batch_encode(input_text_list) and returns: unknown