code: def filter_options(self, texts, word, options, threshold=5):
      if type(texts) != list:
          texts = [texts]
      options = options + [word]
      in_all = set(options)
      orig_ret = []
      for text in texts:
          masked = re.sub(
              r"\b%s\b" % re.escape(word),
              self.tokenizer.mask_token,
              text,
          )
          if masked == text:
              continue
          ret = self.unmask(masked, beam_size=100, candidates=options)
          if not ret:
              in_all = in_all.intersection(set())
              continue
          non_word = [
              x
              for x in ret
              if np.all(
                  [
                      y not in [self.tokenizer.unk_token, word]
                      for y in x[0]
                  ]
              )
          ]
          score = [
              x
              for x in ret
              if np.all(
                  [y in [word, self.tokenizer.unk_token] for y in x[0]]
              )
          ]
          if score:
              score = score[0][-1]
          # this will happen when the word is not in the vocabulary, in which case we don't look at the score
          else:
              score = 0
          new_ret = [
              (x[0], x[1], score - x[2])
              for x in non_word
              if score - x[2] < threshold
          ]
          # print(text)
          # print(new_ret)
          # print()
          if text == texts[0]:
              orig_ret = new_ret
          in_all = in_all.intersection(set([x[0][0] for x in new_ret]))
      return [x for x in orig_ret if x[0][0] in in_all]
  
imports: from transformers import AutoTokenizer, AutoModelForMaskedLM
  import collections
  import itertools
  import numpy
  import re
  from transformers import GPT2Config
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
  from tqdm.auto import tqdm
  import torch
  import torch.nn.functional
  from pattern.en import wordnet, pluralize
  import requests
  import json
label_desc: Use image node_id 11 for calling the TextGenerator obj's underlying member method code with example usage: obj.filter_options(texts, word, options, threshold) and returns: unknown