code: def __init__(
      self,
      url=None,
      model_name="roberta-base",
      prefix_sentence="",
      allow_word_pieces=False,
      **kwargs,
  ):
      self.url = url
      if url is None:
          self.device = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
          # self.tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
          # self.model = BertForMaskedLM.from_pretrained('bert-base-cased')
          self.tokenizer = AutoTokenizer.from_pretrained(model_name)
          self.model = AutoModelForMaskedLM.from_pretrained(model_name)
          self.model.to(self.device)
          self.model.eval()
          self.prefix_sentence = prefix_sentence
          self.prefix_len = len(
              self.tokenizer.encode(
                  prefix_sentence, add_special_tokens=False
              )
          )
          self.allow_word_pieces = allow_word_pieces
          tmp = self.tokenizer.tokenize(" a")[0].split("a")
          assert len(tmp) == 2
          assert tmp[1] == ""
          self.space_prefix = tmp[0]
          if not self.allow_word_pieces:
              self.with_space = torch.tensor(
                  np.array(
                      list(
                          set(
                              [
                                  i
                                  for x, i in self.tokenizer.get_vocab().items()
                                  if x.startswith(self.space_prefix)
                              ]
                          )
                      )
                  ),
                  device=self.device,
              )
              self.with_space_set = set(self.with_space.cpu().numpy())
              self.special_chars = set(
                  [
                      i
                      for x, i in self.tokenizer.get_vocab().items()
                      if not x.strip(self.space_prefix).isalnum()
                  ]
              )
  
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 1 to create a new TextGenerator object from inherited base classes: object with example: obj = TextGenerator(url, model_name, prefix_sentence, allow_word_pieces)