class_docstr: Transforms an input by replacing its tokens with words of mask language
  predicted.
  To accelerate transformation for long text, input single sentence to
  language model rather than whole text.
docstr: Loads masked language model to predict candidates.
docstr_tok: ["Loads","masked","language","model","to","predict","candidates","."]
code: def get_model(self):
      r"""
      Loads masked language model to predict candidates.
  
      """
      from transformers import BertTokenizer, BertForMaskedLM
  
      self.tokenizer = BertTokenizer.from_pretrained(
          self.masked_model, do_lower_case=False
      )
      self.model = BertForMaskedLM.from_pretrained(self.masked_model)
      self.model.to(self.device)
      self.model.eval()
  
file_path: textflint/textflint/generation/transformation/UTCN/cn_mlm_suggestion.py
imports: import torch
  from ....common import device
  from ...transformation import CnWordSubstitute
  from ....common.settings import CN_BERT_MODEL_NAME
  from ....common.utils.list_op import trade_off_sub_words
label_desc: Use image node_id 4 for calling the MLMSuggestion obj's underlying member method code with example usage: obj.get_model() without return types