code: def predict(self, texts, target_words=None, n=1):
      results = []
  
      predict_results = []
      with torch.no_grad():
          for i in range(0, len(texts), self.batch_size):
              predict_result = self.model(
                  texts[i : i + self.batch_size], num_workers=1
              )
              if (
                  isinstance(predict_result, list)
                  and len(predict_result) > 0
              ):
                  if isinstance(predict_result[0], list):
                      predict_results.extend(predict_result)
                  else:
                      predict_results.extend([predict_result])
  
      for result in predict_results:
          temp_results = []
          for r in result:
              token = r["token_str"]
              if self.model_type in ["bert"] and token.startswith("##"):
                  continue
              # subword came without space for roberta but not normal subowrd prefix
              if self.model_type in [
                  "roberta",
                  "bart",
              ] and not token.startswith(" "):
                  continue
  
              temp_results.append(token)
  
          results.append(temp_results)
  
      return results
  
file_path: nlpaug/nlpaug/model/lang_models/fill_mask_transformers.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 10 for calling the FmTransformers obj's underlying member method code with example usage: obj.predict(texts, target_words, n) and returns: results