code: def predict(self, texts, n=1):
      # Convert to feature
      inputs = self.tokenizer(
          texts, padding="longest", return_tensors=self.return_tensor
      )
      token_inputs = inputs["input_ids"].to(self.device)
      mask_inputs = inputs["attention_mask"].to(self.device)
  
      # Prediction
      min_length = min([len(text) for text in texts])
      min_length = self.get_min_length(min_length)
  
      max_length = max([len(text) for text in texts])
      max_length = self.get_max_length(max_length)
  
      results = []
      with torch.no_grad():
          outputs = self.model.generate(
              input_ids=token_inputs,
              attention_mask=mask_inputs,
              min_length=min_length,
              max_length=max_length,
              num_beams=self.num_beam,
              no_repeat_ngram_size=self.no_repeat_ngram_size,
          )
  
      for target_token_ids in outputs:
          tokens = self.tokenizer.decode(
              target_token_ids,
              skip_special_tokens=self.skip_special_token,
          )
          # Return full sentence only.
          for i in range(len(tokens) - 1, -1, -1):
              if tokens[i] in text_tokenizer.SENTENCE_SEPARATOR:
                  results.append(tokens[: i + 1])
                  break
  
      return results
  
file_path: nlpaug/nlpaug/model/lang_models/bart.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
  import nlpaug.util.text.tokenizer
label_desc: Use image node_id 6 for calling the Bart obj's underlying member method code with example usage: obj.predict(texts, n) and returns: results