code: def predict(self, texts, target_words=None, n=1):
      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],
                  pad_token_id=50256,
                  min_length=self.min_length,
                  max_length=self.max_length,
                  temperature=self.temperature,
                  top_k=self.top_k,
                  top_p=self.top_p,
                  do_sample=True,
                  num_return_sequences=1,
                  num_workers=1,
              )
              if isinstance(predict_result, list):
                  results.extend([y for x in predict_result for y in x])
  
      return [r["generated_text"] for r in results]
  
file_path: nlpaug/nlpaug/model/lang_models/text_generation_transformers.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
label_desc: Use image node_id 4 for calling the TextGenTransformers obj's underlying member method code with example usage: obj.predict(texts, target_words, n) and returns: unknown