code: def forward(
      self,
      input_ids,
      token_type_ids,
      position_ids,
      attention_mask,
      max_length=64,
      min_length=1,
      decode_strategy="sampling",
      temperature=1.0,
      top_k=5,
      top_p=1.0,
      num_beams=0,
      length_penalty=1.0,
      early_stopping=False,
      num_return_sequences=1,
  ):
      ids, scores = self.model.generate(
          input_ids=input_ids,
          token_type_ids=token_type_ids,
          position_ids=position_ids,
          attention_mask=attention_mask,
          max_length=max_length,
          min_length=min_length,
          decode_strategy=decode_strategy,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
          num_beams=num_beams,
          length_penalty=length_penalty,
          early_stopping=early_stopping,
          num_return_sequences=num_return_sequences,
      )
  
      return ids, scores
  
file_path: PaddleHub/modules/text/text_generation/unified_transformer-12L-cn/module.py
imports: import contextlib
  from collections import deque
  from typing import List, Union
  import numpy
  import paddle
  import paddle.nn
  from paddlehub.module.module import moduleinfo, serving
  from paddlenlp.data import Pad
  from paddlenlp.transformers import UnifiedTransformerLMHeadModel, UnifiedTransformerTokenizer
  from unified_transformer_12L_cn.utils import select_response
label_desc: Use image node_id 5 for calling the UnifiedTransformer obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, position_ids, attention_mask, max_length, min_length, decode_strategy, temperature, top_k, top_p, num_beams, length_penalty, early_stopping, num_return_sequences) and returns: ids,  scores