code: def get_prompt_with_target(self, target):
      prompt = self.get_prompt()
      prompt_with_target = []
  
      # Some dataset provides multiple target answers.
      # This will make it difficult when we calculate loss.
      # We convert target into list[str] first if the question only has one target answer.
      target_answers = []
      if isinstance(target, str):
          target_answers = [target]
      else:
          target_answers = target
  
      for target_answer in target_answers:
          if self.sep_style == SeparatorStyle.ADD_BOS_EOS_TOKEN:
              prompt_with_target.append(prompt + target_answer)
          elif self.sep_style == SeparatorStyle.ALPACA:
              prompt_with_target.append(prompt + target_answer)
          elif self.sep_style == SeparatorStyle.PLAIN:
              prompt_with_target.append(prompt + target_answer)
          elif self.sep_style == SeparatorStyle.YAYI:
              prompt_with_target.append(prompt + target_answer)
          else:
              raise ValueError(f"Invalid style: {self.sep_style}")
  
      return prompt_with_target
  
file_path: ColossalAI/applications/ColossalEval/colossal_eval/utils/conversation.py
imports: import dataclasses
  from enum import Enum, auto
  from typing import Dict, List, Optional, Tuple
  from transformers import AutoTokenizer
label_desc: Use image node_id 3 for calling the Conversation obj's underlying member method code with example usage: obj.get_prompt_with_target(target) and returns: prompt_with_target