code: def _split_up_prompt(prompt):
      human_texts = re.findall(
          r"\n\nHuman:(.*?)(?=(\n\nAssistant:|$))",
          prompt,
          flags=re.DOTALL,
      )
      assistant_texts = re.findall(
          r"\n\nAssistant:(.*?)(?=(\n\nHuman:|$))",
          prompt,
          flags=re.DOTALL,
      )
      human_texts = [text[0].strip() for text in human_texts]
      assistant_texts = [text[0].strip() for text in assistant_texts]
  
      assert len(human_texts) == len(assistant_texts), prompt
      dialogue = list(zip(human_texts, assistant_texts))
      return dialogue
  
file_path: h2o-llmstudio/llm_studio/app_utils/default_datasets.py
imports: import os
  import random
  import re
  import uuid
  import pandas
  from datasets import load_dataset
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling a global function with example usage: _split_up_prompt(prompt) and returns: dialogue