code: def _parse_row(prompt_and_response):
      """Extract the anthropic prompt from a prompt and response pair."""
      search_term = "\n\nAssistant:"
      search_term_idx = prompt_and_response["chosen"].rfind(search_term)
      assert (
          search_term_idx != -1
      ), f"Prompt and response does not contain '{search_term}'"
      prompt = prompt_and_response["chosen"][
          : search_term_idx + len(search_term)
      ]
  
      chosen_response = prompt_and_response["chosen"][len(prompt) :]
      rejected_response = prompt_and_response["rejected"][len(prompt) :]
  
      return prompt, chosen_response, rejected_response
  
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 4 for calling a global function with example usage: _parse_row(prompt_and_response) and returns: prompt,  chosen_response,  rejected_response