code: def load_mt_bench_data(cfg: Any) -> pd.DataFrame:
      """Loads MT-BENCH data.
  
      Args:
          cfg: input config
  
      Returns:
          MT-BENCH DataFrame
      """
  
      prompt_column = cfg.dataset.prompt_column[0]
      answer_column = cfg.dataset.answer_column
  
      df = df = pd.read_json(
          "prompts/mt-bench/question.jsonl", lines=True
      )
      df = df.rename(
          columns={"turns": prompt_column, "reference": answer_column}
      )
      df[prompt_column] = df[prompt_column].apply(lambda x: x[0])
      df[answer_column] = (
          df[answer_column]
          .fillna("")
          .apply(lambda x: x[0] if x != "" else x)
      )
  
      return df
  
imports: import logging
  import math
  import os
  from typing import Any, DefaultDict, Dict, List, Optional, Tuple, Union, no_type_check
  import networkx
  import numpy
  import pandas
  import pyarrow.parquet
  import torch
  from sklearn.model_selection import GroupShuffleSplit, train_test_split
  from torch import distributed
  from torch.utils.data import DataLoader, Sampler, SequentialSampler
  from llm_studio.src.datasets.conversation_chain_handler import ConversationChainHandler
  from llm_studio.src.utils.exceptions import LLMDataException
  from llm_studio.src.utils.utils import PatchedAttribute, set_seed
label_desc: Use image node_id 6 for calling a global function with example usage: load_mt_bench_data(cfg) and returns: df