code: def save_gpt_evaluation_results(
      model_name: str,
      gpt_evaluation_results: Dict[str, Any],
      save_path: str,
  ) -> Dict[str, Any]:
      """
      Save evaluation results for different categories for one model.
  
      Args:
          model_name: name of the model for saving evaluation results.
          gpt_evaluation_results: evaluations results for all of the model answers.
          save_path: path to save GPT evaluation statistics.
      """
  
      all_evaluations = []
      for category, evaluations in gpt_evaluation_results.items():
          jdump(
              evaluations,
              os.path.join(
                  save_path,
                  model_name,
                  f"{category}_evaluation_results.json",
              ),
          )
          all_evaluations.extend(evaluations)
  
      jdump(
          all_evaluations,
          os.path.join(
              save_path, f"{model_name}_evaluation_results.json"
          ),
      )
  
      return all_evaluations
  
imports: import concurrent.futures
  import os
  import re
  import time
  from copy import deepcopy
  from typing import Any, Dict, List
  import matplotlib.pyplot
  import numpy
  import openai
  import pandas
  import seaborn
  import tqdm
  from colossal_eval.utils import jdump, jload
label_desc: Use image node_id 13 for calling a global function with example usage: save_gpt_evaluation_results(model_name, gpt_evaluation_results, save_path) and returns: all_evaluations