code: def _create_formatted_finetuning_example(
      training_example: TrainingExample,
      formatter_configs: FormatterConfigs,
      category_indices_to_include_in_llama_guard_prompt: List[int],
  ) -> str:
      if (
          formatter_configs.llama_guard_prompt_configs.should_shuffle_category_codes
      ):
          random.shuffle(
              category_indices_to_include_in_llama_guard_prompt
          )
      else:
          category_indices_to_include_in_llama_guard_prompt = sorted(
              category_indices_to_include_in_llama_guard_prompt
          )
  
      llama_guard_prompt = _create_llama_guard_prompt(
          training_example,
          category_indices_to_include_in_llama_guard_prompt,
          formatter_configs,
      )
  
      llama_guard_generation = _create_llama_guard_generation(
          training_example,
          category_indices_to_include_in_llama_guard_prompt,
          formatter_configs,
      )
  
      return f"{llama_guard_prompt} {llama_guard_generation}"
  
imports: import copy
  import random
  from dataclasses import dataclass
  from enum import Enum
  from typing import Dict, List, Literal, Optional, Sequence
label_desc: Use image node_id 3 for calling a global function with example usage: _create_formatted_finetuning_example(training_example, formatter_configs, category_indices_to_include_in_llama_guard_prompt) and returns: llama_guard_prompt, str, llama_guard_generation