code: def preprocess_llama(example, prompter, tokenizer, options):
      full_prompt = prompter.generate_prompt(
          example["instruction"],
          example["input"],
          example["output"],
      )
      tokenized_full_prompt = llama_tokenize(
          full_prompt, tokenizer, options.cutoff_len
      )
      if not options.train_on_inputs:
          user_prompt = prompter.generate_prompt(
              example["instruction"], example["input"]
          )
          tokenized_user_prompt = llama_tokenize(
              user_prompt,
              tokenizer,
              options.cutoff_len,
              add_eos_token=False,
          )
          user_prompt_len = len(tokenized_user_prompt["input_ids"])
          tokenized_full_prompt["labels"] = [
              -100
          ] * user_prompt_len + tokenized_full_prompt["labels"][
              user_prompt_len:
          ]
  
      return tokenized_full_prompt
  
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_llama(example, prompter, tokenizer, options) and returns: tokenized_full_prompt