code: def prepare_xlnet_input(args, _, tokenizer, prompt_text):
      prefix = (
          args.prefix
          if args.prefix
          else args.padding_text
          if args.padding_text
          else PREFIX
      )
      prompt_text = prefix + prompt_text
      return prompt_text
  
imports: import argparse
  import inspect
  import logging
  from typing import Tuple
  import torch
  from accelerate import PartialState
  from accelerate.utils import set_seed
  from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, CTRLLMHeadModel, CTRLTokenizer, GenerationMixin, GPT2LMHeadModel, GPT2Tokenizer, GPTJForCausalLM, LlamaForCausalLM, LlamaTokenizer, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, OPTForCausalLM, TransfoXLLMHeadModel, TransfoXLTokenizer, XLMTokenizer, XLMWithLMHeadModel, XLNetLMHeadModel, XLNetTokenizer
  from transformers.modeling_outputs import CausalLMOutputWithPast
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_xlnet_input(args, _, tokenizer, prompt_text) and returns: prompt_text