code: def adjust_length_to_model(length, max_sequence_length):
      if length < 0 and max_sequence_length > 0:
          length = max_sequence_length
      elif 0 < max_sequence_length < length:
          length = max_sequence_length  # No generation bigger than model size
      elif length < 0:
          length = MAX_LENGTH  # avoid infinite loop
      return length
  
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 5 for calling a global function with example usage: adjust_length_to_model(length, max_sequence_length) and returns: length