code: def compute_accuracy(pred: list, gold: list):
      acc = 0.0
      for p, g in zip(pred, gold):
          if p == g:
              acc += 1
  
      return acc / len(pred)
  
imports: import argparse
  import copy
  import logging
  import math
  import os
  import random
  import re
  from pathlib import Path
  import datasets
  import torch
  import transformers
  from accelerate import Accelerator, DistributedType
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import load_dataset
  from huggingface_hub import Repository, create_repo
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import send_example_telemetry
  from transformers.utils.versions import require_version
  from peft import PeftModel
label_desc: Use image node_id 4 for calling a global function with example usage: compute_accuracy(pred, gold) and returns: unknown