code: def get_label_list(raw_dataset, split="train") -> List[str]:
      """Get the list of labels from a mutli-label dataset"""
  
      if isinstance(raw_dataset[split]["label"][0], list):
          label_list = [
              label
              for sample in raw_dataset[split]["label"]
              for label in sample
          ]
          label_list = list(set(label_list))
      else:
          label_list = raw_dataset[split].unique("label")
      # we will treat the label list as a list of string instead of int, consistent with model.config.label2id
      label_list = [str(label) for label in label_list]
      return label_list
  
imports: import logging
  import os
  import random
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import List, Optional
  import datasets
  import evaluate
  import numpy
  import transformers
  from datasets import Value, load_dataset
  from peft import LoraConfig, TaskType, get_peft_model
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, default_data_collator, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTTrainer, ORTTrainingArguments
label_desc: Use image node_id 1 for calling a global function with example usage: get_label_list(raw_dataset, split) and returns: label_list