code: def load_model_hook(models, input_dir):
      while len(models) > 0:
          model = models.pop()
          # pop models so that they are not loaded again
          if hasattr(model, "active_adapter") and hasattr(
              model, "load_adapter"
          ):
              model.load_adapter(
                  input_dir, model.active_adapter, is_trainable=True
              )
  
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import evaluate
  import torch
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import DatasetDict, load_dataset
  from huggingface_hub import Repository, create_repo
  from torch import nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import AutoModel, AutoTokenizer, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import get_full_repo_name
  from peft import LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 3 for calling a global function with example usage: load_model_hook(models, input_dir) without return types