code: def pil_loader(path: str):
      with open(path, "rb") as f:
          im = Image.open(f)
          return im.convert("RGB")
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import datasets
  import numpy
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
  import transformers
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, HfArgumentParser, TrainingArguments
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
  from ls_hf_vit_encoder_layer import inject_ls_enc_layer
  from examples.training.huggingface.gcq import LSTrainer, GCQArguments
label_desc: Use image node_id 1 for calling a global function with example usage: pil_loader(path) and returns: im