code: def pad_if_smaller(img, size, fill=0):
      min_size = min(img.size)
      if min_size < size:
          original_width, original_height = img.size
          pad_height = (
              size - original_height if original_height < size else 0
          )
          pad_width = (
              size - original_width if original_width < size else 0
          )
          img = functional.pad(
              img, (0, 0, pad_width, pad_height), fill=fill
          )
      return img
  
imports: import json
  import logging
  import os
  import random
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import numpy
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  import evaluate
  import transformers
  from huggingface_hub import hf_hub_download
  from transformers import AutoConfig, AutoFeatureExtractor, AutoModelForSemanticSegmentation, HfArgumentParser, Trainer, TrainingArguments, default_data_collator
  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
label_desc: Use image node_id 1 for calling a global function with example usage: pad_if_smaller(img, size, fill) and returns: img