code: def __call__(self, image, target):
      if random.random() < self.flip_prob:
          image = functional.hflip(image)
          target = functional.hflip(target)
      return image, target
  
imports: import json
  import logging
  import os
  import random
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  import transformers
  from transformers import AutoConfig, AutoImageProcessor, 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 2 for calling the RandomHorizontalFlip obj's underlying member method code with example usage: obj.__call__(image, target) and returns: image,  target