code: def __init__(self, image_size, mean, std):
      super().__init__()
      self.transforms = torch.nn.Sequential(
          Resize([image_size], interpolation=InterpolationMode.BICUBIC),
          CenterCrop(image_size),
          ConvertImageDtype(torch.float),
          Normalize(mean, std),
      )
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torchvision.io import ImageReadMode, read_image
  from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
  from torchvision.transforms.functional import InterpolationMode
  import transformers
  from transformers import AutoFeatureExtractor, AutoModel, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, 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
label_desc: Use image node_id 1 to create a new Transform object from inherited base classes: torch.nn with example: obj = Transform(image_size, mean, std)