class_docstr: Prepare image data for the model specified by "prefix". For multiple models requiring image data,
  we need to create a ImageProcessor for each related model so that they will have independent input.
class_docstr_tok: ["Prepare","image","data","for","the","model","specified","by","``","prefix","''",".","For","multiple","models","requiring","image","data",",","we","need","to","create","a","ImageProcessor","for","each","related","model","so","that","they","will","have","independent","input","."]
code: def __setstate__(self, state):
      self.__dict__ = state
      if "train_transform_types" in state:  # backward compatible
          self.train_transforms = list(self.train_transform_types)
      if "val_transform_types" in state:
          self.val_transforms = list(self.val_transform_types)
  
      self.train_processor = construct_image_processor(
          image_transforms=self.train_transforms,
          size=self.size,
          normalization=self.normalization,
      )
  
imports: import ast
  import logging
  import warnings
  from io import BytesIO
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import PIL
  import torch
  from omegaconf import ListConfig
  from PIL import ImageFile
  from torch import nn
  from torchvision import transforms
  from .utils import construct_image_processor, image_mean_std
  from ..constants import AUTOMM, CLIP, CLIP_IMAGE_MEAN, CLIP_IMAGE_STD, COLUMN, IMAGE, IMAGE_BYTEARRAY, IMAGE_PATH, IMAGE_VALID_NUM, TIMM_IMAGE
  from ..models.timm_image import TimmAutoModelForImagePrediction
  from .collator import PadCollator, StackCollator
  from .utils import extract_value_from_config
label_desc: Use image node_id 10 for calling the ImageProcessor obj's underlying member method code with example usage: obj.__setstate__(state) without return types