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 mask_label_key(self):
      return f"{self.prefix}_{MASK_LABEL}"
  
imports: import logging
  import random
  import warnings
  from io import BytesIO
  from typing import Dict, List, Optional, Union
  import numpy
  import PIL
  import torch
  from omegaconf import DictConfig
  from PIL import Image, ImageFile
  from torch import nn
  from torchvision import transforms
  from .utils import construct_image_processor, image_mean_std
  from ..constants import CLASS_LABEL, COLUMN, IMAGE, IMAGE_BYTEARRAY, IMAGE_VALID_NUM, LABEL, MASK_LABEL, SEMANTIC_SEGMENTATION_GT, SEMANTIC_SEGMENTATION_IMG
  from .collator import ListCollator, PadCollator, StackCollator
label_desc: Use image node_id 6 for calling the SemanticSegImageProcessor obj's underlying member method code with example usage: obj.mask_label_key() and returns: self, str, MASK_LABEL