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","."]
docstr: Obtain one sample's images and customized them for a specific model.
  
  Parameters
  ----------
  images
      Images of one sample.
  feature_modalities
      The modality of the feature columns.
  is_training
      Whether to process images in the training mode.
  
  Returns
  -------
  A dictionary containing one sample's processed images and their number.
docstr_tok: ["Obtain","one","sample","'s","images","and","customized","them","for","a","specific","model",".","Parameters","--","--","--","--","--","images","Images","of","one","sample",".","feature_modalities","The","modality","of","the","feature","columns",".","is_training","Whether","to","process","images","in","the","training","mode",".","Returns","--","--","--","-","A","dictionary","containing","one","sample","'s","processed","images","and","their","number","."]
code: def __call__(
      self,
      images: Dict[str, List[str]],
      feature_modalities: Dict[str, Union[int, float, list]],
      is_training: bool,
  ) -> Dict:
      """
      Obtain one sample's images and customized them for a specific model.
  
      Parameters
      ----------
      images
          Images of one sample.
      feature_modalities
          The modality of the feature columns.
      is_training
          Whether to process images in the training mode.
  
      Returns
      -------
      A dictionary containing one sample's processed images and their number.
      """
      images = {
          k: [v] if isinstance(v, str) else v for k, v in images.items()
      }
      return self.process_one_sample(
          images, feature_modalities, is_training
      )
  
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 11 for calling the SemanticSegImageProcessor obj's underlying member method code with example usage: obj.__call__(images, feature_modalities, is_training) and returns: self