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.
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docstr: Read images, process them, and stack them. One sample can have multiple images,
  resulting in a tensor of (n, 3, size, size), where n <= max_img_num_per_col is the available image number.
  
  Parameters
  ----------
  image_features
      One sample may have multiple image columns in a pd.DataFrame and multiple images
      inside each image column.
  feature_modalities
      What modality each column belongs to.
  is_training
      Whether to process images in the training mode.
  image_mode
      A string which defines the type and depth of a pixel in the image.
      For example, RGB, RGBA, CMYK, and etc.
  
  Returns
  -------
  A dictionary containing one sample's images and their number.
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code: def process_one_sample(
      self,
      image_features: Dict[str, Union[List[str], List[bytearray]]],
      feature_modalities: Dict[str, List[str]],
      is_training: bool,
      image_mode: Optional[str] = "RGB",
  ) -> Dict:
      """
      Read images, process them, and stack them. One sample can have multiple images,
      resulting in a tensor of (n, 3, size, size), where n <= max_img_num_per_col is the available image number.
  
      Parameters
      ----------
      image_features
          One sample may have multiple image columns in a pd.DataFrame and multiple images
          inside each image column.
      feature_modalities
          What modality each column belongs to.
      is_training
          Whether to process images in the training mode.
      image_mode
          A string which defines the type and depth of a pixel in the image.
          For example, RGB, RGBA, CMYK, and etc.
  
      Returns
      -------
      A dictionary containing one sample's images and their number.
      """
      images = []
      zero_images = []
      ret = {}
      column_start = 0
  
      for (
          per_col_name,
          per_col_image_features,
      ) in image_features.items():
          for img_feature in per_col_image_features[
              : self.max_img_num_per_col
          ]:
              with warnings.catch_warnings():
                  warnings.filterwarnings(
                      "ignore",
                      message=(
                          "Palette images with Transparency expressed in bytes should be converted to RGBA images"
                      ),
                  )
                  is_zero_img = False
                  try:
                      if (
                          feature_modalities.get(per_col_name)
                          == IMAGE_BYTEARRAY
                      ):
                          image_feature = BytesIO(img_feature)
                      else:
                          image_feature = img_feature
                      with PIL.Image.open(image_feature) as img:
                          img = img.convert(image_mode)
                  except Exception as e:
                      if self.missing_value_strategy.lower() == "zero":
                          logger.debug(
                              f"Using a zero image due to '{e}'"
                          )
                          img = PIL.Image.new(
                              image_mode,
                              (self.size, self.size),
                              color=0,
                          )
                          is_zero_img = True
                      else:
                          raise e
              if is_training:
                  img = self.train_processor(img)
              else:
                  img = self.val_processor(img)
  
              if is_zero_img:
                  zero_images.append(img)
              else:
                  images.append(img)
  
          if self.requires_column_info:
              # only count the valid images since they are put ahead of the zero images in the below returning
              ret[
                  f"{self.image_column_prefix}_{per_col_name}"
              ] = np.array([column_start, len(images)], dtype=np.int64)
              column_start = len(images)
  
      ret.update(
          {
              self.image_key: torch.tensor([])
              if len(images + zero_images) == 0
              else torch.stack(images + zero_images, dim=0),
              self.image_valid_num_key: len(images),
          }
      )
      return ret
  
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 7 for calling the ImageProcessor obj's underlying member method code with example usage: obj.process_one_sample(image_features, feature_modalities, is_training, image_mode) and returns: ret