class_docstr: Prepare rois data for mmdetection models.
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_paths
      One sample may have multiple image columns in a pd.DataFrame and multiple images
      inside each image column.
  is_training
      Whether to process images in the training mode.
  
  Returns
  -------
  A dictionary containing one sample's images and their number.
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code: def process_one_loaded_sample(
      self,
      image_paths: Dict[str, List[str]],
      is_training: bool,
  ) -> 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_paths
          One sample may have multiple image columns in a pd.DataFrame and multiple images
          inside each image column.
      is_training
          Whether to process images in the training mode.
  
      Returns
      -------
      A dictionary containing one sample's images and their number.
      """
      assert is_training
  
      image_paths.update(
          {
              self.image_key: self.train_processor(
                  image_paths[self.image_key]
              )
          }
      )
  
      return image_paths
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_mmlab/process_mmdet.py
imports: import logging
  from typing import Dict, List, Optional, Union
  import numpy
  import PIL
  from PIL import ImageFile
  from torch import nn
  from ..utils import is_rois_input
  from .process_mmlab_base import MMLabProcessor
label_desc: Use image node_id 4 for calling the MMDetProcessor obj's underlying member method code with example usage: obj.process_one_loaded_sample(image_paths, is_training) and returns: image_paths