class_docstr: Prepare rois data for mmdetection models.
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.
  load_only
      Whether to only load the data. Other processing steps may happen in dataset.__getitem__.
  
  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",".","load_only","Whether","to","only","load","the","data",".","Other","processing","steps","may","happen","in","dataset.__getitem__",".","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,
      load_only: bool = False,
  ) -> 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.
      load_only
          Whether to only load the data. Other processing steps may happen in dataset.__getitem__.
  
      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()
      }
      if load_only:
          return self.load_one_sample(images, is_training)
      else:
          return self.process_one_sample(images, is_training)
  
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 6 for calling the MMDetProcessor obj's underlying member method code with example usage: obj.__call__(images, feature_modalities, is_training, load_only) and returns: self, self