class_docstr: The base class to prepare data for mmlab models specified by "prefix".
  Child class shall provide its specific collate function in __init__.
class_docstr_tok: ["The","base","class","to","prepare","data","for","mmlab","models","specified","by","``","prefix","''",".","Child","class","shall","provide","its","specific","collate","function","in","__init__","."]
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, is_training)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_mmlab/process_mmlab_base.py
imports: import logging
  import warnings
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import PIL
  from PIL import ImageFile
  from torch import nn
  from ...constants import AUTOMM, COLUMN, IMAGE, IMAGE_VALID_NUM, MMDET_IMAGE
  from ..collator import StackCollator
  from ..utils import is_rois_input
label_desc: Use image node_id 7 for calling the MMLabProcessor obj's underlying member method code with example usage: obj.__call__(images, feature_modalities, is_training) and returns: self