class_docstr: Prepare ground-truth labels for the model specified by "prefix".
  For multiple models, we need to create a LabelProcessor for each model so that
  each model will have independent labels.
docstr: Extract one sample's labels and customize them for a specific model.
  
  Parameters
  ----------
  labels
      Labels of one sample.
  feature_modalities
      The modality of the feature columns.
  is_training
      Whether to do processing in the training mode. This unused flag is for the API compatibility.
  load_only
      Whether to only load the data. Other processing steps may happen in dataset.__getitem__.
  
  Returns
  -------
  A dictionary containing one sample's processed label.
code: def __call__(
      self,
      labels: Dict[str, Union[int, float]],
      feature_modalities: Dict[str, Union[int, float, list]],
      is_training: bool,
      load_only: bool = False,  # TODO: refactor mmdet_image and remove this
  ) -> Dict:
      """
      Extract one sample's labels and customize them for a specific model.
  
      Parameters
      ----------
      labels
          Labels of one sample.
      feature_modalities
          The modality of the feature columns.
      is_training
          Whether to do processing in the training mode. This unused flag is for the API compatibility.
      load_only
          Whether to only load the data. Other processing steps may happen in dataset.__getitem__.
  
      Returns
      -------
      A dictionary containing one sample's processed label.
      """
      return self.process_one_sample(labels)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_label.py
imports: from typing import Any, Dict, List, Optional, Union
  from torch import nn
  from ..constants import LABEL, MMDET_IMAGE
  from .collator import ListCollator, StackCollator
label_desc: Use image node_id 5 for calling the LabelProcessor obj's underlying member method code with example usage: obj.__call__(labels, feature_modalities, is_training, load_only) and returns: self