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: Process one sample's labels. Here it only picks the first label.
  New rules can be added if necessary.
  
  Parameters
  ----------
  labels
      One sample may have multiple labels.
  Returns
  -------
  A dictionary containing one sample's label.
code: def process_one_sample(
      self,
      labels: Dict[str, Union[int, float, list]],
  ) -> Dict:
      """
      Process one sample's labels. Here it only picks the first label.
      New rules can be added if necessary.
  
      Parameters
      ----------
      labels
          One sample may have multiple labels.
      Returns
      -------
      A dictionary containing one sample's label.
      """
  
      return {
          self.label_key: labels[
              next(iter(labels))
          ],  # get the first key's value
      }
  
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 4 for calling the LabelProcessor obj's underlying member method code with example usage: obj.process_one_sample(labels) and returns: dict