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: Collate individual labels into a batch. Here it stacks labels.
  This function will be used when creating Pytorch DataLoader.
  
  Returns
  -------
  A dictionary containing one model's collator function for labels.
code: def collate_fn(
      self,
      label_column_names: Optional[List] = None,
      per_gpu_batch_size: Optional[int] = None,
  ) -> Dict:
      """
      Collate individual labels into a batch. Here it stacks labels.
      This function will be used when creating Pytorch DataLoader.
  
      Returns
      -------
      A dictionary containing one model's collator function for labels.
      """
      if self.prefix == MMDET_IMAGE:
          fn = {self.label_key: ListCollator()}
      else:
          fn = {self.label_key: StackCollator()}
      return fn
  
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 3 for calling the LabelProcessor obj's underlying member method code with example usage: obj.collate_fn(label_column_names, per_gpu_batch_size) and returns: fn