class_docstr: Prepare numerical data for the model specified by "prefix".
  For multiple models requiring numerical data, we need to create a NumericalProcessor
  for each related model so that they will have independent input.
docstr: Collate individual samples into a batch. Here it stacks numerical features.
  This function will be used when creating Pytorch DataLoader.
  
  Returns
  -------
  A dictionary containing one model's collator function for numerical features.
code: def collate_fn(self, numerical_column_names: List) -> Dict:
      """
      Collate individual samples into a batch. Here it stacks numerical features.
      This function will be used when creating Pytorch DataLoader.
  
      Returns
      -------
      A dictionary containing one model's collator function for numerical features.
      """
      fn = {}
      if self.requires_column_info:
          assert numerical_column_names, "Empty numerical column names."
          for col_name in numerical_column_names:
              fn[
                  f"{self.numerical_column_prefix}_{col_name}"
              ] = StackCollator()
  
      fn[self.numerical_key] = StackCollator()
  
      return fn
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_numerical.py
imports: from typing import Any, Dict, List, Optional, Union
  import numpy
  from torch import nn
  from ..constants import COLUMN, NUMERICAL
  from .collator import StackCollator
label_desc: Use image node_id 4 for calling the NumericalProcessor obj's underlying member method code with example usage: obj.collate_fn(numerical_column_names) and returns: fn