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: Extract one sample's numerical features and customize it for a specific model.
  
  Parameters
  ----------
  numerical_features
      Numerical features 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.
  
  Returns
  -------
  A dictionary containing one sample's processed numerical features.
code: def __call__(
      self,
      numerical_features: Dict[str, float],
      feature_modalities: Dict[str, Union[int, float, list]],
      is_training: bool,
  ) -> Dict:
      """
      Extract one sample's numerical features and customize it for a specific model.
  
      Parameters
      ----------
      numerical_features
          Numerical features 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.
  
      Returns
      -------
      A dictionary containing one sample's processed numerical features.
      """
      return self.process_one_sample(numerical_features)
  
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 6 for calling the NumericalProcessor obj's underlying member method code with example usage: obj.__call__(numerical_features, feature_modalities, is_training) and returns: self