class_docstr: Prepare categorical data for the model specified by "prefix".
  For multiple models requiring categorical data, we need to create a CategoricalProcessor
  for each related model so that they will have independent input.
docstr: Extract one sample's categorical features and customize it for a specific model.
  
  Parameters
  ----------
  categorical_features
      Categorical 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 categorical features.
code: def __call__(
      self,
      categorical_features: Dict[str, int],
      feature_modalities: Dict[str, Union[int, float, list]],
      is_training: bool,
  ) -> Dict:
      """
      Extract one sample's categorical features and customize it for a specific model.
  
      Parameters
      ----------
      categorical_features
          Categorical 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 categorical features.
      """
      return self.process_one_sample(categorical_features)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/process_categorical.py
imports: from typing import Any, Dict, List, Optional, Union
  import numpy
  from torch import nn
  from ..constants import CATEGORICAL, COLUMN
  from .collator import StackCollator, TupleCollator
label_desc: Use image node_id 6 for calling the CategoricalProcessor obj's underlying member method code with example usage: obj.__call__(categorical_features, feature_modalities, is_training) and returns: self