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: Process one sample's categorical features. Assume the categorical features
  are the encoded labels from sklearn' LabelEncoder().
  
  Parameters
  ----------
  categorical_features
      Categorical features of one sample.
  
  Returns
  -------
  A dictionary containing the processed categorical features.
code: def process_one_sample(
      self,
      categorical_features: Dict[str, int],
  ) -> Dict:
      """
      Process one sample's categorical features. Assume the categorical features
      are the encoded labels from sklearn' LabelEncoder().
  
      Parameters
      ----------
      categorical_features
          Categorical features of one sample.
  
      Returns
      -------
      A dictionary containing the processed categorical features.
      """
      ret = {}
      if self.requires_column_info:
          # TODO: consider moving this for loop into __init__() since each sample has the same information.
          for i, col_name in enumerate(categorical_features.keys()):
              ret[f"{self.categorical_column_prefix}_{col_name}"] = i
  
      ret[self.categorical_key] = list(categorical_features.values())
  
      return ret
  
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 5 for calling the CategoricalProcessor obj's underlying member method code with example usage: obj.process_one_sample(categorical_features) and returns: ret