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: Process one sample's numerical features.
  Here it converts numerical features to a NumPy array.
  
  Parameters
  ----------
  numerical_features
      Numerical features of one sample.
  
  Returns
  -------
  A dictionary containing the processed numerical features.
code: def process_one_sample(
      self,
      numerical_features: Dict[str, float],
  ) -> Dict:
      """
      Process one sample's numerical features.
      Here it converts numerical features to a NumPy array.
  
      Parameters
      ----------
      numerical_features
          Numerical features of one sample.
  
      Returns
      -------
      A dictionary containing the processed numerical 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(numerical_features.keys()):
              ret[f"{self.numerical_column_prefix}_{col_name}"] = i
  
      if self.merge == "concat":
          ret[self.numerical_key] = np.array(
              list(numerical_features.values()), dtype=np.float32
          )
      else:
          raise ValueError(f"Unknown merging type: {self.merge}")
  
      return ret
  
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 5 for calling the NumericalProcessor obj's underlying member method code with example usage: obj.process_one_sample(numerical_features) and returns: ret