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: Parameters
  ----------
  model
      The model for which this processor would be created.
  merge
      How to merge numerical features from multiple columns in a multimodal pd.DataFrame.
      Currently, it only supports one choice:
      - concat
          Concatenate the numerical features.
  requires_column_info
      Whether to require feature column information in dataloader.
code: def __init__(
      self,
      model: nn.Module,
      merge: Optional[str] = "concat",
      requires_column_info: bool = False,
  ):
      """
      Parameters
      ----------
      model
          The model for which this processor would be created.
      merge
          How to merge numerical features from multiple columns in a multimodal pd.DataFrame.
          Currently, it only supports one choice:
          - concat
              Concatenate the numerical features.
      requires_column_info
          Whether to require feature column information in dataloader.
      """
      self.prefix = model.prefix
      self.merge = merge
      self.requires_column_info = requires_column_info
  
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 1 to create a new NumericalProcessor object  with example: obj = NumericalProcessor(model, merge, requires_column_info)