code: def get_automm_hyperparameters(mode, text_backbone, cat_as_text):
      if mode == "ft-transformer":
          hparams = {
              "model.names": [
                  "ft_transformer",
                  "hf_text",
                  "fusion_transformer",
              ],
              "model.hf_text.checkpoint_name": text_backbone,
              "data.categorical.convert_to_text": cat_as_text,
          }
      elif mode == "mlp":
          hparams = {
              "model.names": [
                  "categorical_mlp",
                  "numerical_mlp",
                  "hf_text",
                  "fusion_mlp",
              ],
              "model.hf_text.checkpoint_name": text_backbone,
              "data.categorical.convert_to_text": cat_as_text,
          }
      else:
          raise NotImplementedError(f"mode={mode} is not supported!")
      return hparams
  
file_path: autogluon/examples/automm/kaggle_california_house_price/example_kaggle_house.py
imports: import pandas
  import numpy
  import argparse
  import os
  import random
  from autogluon.tabular import TabularPredictor
  from autogluon.multimodal import MultiModalPredictor
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: get_automm_hyperparameters(mode, text_backbone, cat_as_text) and returns: hparams