code: def get_parser():
      parser = argparse.ArgumentParser(
          description="The Basic Example of AutoGluon for House Price Prediction."
      )
      parser.add_argument(
          "--mode",
          choices=["stack5", "weighted", "single", "single_bag5"],
          default="weighted",
          help='"stack5" means 5-fold stacking. "weighted" means weighted ensemble.'
          ' "single" means use a single model.'
          ' "single_bag5" means 5-fold bagging via the AutoMM model.',
      )
      parser.add_argument(
          "--automm-mode",
          choices=["ft-transformer", "mlp"],
          default="ft-transformer",
          help="Fusion model in AutoMM.",
      )
      parser.add_argument(
          "--text-backbone",
          default="google/electra-small-discriminator",
      )
      parser.add_argument("--cat-as-text", default=False)
      parser.add_argument(
          "--data_path", type=str, default="california-house-prices"
      )
      parser.add_argument("--seed", type=int, default=123)
      parser.add_argument("--exp_path", default=None)
      parser.add_argument("--with_tax_values", default=1, type=int)
      return parser
  
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 1 for calling a global function with example usage: get_parser() and returns: parser