code: def get_parser():
      parser = argparse.ArgumentParser(
          description="The Basic Example of AutoML for Text Prediction."
      )
      parser.add_argument(
          "--train_file",
          type=str,
          help="The training pandas dataframe.",
          default=None,
      )
      parser.add_argument(
          "--dev_file",
          type=str,
          help="The validation pandas dataframe",
          default=None,
      )
      parser.add_argument(
          "--test_file",
          type=str,
          help="The test pandas dataframe",
          default=None,
      )
      parser.add_argument(
          "--seed", type=int, help="The seed", default=123
      )
      parser.add_argument(
          "--feature_columns", help="Feature columns", default=None
      )
      parser.add_argument(
          "--label_columns", help="Label columns", default=None
      )
      parser.add_argument(
          "--eval_metric",
          type=str,
          help="The metric used to evaluate the model.",
          default=None,
      )
      parser.add_argument(
          "--all_metrics",
          type=str,
          help="All metrics that we will report. This will usually "
          "include the eval_metric",
          default=None,
      )
      parser.add_argument("--task", type=str, default=None)
      parser.add_argument(
          "--do_train",
          action="store_true",
          help="Whether to train the model",
      )
      parser.add_argument(
          "--do_eval",
          action="store_true",
          help="Whether to evaluate the model",
      )
      parser.add_argument(
          "--exp_dir",
          type=str,
          default=None,
          help="The experiment directory where the model params will be written.",
      )
      parser.add_argument(
          "--mode",
          choices=["stacking", "weighted", "single"],
          default="single",
          help="Whether to use a single model or a stack ensemble. "
          'If it is "single", If it is turned on, we will use 5-fold, 1-layer for stacking.',
      )
      parser.add_argument(
          "--preset",
          type=str,
          help="Pre-registered configurations",
          choices=[
              "medium_quality_faster_train",
              "high_quality",
              "best_quality",
          ],
          default=None,
      )
      return parser
  
file_path: autogluon/examples/automm/text_prediction/run_text_prediction.py
imports: import os
  import json
  import argparse
  import numpy
  import random
  import pandas
  import copy
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.tabular import TabularPredictor
  from autogluon.core.utils.loaders import load_pd
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
label_desc: Use image node_id 1 for calling a global function with example usage: get_parser() and returns: parser