code: def train(args):
      if args.task == "adult":
          df_train = TabularDataset(
              "https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv"
          )
          df_test = TabularDataset(
              "https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv"
          )
          label = "class"
      elif args.task == "TCGA_HNSC":
          df = data_loader(args.path)
          df_train, df_test = preprocess(
              df, args.test_size, args.shuffle
          )
          label = "vital_status"
      else:
          raise NotImplementedError
  
      metric = "accuracy"
      hyperparameters = (
          {}
          if args.mode == "FT_Transformer"
          else get_hyperparameter_config("default")
      )
      hyperparameters["FT_TRANSFORMER"] = {
          "env.num_gpus": args.num_gpus,
          "env.num_workers": args.num_workers,
      }
      predictor = TabularPredictor(
          label=label,
          eval_metric=metric,
      ).fit(
          train_data=df_train,
          hyperparameters=hyperparameters,
          time_limit=900,
      )
      leaderboard = predictor.leaderboard(df_test)
      leaderboard.to_csv("./leaderboard.csv")
      return
  
file_path: autogluon/examples/automm/TCGA_cancer_survival/example_cancer_survival.py
imports: import pandas
  import numpy
  import argparse
  import os
  import random
  from autogluon.tabular import TabularPredictor, TabularDataset
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
  import torch
  from sklearn.model_selection import train_test_split
  from autogluon.multimodal.utils import download
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: train(args) without return types