code: def train_supervised(problem, model_name, hparams, data_dir, output_dir,
                       train_steps, eval_steps, local_eval_frequency=None,
                       schedule="continuous_train_and_eval"):
    
    if local_eval_frequency is None:
      local_eval_frequency = FLAGS.local_eval_frequency
  
    exp_fn = trainer_lib.create_experiment_fn(
        model_name, problem, data_dir, train_steps, eval_steps,
        min_eval_frequency=local_eval_frequency
    )
    run_config = trainer_lib.create_run_config(model_name, model_dir=output_dir)
    exp = exp_fn(run_config, hparams)
    getattr(exp, schedule)()
docs: Train supervised.