Train supervised.

def train_supervised(problem, model_name, hparams, data_dir, output_dir,
                     train_steps, eval_steps, local_eval_frequency=None,
                     schedule="continuous_train_and_eval"):
  """Train supervised."""
  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)()