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",
  ):
      """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)()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import datetime
  import math
  import os
  import pprint
  import random
  import time
  import six
  from tensor2tensor.bin import t2t_trainer
  from tensor2tensor.models.research import rl
  from tensor2tensor.rl import rl_utils
  from tensor2tensor.rl import trainer_model_based_params
  from tensor2tensor.rl.dopamine_connector import DQNLearner
  from tensor2tensor.rl.restarter import Restarter
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
label_desc: Use image node_id 7 for calling a global function with example usage: train_supervised(problem, model_name, hparams, data_dir, output_dir, train_steps, eval_steps, local_eval_frequency, schedule) without return types