code: def main(_argv):
      """The entrypoint for the script"""
  
      # Parse YAML FLAGS
      FLAGS.hooks = _maybe_load_yaml(FLAGS.hooks)
      FLAGS.metrics = _maybe_load_yaml(FLAGS.metrics)
      FLAGS.model_params = _maybe_load_yaml(FLAGS.model_params)
      FLAGS.input_pipeline_train = _maybe_load_yaml(
          FLAGS.input_pipeline_train
      )
      FLAGS.input_pipeline_dev = _maybe_load_yaml(
          FLAGS.input_pipeline_dev
      )
  
      # Load flags from config file
      final_config = {}
      if FLAGS.config_paths:
          for config_path in FLAGS.config_paths.split(","):
              config_path = config_path.strip()
              if not config_path:
                  continue
              config_path = os.path.abspath(config_path)
              tf.logging.info("Loading config from %s", config_path)
              with gfile.GFile(config_path.strip()) as config_file:
                  config_flags = yaml.load(config_file)
                  final_config = _deep_merge_dict(
                      final_config, config_flags
                  )
  
      tf.logging.info("Final Config:\n%s", yaml.dump(final_config))
  
      # Merge flags with config values
      for flag_key, flag_value in final_config.items():
          if hasattr(FLAGS, flag_key) and isinstance(
              getattr(FLAGS, flag_key), dict
          ):
              merged_value = _deep_merge_dict(
                  flag_value, getattr(FLAGS, flag_key)
              )
              setattr(FLAGS, flag_key, merged_value)
          elif hasattr(FLAGS, flag_key):
              setattr(FLAGS, flag_key, flag_value)
          else:
              tf.logging.warning("Ignoring config flag: %s", flag_key)
  
      if (
          FLAGS.save_checkpoints_secs is None
          and FLAGS.save_checkpoints_steps is None
      ):
          FLAGS.save_checkpoints_secs = 600
          tf.logging.info(
              "Setting save_checkpoints_secs to %d",
              FLAGS.save_checkpoints_secs,
          )
  
      if not FLAGS.output_dir:
          FLAGS.output_dir = tempfile.mkdtemp()
  
      if not FLAGS.input_pipeline_train:
          raise ValueError("You must specify input_pipeline_train")
  
      if not FLAGS.input_pipeline_dev:
          raise ValueError("You must specify input_pipeline_dev")
  
      learn_runner.run(
          experiment_fn=create_experiment,
          output_dir=FLAGS.output_dir,
          schedule=FLAGS.schedule,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import os
  import tempfile
  import yaml
  import tensorflow
  from tensorflow.contrib.learn.python.learn import learn_runner
  from tensorflow.contrib.learn.python.learn.estimators import run_config
  from tensorflow import gfile
  from seq2seq import models
  from seq2seq.contrib.experiment import Experiment
  from seq2seq.configurable import _maybe_load_yaml, _create_from_dict
  from seq2seq.configurable import _deep_merge_dict
  from seq2seq.data import input_pipeline
  from seq2seq.metrics import metric_specs
  from seq2seq.training import hooks
  from seq2seq.training import utils
label_desc: Use image node_id 2 for calling a global function with example usage: main(_argv) without return types