old_contents: import tensorflow as tf
  
  from .flag import FLAGS, FlagAdder
  from .estimator import def_estimator
  from .inputs import def_def_train_input_fn
  from .inputs import def_def_eval_input_fn
  
  
  def def_def_experiment_fn():
      adder = FlagAdder()
  
      works_with = lambda name: "Works only with {}".format(name)
      train_help = works_with("qnd.train_and_evaluate()")
  
      adder.add_flag("train_steps", type=int, help=train_help)
      adder.add_flag("eval_steps", type=int, help=works_with("qnd.evaluate()"))
      adder.add_flag("min_eval_frequency", type=int, default=1, help=train_help)
  
      estimator = def_estimator()
      def_train_input_fn = def_def_train_input_fn()
      def_eval_input_fn = def_def_eval_input_fn()
  
      def def_experiment_fn(model_fn, input_fn):
          def experiment_fn(output_dir):
              return tf.contrib.learn.Experiment(
                  estimator(model_fn, output_dir),
                  def_train_input_fn(input_fn),
                  def_eval_input_fn(input_fn),
                  **{arg: getattr(FLAGS, arg) for arg in adder.flags})
  
          return experiment_fn
  
      return def_experiment_fn
  
new_contents: import tensorflow as tf
  
  from .flag import FLAGS, FlagAdder
  from .estimator import def_estimator
  from .inputs import DataUse, def_def_train_input_fn, def_def_eval_input_fn
  
  
  def def_def_experiment_fn():
      adder = FlagAdder()
  
      for use in DataUse:
          use = use.value
          adder.add_flag("{}_steps".format(use), type=int,
                         help="Maximum number of {} steps".format(use))
  
      adder.add_flag("min_eval_frequency", type=int, default=1,
                     help="Minimum evaluation frequency in number of model "
                          "savings")
  
      estimator = def_estimator()
      def_train_input_fn = def_def_train_input_fn()
      def_eval_input_fn = def_def_eval_input_fn()
  
      def def_experiment_fn(model_fn, input_fn):
          def experiment_fn(output_dir):
              return tf.contrib.learn.Experiment(
                  estimator(model_fn, output_dir),
                  def_train_input_fn(input_fn),
                  def_eval_input_fn(input_fn),
                  **{arg: getattr(FLAGS, arg) for arg in adder.flags})
  
          return experiment_fn
  
      return def_experiment_fn
  
subject: Fix outdated command line help