old_contents: import tensorflow as tf
  
  from .flag import FLAGS, FlagAdder
  from .estimator import def_estimator
  from .inputs import def_def_train_input_fn, def_def_eval_input_fn
  
  
  def def_def_experiment_fn(batch_inputs=True,
                            prepare_filename_queues=True,
                            distributed=False):
      adder = FlagAdder()
  
      for mode in [tf.contrib.learn.ModeKeys.TRAIN,
                   tf.contrib.learn.ModeKeys.EVAL]:
          adder.add_flag("{}_steps".format(mode), type=int,
                         help="Maximum number of {} steps".format(mode))
  
      adder.add_flag("min_eval_frequency", type=int, default=1,
                     help="Minimum evaluation frequency in number of model "
                          "savings")
  
      estimator = def_estimator(distributed)
      def_train_input_fn = def_def_train_input_fn(batch_inputs,
                                                  prepare_filename_queues)
      def_eval_input_fn = def_def_eval_input_fn(batch_inputs,
                                                prepare_filename_queues)
  
      def def_experiment_fn(model_fn, train_input_fn, eval_input_fn=None):
          def experiment_fn(output_dir):
              return tf.contrib.learn.Experiment(
                  estimator(model_fn, output_dir),
                  def_train_input_fn(train_input_fn),
                  def_eval_input_fn(eval_input_fn or train_input_fn),
                  **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 def_def_train_input_fn, def_def_eval_input_fn
  
  
  def def_def_experiment_fn(batch_inputs=True,
                            prepare_filename_queues=True,
                            distributed=False):
      adder = FlagAdder()
  
      for mode in [tf.contrib.learn.ModeKeys.TRAIN,
                   tf.contrib.learn.ModeKeys.EVAL]:
          adder.add_flag("{}_steps".format(mode), type=int,
                         help="Maximum number of {} steps".format(mode))
  
      adder.add_flag("min_eval_frequency", type=int, default=1,
                     help="Minimum evaluation frequency in number of train steps")
  
      estimator = def_estimator(distributed)
      def_train_input_fn = def_def_train_input_fn(batch_inputs,
                                                  prepare_filename_queues)
      def_eval_input_fn = def_def_eval_input_fn(batch_inputs,
                                                prepare_filename_queues)
  
      def def_experiment_fn(model_fn, train_input_fn, eval_input_fn=None):
          def experiment_fn(output_dir):
              return tf.contrib.learn.Experiment(
                  estimator(model_fn, output_dir),
                  def_train_input_fn(train_input_fn),
                  def_eval_input_fn(eval_input_fn or train_input_fn),
                  **adder.flags)
  
          return experiment_fn
  
      return def_experiment_fn
  
subject: Fix help message of --min_eval_frequency flag