def METHOD_NAME(_):
  # Generate some fake Iris data.
  # It is okay for this example because this example is about how to use the
  # debugger, not how to use machine learning to solve the Iris classification
  # problem.
  def training_input_fn():
    return ({"features": tf.random_normal([128, 4])},
            tf.random_uniform([128], minval=0, maxval=3, dtype=tf.int32))
  def test_input_fn():
    return ({"features": tf.random_normal([32, 4])},
            tf.random_uniform([32], minval=0, maxval=3, dtype=tf.int32))
  feature_columns = [
      tf.feature_column.numeric_column("features", shape=(4,))]
  # Build 3 layer DNN with 10, 20, 10 units respectively.
  model_dir = FLAGS.model_dir or tempfile.mkdtemp(prefix="debug_tflearn_iris_")
  classifier = tf.estimator.DNNClassifier(
      feature_columns=feature_columns,
      hidden_units=[10, 20, 10],
      n_classes=3,
      model_dir=model_dir)
  if FLAGS.debug and FLAGS.tensorboard_debug_address:
    raise ValueError(
        "The --debug and --tensorboard_debug_address flags are mutually "
        "exclusive.")
  hooks = []
  if FLAGS.debug:
    if FLAGS.use_random_config_path:
      fd, config_file_path = tempfile.mkstemp(".tfdbg_config")
      os.close(fd)
    else:
      config_file_path = None
    hooks.append(tf_debug.LocalCLIDebugHook(ui_type=FLAGS.ui_type,
                                            dump_root=FLAGS.dump_root,
                                            config_file_path=config_file_path))
  elif FLAGS.tensorboard_debug_address:
    hooks.append(tf_debug.TensorBoardDebugHook(FLAGS.tensorboard_debug_address))
  # Train model, using tfdbg hook.
  classifier.train(training_input_fn,
                   steps=FLAGS.train_steps,
                   hooks=hooks)
  # Evaluate accuracy, using tfdbg hook.
  accuracy_score = classifier.evaluate(test_input_fn,
                                       steps=FLAGS.eval_steps,
                                       hooks=hooks)["accuracy"]
  print("After training %d steps, Accuracy = %f" %
        (FLAGS.train_steps, accuracy_score))
  # Make predictions, using tfdbg hook.
  predict_results = classifier.predict(test_input_fn, hooks=hooks)
  print("A prediction result: %s" % next(predict_results))