def METHOD_NAME(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir,
                                    one_hot=True,
                                    fake_data=FLAGS.fake_data)
  def feed_dict(train):
    if train or FLAGS.fake_data:
      xs, ys = mnist.train.next_batch(FLAGS.train_batch_size,
                                      fake_data=FLAGS.fake_data)
    else:
      xs, ys = mnist.test.images, mnist.test.labels
    return {x: xs, y_: ys}
  sess = tf.InteractiveSession()
  # Create the MNIST neural network graph.
  # Input placeholders.
  with tf.name_scope("input"):
    x = tf.placeholder(
        tf.float32, [None, IMAGE_SIZE * IMAGE_SIZE], name="x-input")
    y_ = tf.placeholder(tf.float32, [None, NUM_LABELS], name="y-input")
  def weight_variable(shape):
    """Create a weight variable with appropriate initialization."""
    initial = tf.truncated_normal(shape, stddev=0.1, seed=RAND_SEED)
    return tf.Variable(initial)
  def bias_variable(shape):
    """Create a bias variable with appropriate initialization."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
  def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    """Reusable code for making a simple neural net layer."""
    # Adding a name scope ensures logical grouping of the layers in the graph.
    with tf.name_scope(layer_name):
      # This Variable will hold the state of the weights for the layer
      with tf.name_scope("weights"):
        weights = weight_variable([input_dim, output_dim])
      with tf.name_scope("biases"):
        biases = bias_variable([output_dim])
      with tf.name_scope("Wx_plus_b"):
        preactivate = tf.matmul(input_tensor, weights) + biases
      activations = act(preactivate)
      return activations
  hidden = nn_layer(x, IMAGE_SIZE**2, HIDDEN_SIZE, "hidden")
  logits = nn_layer(hidden, HIDDEN_SIZE, NUM_LABELS, "output", tf.identity)
  y = tf.nn.softmax(logits)
  with tf.name_scope("cross_entropy"):
    # The following line is the culprit of the bad numerical values that appear
    # during training of this graph. Log of zero gives inf, which is first seen
    # in the intermediate tensor "cross_entropy/Log:0" during the 4th run()
    # call. A multiplication of the inf values with zeros leads to nans,
    # which is first in "cross_entropy/mul:0".
    #
    # You can use the built-in, numerically-stable implementation to fix this
    # issue:
    #   diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=logits)
    diff = -(y_ * tf.log(y))
    with tf.name_scope("total"):
      cross_entropy = tf.reduce_mean(diff)
  with tf.name_scope("train"):
    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
        cross_entropy)
  with tf.name_scope("accuracy"):
    with tf.name_scope("correct_prediction"):
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    with tf.name_scope("accuracy"):
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  sess.run(tf.global_variables_initializer())
  if FLAGS.debug and FLAGS.tensorboard_debug_address:
    raise ValueError(
        "The --debug and --tensorboard_debug_address flags are mutually "
        "exclusive.")
  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
    sess = tf_debug.LocalCLIDebugWrapperSession(
        sess,
        ui_type=FLAGS.ui_type,
        config_file_path=config_file_path)
  elif FLAGS.tensorboard_debug_address:
    sess = tf_debug.TensorBoardDebugWrapperSession(
        sess, FLAGS.tensorboard_debug_address)
  # Add this point, sess is a debug wrapper around the actual Session if
  # FLAGS.debug is true. In that case, calling run() will launch the CLI.
  for i in range(FLAGS.max_steps):
    acc = sess.run(accuracy, feed_dict=feed_dict(False))
    print("Accuracy at step %d: %s" % (i, acc))
    sess.run(train_step, feed_dict=feed_dict(True))