//  [python]
def train(images, labels, ckpt_path, dropout=False):
  """
  This function contains the loop that actually trains the model.
  :param images: a numpy array with the input data
  :param labels: a numpy array with the output labels
  :param ckpt_path: a path (including name) where model checkpoints are saved
  :param dropout: Boolean, whether to use dropout or not
  :return: True if everything went well
  """

  # Check training data
  assert len(images) == len(labels)
  assert images.dtype == np.float32
  assert labels.dtype == np.int32

  # Set default TF graph
  with tf.Graph().as_default():
    global_step = tf.Variable(0, trainable=False)

    # Declare data placeholder
    train_data_node = _input_placeholder()

    # Create a placeholder to hold labels
    train_labels_shape = (FLAGS.batch_size,)
    train_labels_node = tf.placeholder(tf.int32, shape=train_labels_shape)

    print("Done Initializing Training Placeholders")

    # Build a Graph that computes the logits predictions from the placeholder
    if FLAGS.deeper:
      logits = inference_deeper(train_data_node, dropout=dropout)
    else:
      logits = inference(train_data_node, dropout=dropout)

    # Calculate loss
    loss = loss_fun(logits, train_labels_node)

    # Build a Graph that trains the model with one batch of examples and
    # updates the model parameters.
    train_op = train_op_fun(loss, global_step)

    # Create a saver.
    saver = tf.train.Saver(tf.global_variables())

    print("Graph constructed and saver created")

    # Build an initialization operation to run below.
    init = tf.initialize_all_variables()

    # Create and init sessions
    sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)) #NOLINT(long-line)
    sess.run(init)

    print("Session ready, beginning training loop")

    # Initialize the number of batches
    data_length = len(images)
    nb_batches = math.ceil(data_length / FLAGS.batch_size)

    for step in xrange(FLAGS.max_steps):
      # for debug, save start time
      start_time = time.time()

      # Current batch number
      batch_nb = step % nb_batches

      # Current batch start and end indices
      start, end = utils.batch_indices(batch_nb, data_length, FLAGS.batch_size)

      # Prepare dictionnary to feed the session with
      feed_dict = {train_data_node: images[start:end],
                   train_labels_node: labels[start:end]}

      # Run training step
      _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)

      # Compute duration of training step
      duration = time.time() - start_time

      # Sanity check
      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

      # Echo loss once in a while
      if step % 100 == 0:
        num_examples_per_step = FLAGS.batch_size
        examples_per_sec = num_examples_per_step / duration
        sec_per_batch = float(duration)

        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                      'sec/batch)')
        print (format_str % (datetime.now(), step, loss_value,
                             examples_per_sec, sec_per_batch))

      # Save the model checkpoint periodically.
      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
        saver.save(sess, ckpt_path, global_step=step)

  return True