import tensorflow as tf

      alpha: Float that controls the length penalty. larger the alpha, stronger
        the preference for slonger translations.

    Returns:
       samples: an integer `Tensor`. Top samples from the beam search
    """
    batch_size = common_layers.shape_list(features["inputs"])[0]

    def symbols_to_logits_fn(ids):
      """Go from ids to logits."""
      ids = tf.expand_dims(tf.expand_dims(ids, axis=2), axis=3)
      ids = tf.pad(ids[:, 1:], [[0, 0], [0, 1], [0, 0], [0, 0]])
      if "partial_targets" in features:
        pt = features["partial_targets"]
        pt_length = common_layers.shape_list(pt)[1]
        pt = tf.tile(pt, [1, beam_size])
        pt = tf.reshape(pt, [batch_size * beam_size, pt_length, 1, 1])
        ids = tf.concat([pt, ids], axis=1)

      features["targets"] = ids
