import tensorflow as tf

    sample_masks = 1. * tf.cast(sample, tf.float32) / num_choices
    sample_log_prob = tf.reduce_mean(dist.log_prob(sample))

  return (dist_entropy, sample_masks, sample_log_prob)


def get_loss_weights(name=None):
  """Returns the weight for loss."""
  with tf.variable_scope(name, 'rl_op_selection'):

    logits = tf.get_variable(
        name='loss_logits_rl_w',
        initializer=tf.initializers.zeros(),
        shape=[
            FLAGS.num_choices,
        ],
