import tensorflow as tf

  """
  with tf.name_scope(name):
    private_samples -= tf.reduce_mean(private_samples, 0)
    shared_samples -= tf.reduce_mean(shared_samples, 0)

    private_samples = tf.nn.l2_normalize(private_samples, 1)
    shared_samples = tf.nn.l2_normalize(shared_samples, 1)

    correlation_matrix = tf.matmul(private_samples, shared_samples, transpose_a=True)

    cost = tf.reduce_mean(tf.square(correlation_matrix)) * weight
    cost = tf.where(cost > 0, cost, 0, name='value')
