def METHOD_NAME(logmix, mean, logstd, y, batch_mask, dont_reduce_loss):
  """Computes MDN loss term for svg decoder model."""
  logsqrttwopi = np.log(np.sqrt(2.0 * np.pi))
  v = logmix + _tf_lognormal(y, mean, logstd, logsqrttwopi)
  v = tf.reduce_logsumexp(v, 1, keepdims=True)
  v = tf.reshape(v, [-1, 51, 1, 6])
  # mask out unimportant terms given the ground truth commands
  v = tf.multiply(v, batch_mask)
  if dont_reduce_loss:
    return -tf.reduce_mean(tf.reduce_sum(v, axis=3), [1, 2])
  return -tf.reduce_mean(tf.reduce_sum(v, axis=3))