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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
 """Discretized mixture of logistics loss. Args: pred: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (one per channel), and three coefficients which linearly parameterize dependence across channels. labels: A [batch, height, width, channels] tensor of 8-bit pixel intensities. The computation assumes channels is 3. weights_fn: A function of labels, returning a Tensor of shape [batch, height, width] which weights each loss term. Default is to scale each loss term by 1/3 so that they capture the average across channels. reduce_sum: A boolean, to return scalar loss instead of per position. Returns: Tuple of loss tensors for numerator and denominator, each a scalar if reduce_sum else of shape [batch, height, width]. The sum of their divisions is the number of nats for each pixel in labels. """

  real_labels = convert_rgb_to_symmetric_real(labels)
  dml_loss_value = discretized_mix_logistic_loss(pred=pred, labels=real_labels)
  weights = weights_fn(labels)
  loss_num = weights * dml_loss_value
  loss_den = weights_nonzero(weights)
  if reduce_sum:
    loss_num = tf.reduce_sum(loss_num)
    loss_den = tf.reduce_sum(loss_den)
  return loss_num, loss_den