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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 multi_label_loss(top_out, targets, model_hparams, vocab_size, weights_fn):
 """Average loss over the labels."""

  del vocab_size  # unused arg
  logits = top_out
  num_labels = tf.shape(targets)[1]
  logits = tf.tile(logits, [1, num_labels, 1, 1, 1])

  xent, weights = common_layers.padded_cross_entropy(
      logits,
      targets,
      model_hparams.label_smoothing,
      weights_fn=weights_fn,
      reduce_sum=False,
  )
  xent = tf.squeeze(xent, [2, 3])
  weights = tf.squeeze(weights, [2, 3])
  # average loss over all labels
  loss = tf.reduce_sum(xent, axis=1)
  weights = tf.reduce_sum(weights, axis=1)
  loss /= (weights + 1e-8)
  weights = tf.to_float(tf.greater(weights, 0.))

  return tf.reduce_sum(loss*weights), tf.reduce_sum(weights)