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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 sequence_accuracy(labels, outputs):
 """Compute the sequence-level accuracy. A sequence is only considered correct if all of its entries were predicted correctly. Args: labels: ground-truth labels, shape=(batch, packed_seq_length) outputs: predicted tokens, shape=(batch, seq_length) Returns: Two ops, one for getting the current average accuracy and another for updating the running average estimate. """

  # A sequence is correct if all of the non-padded entries are correct
  all_correct = tf.reduce_all(
      tf.logical_or(tf.equal(labels, outputs), tf.equal(labels, 0)), axis=-1
  )
  return tf.metrics.mean(all_correct)