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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 padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
 """Calculate cross entropy loss while ignoring padding. Args: logits: Tensor of size [batch_size, length_logits, vocab_size] labels: Tensor of size [batch_size, length_labels] smoothing: Label smoothing constant, used to determine the on and off values vocab_size: int size of the vocabulary Returns: Returns a float32 tensor with shape [batch_size, max(length_logits, length_labels)] """

  with tf.name_scope("loss", [logits, labels]):
    logits, labels = _pad_tensors_to_same_length(logits, labels)

    # Calculate smoothing cross entropy
    with tf.name_scope("smoothing_cross_entropy", [logits, labels]):
      confidence = 1.0 - smoothing
      low_confidence = (1.0 - confidence) / tf.to_float(vocab_size - 1)
      soft_targets = tf.one_hot(
          tf.cast(labels, tf.int32),
          depth=vocab_size,
          on_value=confidence,
          off_value=low_confidence)
      xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(
          logits=logits, labels=soft_targets)

      # Calculate the best (lowest) possible value of cross entropy, and
      # subtract from the cross entropy loss.
      normalizing_constant = -(
          confidence * tf.log(confidence) + tf.to_float(vocab_size - 1) *
          low_confidence * tf.log(low_confidence + 1e-20))
      xentropy -= normalizing_constant

    weights = tf.to_float(tf.not_equal(labels, 0))
    return xentropy * weights, weights