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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 weights_concatenated(labels):
 """Assign weight 1.0 to the "target" part of the concatenated labels. The labels look like: source English I love you . ID1 target French Je t'aime . ID1 source We want to assign weight 1.0 to all words in the target text (including the ID1 end symbol), but not to the source text or the boilerplate. In the above example, the target words that get positive weight are: Je t'aime . ID1 le chat ID1 Args: labels: a Tensor Returns: a Tensor """

  eos_mask = tf.to_int32(tf.equal(labels, 1))
  sentence_num = tf.cumsum(eos_mask, axis=1, exclusive=True)
  in_target = tf.equal(tf.mod(sentence_num, 2), 1)
  # first two tokens of each sentence are boilerplate.
  sentence_num_plus_one = sentence_num + 1
  shifted = tf.pad(sentence_num_plus_one,
                   [[0, 0], [2, 0], [0, 0], [0, 0]])[:, :-2, :, :]
  nonboilerplate = tf.equal(sentence_num_plus_one, shifted)
  ret = to_float(tf.logical_and(nonboilerplate, in_target))
  return ret