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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 _build(self):
 """Returns a tuple containing observation and target one-hot tensors."""

    q = tf.FIFOQueue(
        self._queue_capacity, [self._dtype, self._dtype],
        shapes=[[self._num_steps, self._batch_size, self._vocab_size]]*2)
    obs, target = tf.py_func(self._get_batch, [], [tf.int32, tf.int32])
    obs = self._one_hot(obs)
    target = self._one_hot(target)
    enqueue_op = q.enqueue([obs, target])
    obs, target = q.dequeue()
    tf.train.add_queue_runner(tf.train.QueueRunner(q, [enqueue_op]))
    return SequenceDataOpsNoMask(obs, target)