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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 labels_to_onehots(labels, num_classes):
 """Convert a vector of integer class labels to a matrix of one-hot target vectors. :param labels: a vector of integer labels, 0 to num_classes. Has shape (batch_size,). :param num_classes: the total number of classes :return: has shape (batch_size, num_classes) """

    batch_size = labels.get_shape().as_list()[0]

    with tf.name_scope("one_hot"):
        labels = tf.expand_dims(labels, 1)
        indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
        sparse_ptrs = tf.concat(1, [indices, labels], name="ptrs")
        onehots = tf.sparse_to_dense(sparse_ptrs, [batch_size, num_classes],
                                     1.0, 0.0)
        return onehots