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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 fill_up(self, bucket_batch_sizes: List[BucketBatchSize], seed: int = 42) -> 'ParallelDataSet': """ Returns a new dataset with buckets filled up. :param bucket_batch_sizes: Bucket batch sizes. :param seed: The random seed used for sampling sentences to fill up. :return: New dataset with buckets filled up to the next multiple of batch size """

        source = list(self.source)
        target = list(self.target)
        label = list(self.label)

        rs = np.random.RandomState(seed)

        for bucket_idx in range(len(self)):
            bucket = bucket_batch_sizes[bucket_idx].bucket
            bucket_batch_size = bucket_batch_sizes[bucket_idx].batch_size
            bucket_source = self.source[bucket_idx]
            bucket_target = self.target[bucket_idx]
            bucket_label = self.label[bucket_idx]
            num_samples = bucket_source.shape[0]

            # Fill up the last batch by randomly sampling from the extant items.
            if num_samples % bucket_batch_size != 0:
                rest = bucket_batch_size - num_samples % bucket_batch_size
                desired_indices_np = rs.randint(num_samples, size=rest)
                desired_indices = mx.nd.array(desired_indices_np)

                if isinstance(source[bucket_idx], np.ndarray):
                    source[bucket_idx] = np.concatenate((bucket_source, bucket_source.take(desired_indices_np)), axis=0)
                else:
                    source[bucket_idx] = mx.nd.concat(bucket_source, bucket_source.take(desired_indices), dim=0)
                target[bucket_idx] = mx.nd.concat(bucket_target, bucket_target.take(desired_indices), dim=0)
                label[bucket_idx] = mx.nd.concat(bucket_label, bucket_label.take(desired_indices), dim=0)

        return ParallelDataSet(source, target, label)