How to use the cogdl.datasets.register_dataset function in cogdl

class CoraDataset(Planetoid):
    def __init__(self):
        dataset = "Cora"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(CoraDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("citeseer")
class CiteSeerDataset(Planetoid):
    def __init__(self):
        dataset = "CiteSeer"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(CiteSeerDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("pubmed")
class PubMedDataset(Planetoid):
    def __init__(self):
        dataset = "PubMed"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(PubMedDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("reddit")
class RedditDataset(Reddit):
    def __init__(self):
        dataset = "Reddit"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(RedditDataset, self).__init__(path, T.TargetIndegree())

data = read_gatne_data(self.raw_dir)
        torch.save(data, self.processed_paths[0])

    def __repr__(self):
        return "{}()".format(self.name)


@register_dataset("amazon")
class AmazonDataset(GatneDataset):
    def __init__(self):
        dataset = "amazon"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(AmazonDataset, self).__init__(path, dataset)


@register_dataset("twitter")
class TwitterDataset(GatneDataset):
    def __init__(self):
        dataset = "twitter"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(TwitterDataset, self).__init__(path, dataset)


@register_dataset("youtube")
class YouTubeDataset(GatneDataset):
    def __init__(self):
        dataset = "youtube"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(YouTubeDataset, self).__init__(path, dataset)

dataset, filename = "blogcatalog", "blogcatalog"
        url = "http://leitang.net/code/social-dimension/data/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(BlogcatalogDataset, self).__init__(path, filename, url)


@register_dataset("flickr")
class FlickrDataset(MatlabMatrix):
    def __init__(self):
        dataset, filename = "flickr", "flickr"
        url = "http://leitang.net/code/social-dimension/data/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(FlickrDataset, self).__init__(path, filename, url)


@register_dataset("wikipedia")
class WikipediaDataset(MatlabMatrix):
    def __init__(self):
        dataset, filename = "wikipedia", "POS"
        url = "http://snap.stanford.edu/node2vec/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(WikipediaDataset, self).__init__(path, filename, url)


@register_dataset("ppi")
class PPIDataset(MatlabMatrix):
    def __init__(self):
        dataset, filename = "ppi", "Homo_sapiens"
        url = "http://snap.stanford.edu/node2vec/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(PPIDataset, self).__init__(path, filename, url)

return ["data.pt"]

    def get(self, idx):
        assert idx == 0
        return self.data

    def download(self):
        for name in self.raw_file_names:
            download_url("{}/{}".format(self.url, name), self.raw_dir)

    def process(self):
        data = read_edgelist_label_data(self.raw_dir, self.name)
        torch.save(data, self.processed_paths[0])


@register_dataset("dblp")
class DBLP(EdgelistLabel):
    def __init__(self):
        dataset = "dblp"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(DBLP, self).__init__(path, dataset)

data, slices = self.collate([data])
        torch.save((data, slices), self.processed_paths[0])

    def __repr__(self):
        return '{}()'.format(self.name)


@register_dataset('cora')
class CoraDataset(Planetoid):
    def __init__(self):
        dataset = 'Cora'
        path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
        super(CoraDataset, self).__init__(path, dataset, T.NormalizeFeatures())


@register_dataset('citeseer')
class CiteSeerDataset(Planetoid):
    def __init__(self):
        dataset = 'CiteSeer'
        path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
        super(CiteSeerDataset, self).__init__(path, dataset, T.NormalizeFeatures())


@register_dataset('pubmed')
class PubMedDataset(Planetoid):
    def __init__(self):
        dataset = 'PubMed'
        path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
        super(PubMedDataset, self).__init__(path, dataset, T.NormalizeFeatures())

smat = scio.loadmat(path)
        adj_matrix, group = smat["network"], smat["group"]

        y = tf.convert_to_tensor(group.todense(), np.float32)

        row_ind, col_ind = adj_matrix.nonzero()
        edge_index = tf.stack([tf.convert_to_tensor(row_ind), tf.convert_to_tensor(col_ind)])
        edge_attr = tf.convert_to_tensor(adj_matrix[row_ind, col_ind])

        data = Data(edge_index=edge_index, edge_attr=edge_attr, x=None, y=y)
        
        with open(self.processed_paths[0], 'wb') as output:
            pickle.dump(data, output, pickle.HIGHEST_PROTOCOL)


@register_dataset("blogcatalog")
class BlogcatalogDataset(MatlabMatrix):
    def __init__(self):
        dataset, filename = "blogcatalog", "blogcatalog"
        url = "http://leitang.net/code/social-dimension/data/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(BlogcatalogDataset, self).__init__(path, filename, url)


@register_dataset("flickr")
class FlickrDataset(MatlabMatrix):
    def __init__(self):
        dataset, filename = "flickr", "flickr"
        url = "http://leitang.net/code/social-dimension/data/"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(FlickrDataset, self).__init__(path, filename, url)

class CiteSeerDataset(Planetoid):
    def __init__(self):
        dataset = "CiteSeer"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(CiteSeerDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("pubmed")
class PubMedDataset(Planetoid):
    def __init__(self):
        dataset = "PubMed"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(PubMedDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("reddit")
class RedditDataset(Reddit):
    def __init__(self):
        dataset = "Reddit"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(RedditDataset, self).__init__(path, T.TargetIndegree())

import os.path as osp

import torch

import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid, Reddit

from . import register_dataset


@register_dataset("cora")
class CoraDataset(Planetoid):
    def __init__(self):
        dataset = "Cora"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(CoraDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("citeseer")
class CiteSeerDataset(Planetoid):
    def __init__(self):
        dataset = "CiteSeer"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(CiteSeerDataset, self).__init__(path, dataset, T.TargetIndegree())


@register_dataset("pubmed")

adj = sp.load_npz(osp.join(self.raw_dir, "reddit_graph.npz"))
        row = torch.from_numpy(adj.row).to(torch.long)
        col = torch.from_numpy(adj.col).to(torch.long)
        edge_index = torch.stack([row, col], dim=0)
        edge_index, _ = coalesce(edge_index, None, x.size(0), x.size(0))

        data = Data(x=x, edge_index=edge_index, y=y)
        data.train_mask = split == 1
        data.val_mask = split == 2
        data.test_mask = split == 3

        torch.save(self.collate([data]), self.processed_paths[0])


@register_dataset("reddit")
class RedditDataset(Reddit):
    def __init__(self):
        dataset = "Reddit"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "..", "data", dataset)
        super(RedditDataset, self).__init__(path, T.NormalizeFeatures())

class AmazonDataset(GatneDataset):
    def __init__(self):
        dataset = "amazon"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(AmazonDataset, self).__init__(path, dataset)


@register_dataset("twitter")
class TwitterDataset(GatneDataset):
    def __init__(self):
        dataset = "twitter"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(TwitterDataset, self).__init__(path, dataset)


@register_dataset("youtube")
class YouTubeDataset(GatneDataset):
    def __init__(self):
        dataset = "youtube"
        path = osp.join(osp.dirname(osp.realpath(__file__)), "../..", "data", dataset)
        super(YouTubeDataset, self).__init__(path, dataset)