hugegraph-ml/src/hugegraph_ml/data/hugegraph2dgl.py [294:305]:
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        if label_key and label_key in vertices[0]["properties"]:
            node_labels = [v["properties"][label_key] for v in vertices]
            graph_dgl.ndata["label"] = torch.tensor(node_labels, dtype=torch.long)
        if mask_keys:
            for mk in mask_keys:
                if mk in vertices[0]["properties"]:
                    node_masks = [v["properties"][mk] for v in vertices]
                    mask = torch.tensor(node_masks, dtype=torch.bool)
                    graph_dgl.ndata[mk] = mask
        return graph_dgl

    @staticmethod
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hugegraph-ml/src/hugegraph_ml/data/hugegraph2dgl.py [346:357]:
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        if label_key and label_key in vertices[0]["properties"]:
            node_labels = [v["properties"][label_key] for v in vertices]
            graph_dgl.ndata["label"] = torch.tensor(node_labels, dtype=torch.long)
        if mask_keys:
            for mk in mask_keys:
                if mk in vertices[0]["properties"]:
                    node_masks = [v["properties"][mk] for v in vertices]
                    mask = torch.tensor(node_masks, dtype=torch.bool)
                    graph_dgl.ndata[mk] = mask
        return graph_dgl

    @staticmethod
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