How to use the pgl.graph_kernel function in pgl

A :code:`pgl.graph.Subgraph` object.
        """
        reindex = {}

        for ind, node in enumerate(nodes):
            reindex[node] = ind

        if eid is None and edges is None:
            raise ValueError("Eid and edges can't be None at the same time.")

        if edges is None:
            edges = self._edges[eid]
        else:
            edges = np.array(edges, dtype="int64")

        sub_edges = graph_kernel.map_edges(
            np.arange(
                len(edges), dtype="int64"), edges, reindex)

        sub_edge_feat = {}
        for key, value in self._edge_feat.items():
            if eid is None:
                raise ValueError("Eid can not be None with edge features.")
            sub_edge_feat[key] = value[eid]

        sub_node_feat = {}
        for key, value in self._node_feat.items():
            sub_node_feat[key] = value[nodes]

        subgraph = SubGraph(
            num_nodes=len(nodes),
            edges=sub_edges,

def reindex_from_parrent_nodes(self, nodes):
        """Map the given parent graph node id to subgraph id.

        Args:
            nodes: A list of nodes from parent graph.

        Return:
            A list of subgraph ids.
        """
        return graph_kernel.map_nodes(nodes, self._from_reindex)

a list of eids that connected nodes to their predecessors.
        """
        node_pred = self.predecessor(nodes, return_eids=return_eids)
        if return_eids:
            node_pred, node_pred_eid = node_pred

        if nodes is None:
            nodes = self.nodes

        node_pred = node_pred.tolist()

        if return_eids:
            node_pred_eid = node_pred_eid.tolist()

        if return_eids:
            return graph_kernel.sample_subset_with_eid(
                node_pred, node_pred_eid, max_degree, shuffle)
        else:
            return graph_kernel.sample_subset(node_pred, max_degree, shuffle)

def graph_alias_sample_table(graph, edge_weight_name):
    """Build alias sample table for weighted deepwalk.
    Args:
        graph: The input graph
        edge_weight_name: The name of edge weight in edge_feat.

    Return:
        Alias sample tables for each nodes.
    """
    edge_weight = graph.edge_feat[edge_weight_name]
    _, eids_array = graph.successor(return_eids=True)
    alias_array, events_array = [], []
    for eids in eids_array:
        probs = edge_weight[eids]
        probs /= np.sum(probs)
        alias, events = graph_kernel.alias_sample_build_table(probs)
        alias_array.append(alias), events_array.append(events)
    alias_array, events_array = np.array(alias_array), np.array(events_array)
    return alias_array, events_array

if return_eids:
            node_pred, node_pred_eid = node_pred

        if nodes is None:
            nodes = self.nodes

        node_pred = node_pred.tolist()

        if return_eids:
            node_pred_eid = node_pred_eid.tolist()

        if return_eids:
            return graph_kernel.sample_subset_with_eid(
                node_pred, node_pred_eid, max_degree, shuffle)
        else:
            return graph_kernel.sample_subset(node_pred, max_degree, shuffle)

def __init__(self, u, v, num_nodes):
        self._v, self._eid, self._degree, self._sorted_u,\
                self._sorted_v, self._sorted_eid = graph_kernel.build_index(u, v, num_nodes)