How to use the dgl.graph.DGLGraph function in dgl

col_idx.append(0)
        ones = np.ones(shape=(len(row_idx)))
        csr = spsp.csr_matrix((ones, (row_idx, col_idx)), shape=(10, 10))
        g = DGLGraph(csr, readonly=True)
        ncol = mx.nd.random.normal(shape=(10, D))
        ecol = mx.nd.random.normal(shape=(17, D))
        if grad:
            ncol.attach_grad()
            ecol.attach_grad()
        g.ndata['h'] = ncol
        g.edata['w'] = ecol
        g.set_n_initializer(dgl.init.zero_initializer)
        g.set_e_initializer(dgl.init.zero_initializer)
        return g
    else:
        g = DGLGraph()
        g.add_nodes(10) # 10 nodes.
        # create a graph where 0 is the source and 9 is the sink
        for i in range(1, 9):
            g.add_edge(0, i)
            g.add_edge(i, 9)
        # add a back flow from 9 to 0
        g.add_edge(9, 0)
        ncol = mx.nd.random.normal(shape=(10, D))
        ecol = mx.nd.random.normal(shape=(17, D))
        if grad:
            ncol.attach_grad()
            ecol.attach_grad()
        g.ndata['h'] = ncol
        g.edata['w'] = ecol
        g.set_n_initializer(dgl.init.zero_initializer)
        g.set_e_initializer(dgl.init.zero_initializer)

def generate_graph(grad=False, add_data=True):
    g = DGLGraph()
    g.add_nodes(10)
    # create a graph where 0 is the source and 9 is the sink
    for i in range(1, 9):
        g.add_edge(0, i)
        g.add_edge(i, 9)
    # add a back flow from 9 to 0
    g.add_edge(9, 0)
    if add_data:
        ncol = F.randn((10, D))
        ecol = F.randn((17, D))
        if grad:
            ncol = F.attach_grad(ncol)
            ecol = F.attach_grad(ecol)
        g.ndata['h'] = ncol
        g.edata['l'] = ecol
    return g

def test_send_twice_different_msg():
    g = DGLGraph()
    g.set_n_initializer(dgl.init.zero_initializer)
    g.add_nodes(3)
    g.add_edge(0, 1)
    g.add_edge(2, 1)
    def _message_a(edges):
        return {'a': edges.src['a']}
    def _message_b(edges):
        return {'a': edges.src['a'] * 3}
    def _reduce(nodes):
        return {'a': F.max(nodes.mailbox['a'], 1)}

    old_repr = F.randn((3, 5))
    g.ndata['a'] = old_repr
    g.send((0, 1), _message_a)
    g.send((0, 1), _message_b)
    g.recv(1, _reduce)

def check_reduce_0deg(readonly):
    if readonly:
        row_idx = []
        col_idx = []
        for i in range(1, 5):
            row_idx.append(i)
            col_idx.append(0)
        ones = np.ones(shape=(len(row_idx)))
        csr = spsp.csr_matrix((ones, (row_idx, col_idx)), shape=(5, 5))
        g = DGLGraph(csr, readonly=True)
    else:
        g = DGLGraph()
        g.add_nodes(5)
        g.add_edge(1, 0)
        g.add_edge(2, 0)
        g.add_edge(3, 0)
        g.add_edge(4, 0)
    def _message(edges):
        return {'m' : edges.src['h']}
    def _reduce(nodes):
        return {'h' : nodes.data['h'] + nodes.mailbox['m'].sum(1)}
    def _init2(shape, dtype, ctx, ids):
        return 2 + mx.nd.zeros(shape, dtype=dtype, ctx=ctx)
    g.set_n_initializer(_init2, 'h')
    old_repr = mx.nd.random.normal(shape=(5, 5))
    g.set_n_repr({'h': old_repr})
    g.update_all(_message, _reduce)
    new_repr = g.ndata['h']

def test_map_to_subgraph():
    g = DGLGraph()
    g.add_nodes(10)
    g.add_edges(F.arange(0, 9), F.arange(1, 10))
    h = g.subgraph([0, 1, 2, 5, 8])
    v = h.map_to_subgraph_nid([0, 8, 2])
    assert np.array_equal(F.asnumpy(v), np.array([0, 4, 2]))

def check_pull_0deg(readonly):
    if readonly:
        row_idx = []
        col_idx = []
        row_idx.append(0)
        col_idx.append(1)
        ones = np.ones(shape=(len(row_idx)))
        csr = spsp.csr_matrix((ones, (row_idx, col_idx)), shape=(2, 2))
        g = DGLGraph(csr, readonly=True)
    else:
        g = DGLGraph()
        g.add_nodes(2)
        g.add_edge(0, 1)
    def _message(edges):
        return {'m' : edges.src['h']}
    def _reduce(nodes):
        return {'h' : nodes.mailbox['m'].sum(1)}
    def _apply(nodes):
        return {'h' : nodes.data['h'] * 2}
    def _init2(shape, dtype, ctx, ids):
        return 2 + mx.nd.zeros(shape, dtype=dtype, ctx=ctx)
    g.set_n_initializer(_init2, 'h')
    old_repr = mx.nd.random.normal(shape=(2, 5))

    # test#1: pull only 0-deg node

def main(args):
    # dropout parameters
    input_dropout = 0.2
    attention_dropout = 0.2

    # load and preprocess dataset
    adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(args.dataset)
    features = preprocess_features(features)

    # initialize graph
    g = DGLGraph(adj)

    # create model
    model = GAT(args.num_layers,
                features.shape[1],
                args.num_hidden,
                y_train.shape[1],
                args.num_heads,
                F.elu,
                input_dropout,
                attention_dropout,
                args.residual)

    # use optimizer
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)

    # convert labels and masks to tensor

def main(args):
    # dropout parameters
    input_dropout = args.idrop
    attention_dropout = args.adrop

    # load and preprocess dataset
    adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(args.dataset)
    features = preprocess_features(features)

    # initialize graph
    g = DGLGraph(adj)

    # create model
    model = GeniePath(args.num_layers,
                features.shape[1],
                args.num_hidden,
                y_train.shape[1],
                args.num_heads,
                F.elu,
                input_dropout,
                attention_dropout,
                args.residual)
    model.train(g, features, y_train, epochs=args.epochs)