How to use dgl - 10 common examples

def forward(self, g, lg, x, y, deg_g, deg_lg, pm_pd):
        pmpd_x = F.embedding(pm_pd, x)

        sum_x = sum(theta(z) for theta, z in zip(self.theta_list, self.aggregate(g, x)))

        g.set_e_repr({'y' : y})
        g.update_all(fn.copy_edge(edge='y', out='m'), fn.sum('m', 'pmpd_y'))
        pmpd_y = g.pop_n_repr('pmpd_y')

        x = self.theta_x(x) + self.theta_deg(deg_g * x) + sum_x + self.theta_y(pmpd_y)
        n = self.out_feats // 2
        x = th.cat([x[:, :n], F.relu(x[:, n:])], 1)
        x = self.bn_x(x)

        sum_y = sum(gamma(z) for gamma, z in zip(self.gamma_list, self.aggregate(lg, y)))

        y = self.gamma_y(y) + self.gamma_deg(deg_lg * y) + sum_y + self.gamma_x(pmpd_x)
        y = th.cat([y[:, :n], F.relu(y[:, n:])], 1)
        y = self.bn_y(y)

        return x, y

col.append(dst)
            etypes.append(th.from_numpy(etype))
            # update shift
            v_shift += g.number_of_nodes
            e_shift += g.number_of_edges

        n = v_shift
        leaf_ids = th.cat(leaf_ids)
        readout_ids = th.cat(readout_ids)
        pos_arr = th.cat(pos_arr)
        etypes = th.cat(etypes)
        row, col = map(np.concatenate, (row, col))
        coo = coo_matrix((np.zeros_like(row), (row, col)), shape=(n, n))
        g = dgl.DGLGraph(coo, readonly=True)
        g.set_n_initializer(dgl.init.zero_initializer)
        g.set_e_initializer(dgl.init.zero_initializer)

        data = th.cat(data)
        labels = th.cat(labels)
        g.edata['etype'] = etypes
        if self.graph_type == 'openai':
            g.ndata['pos_0'] = pos_arr // self.attrs['stride']
            g.ndata['pos_1'] = pos_arr % self.attrs['stride']
        else:
            g.ndata['pos'] = pos_arr

        g.nodes[leaf_ids].data['x'] = data

        return Batch(g=g, readout_ids=readout_ids, leaf_ids=leaf_ids, y=labels)

row.append(src)
            col.append(dst)
            etypes.append(th.from_numpy(etype))
            # update shift
            v_shift += g.number_of_nodes
            e_shift += g.number_of_edges

        n = v_shift
        leaf_ids = th.cat(leaf_ids)
        readout_ids = th.cat(readout_ids)
        pos_arr = th.cat(pos_arr)
        etypes = th.cat(etypes)
        row, col = map(np.concatenate, (row, col))
        coo = coo_matrix((np.zeros_like(row), (row, col)), shape=(n, n))
        g = dgl.DGLGraph(coo, readonly=True)
        g.set_n_initializer(dgl.init.zero_initializer)
        g.set_e_initializer(dgl.init.zero_initializer)

        data = th.cat(data)
        labels = th.cat(labels)
        g.edata['etype'] = etypes
        if self.graph_type == 'openai':
            g.ndata['pos_0'] = pos_arr // self.attrs['stride']
            g.ndata['pos_1'] = pos_arr % self.attrs['stride']
        else:
            g.ndata['pos'] = pos_arr

        g.nodes[leaf_ids].data['x'] = data

        return Batch(g=g, readout_ids=readout_ids, leaf_ids=leaf_ids, y=labels)

def _test(apply_func):
        g = generate_graph()
        f = g.ndata['f']

        # an out place run to get result
        g.pull(nodes,
               fn.copy_src(src='f', out='m'), fn.sum(msg='m', out='f'), apply_func)
        result = g.ndata['f']

        # inplace deg bucket
        v1 = F.clone(f)
        g.ndata['f'] = v1
        g.pull(nodes, message_func, reduce_func, apply_func, inplace=True)
        r1 = g.ndata['f']
        # check result
        assert F.allclose(r1, result)
        # check inplace
        assert F.allclose(v1, r1)

        # inplace v2v spmv
        v1 = F.clone(f)
        g.ndata['f'] = v1
        g.pull(nodes, fn.copy_src(src='f', out='m'),

def _test(red, partial):
        g = dgl.DGLGraph(nx.erdos_renyi_graph(100, 0.1))
        # NOTE(zihao): add self-loop to avoid zero-degree nodes.
        g.add_edges(g.nodes(), g.nodes())
        hu, hv, he = generate_feature(g, 'none', 'none')
        if partial:
            nid = F.tensor(list(range(0, 100, 2)))

        g.ndata['u'] = F.attach_grad(F.clone(hu))
        g.ndata['v'] = F.attach_grad(F.clone(hv))
        g.edata['e'] = F.attach_grad(F.clone(he))

        with F.record_grad():
            if partial:
                g.pull(nid, fn.copy_edge(edge='e', out='m'),
                       builtin[red](msg='m', out='r1'))
            else:
                g.update_all(fn.copy_edge(edge='e', out='m'),
                             builtin[red](msg='m', out='r1'))
            r1 = g.ndata['r1']
            F.backward(F.reduce_sum(r1))
            e_grad1 = F.grad(g.edata['e'])

        # reset grad
        g.ndata['u'] = F.attach_grad(F.clone(hu))
        g.ndata['v'] = F.attach_grad(F.clone(hv))
        g.edata['e'] = F.attach_grad(F.clone(he))

        with F.record_grad():
            if partial:
                g.pull(nid, udf_copy_edge, udf_reduce[red])

def test_edge_batch():
    d = 10
    g = dgl.DGLGraph(nx.path_graph(20))
    nfeat = F.randn((g.number_of_nodes(), d))
    efeat = F.randn((g.number_of_edges(), d))
    g.ndata['x'] = nfeat
    g.edata['x'] = efeat

    # test all
    eid = utils.toindex(slice(0, g.number_of_edges()))
    u, v, _ = g._graph.edges('eid')

    src_data = g.get_n_repr(u)
    edge_data = g.get_e_repr(eid)
    dst_data = g.get_n_repr(v)
    ebatch = EdgeBatch((u, v, eid), src_data, edge_data, dst_data)
    assert F.shape(ebatch.src['x'])[0] == g.number_of_edges() and\
        F.shape(ebatch.src['x'])[1] == d
    assert F.shape(ebatch.dst['x'])[0] == g.number_of_edges() and\

def test_local_var():
    g = DGLGraph(nx.path_graph(5))
    g.ndata['h'] = F.zeros((g.number_of_nodes(), 3))
    g.edata['w'] = F.zeros((g.number_of_edges(), 4))
    # test override
    def foo(g):
        g = g.local_var()
        g.ndata['h'] = F.ones((g.number_of_nodes(), 3))
        g.edata['w'] = F.ones((g.number_of_edges(), 4))
    foo(g)
    assert F.allclose(g.ndata['h'], F.zeros((g.number_of_nodes(), 3)))
    assert F.allclose(g.edata['w'], F.zeros((g.number_of_edges(), 4)))
    # test out-place update
    def foo(g):
        g = g.local_var()
        g.nodes[[2, 3]].data['h'] = F.ones((2, 3))
        g.edges[[2, 3]].data['w'] = F.ones((2, 4))
    foo(g)

def test_spmv_3d_feat():
    def src_mul_edge_udf(edges):
        return {'sum': edges.src['h'] * F.unsqueeze(F.unsqueeze(edges.data['h'], 1), 1)}

    def sum_udf(nodes):
        return {'h': F.sum(nodes.mailbox['sum'], 1)}

    n = 100
    p = 0.1
    a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
    g = dgl.DGLGraph(a)
    m = g.number_of_edges()

    # test#1: v2v with adj data
    h = F.randn((n, 5, 5))
    e = F.randn((m,))

    g.ndata['h'] = h
    g.edata['h'] = e
    g.update_all(message_func=fn.src_mul_edge('h', 'h', 'sum'), reduce_func=fn.sum('sum', 'h')) # 1
    ans = g.ndata['h']

    g.ndata['h'] = h
    g.edata['h'] = e
    g.update_all(message_func=src_mul_edge_udf, reduce_func=fn.sum('sum', 'h')) # 2
    assert F.allclose(g.ndata['h'], ans)

def test_batch_no_edge():
    g1 = dgl.DGLGraph()
    g1.add_nodes(6)
    g1.add_edges([4, 4, 2, 2, 0], [5, 3, 3, 1, 1])
    g2 = dgl.DGLGraph()
    g2.add_nodes(6)
    g2.add_edges([0, 1, 2, 5, 4, 5], [1 ,2 ,3, 4, 3, 0])
    g3 = dgl.DGLGraph()
    g3.add_nodes(1)  # no edges
    g = dgl.batch([g1, g3, g2]) # should not throw an error

def generate_graph():
    g = DGLGraph()
    for i in range(10):
        g.add_node(i, __REPR__=i+1) # 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)
    return g