How to use the dgl.graph function in dgl

def test_repr():
    G = dgl.graph([(0,1), (0,2), (1,2)], card=10)
    repr_string = G.__repr__()
    print(repr_string)
    G.ndata['x'] = F.zeros((10, 5))
    G.edata['y'] = F.zeros((3, 4))
    repr_string = G.__repr__()
    print(repr_string)

assert len(attr) == len(ef) + 1 # extra id
                eid = attr['id']
                for k in ef:
                    edge_feat[k][eid] = F.unsqueeze(attr[k], 0)
            for k in edge_feat:
                feat = F.cat(edge_feat[k], 0)
                assert F.allclose(feat, ef[k])
        else:
            assert len(ef) == 0

    n1 = F.randn((5, 3))
    n2 = F.randn((5, 10))
    n3 = F.randn((5, 4))
    e1 = F.randn((4, 5))
    e2 = F.randn((4, 7))
    g = dgl.graph([(0,2),(1,4),(3,0),(4,3)])
    g.ndata.update({'n1': n1, 'n2': n2, 'n3': n3})
    g.edata.update({'e1': e1, 'e2': e2})

    # convert to networkx
    nxg = dgl.to_networkx(g, node_attrs=['n1', 'n3'], edge_attrs=['e1', 'e2'])
    assert len(nxg) == 5
    assert nxg.size() == 4
    _check_nx_feature(nxg, {'n1': n1, 'n3': n3}, {'e1': e1, 'e2': e2})

    # convert to DGLGraph, nx graph has id in edge feature
    # use id feature to test non-tensor copy
    g = dgl.graph(nxg, node_attrs=['n1'], edge_attrs=['e1', 'id'])
    # check graph size
    assert g.number_of_nodes() == 5
    assert g.number_of_edges() == 4
    # check number of features

2, 9, 3
    0, 3, 4
    3, 9, 5
    0, 4, 6
    4, 9, 7
    0, 5, 8
    5, 9, 9
    0, 6, 10
    6, 9, 11
    0, 7, 12
    7, 9, 13
    0, 8, 14
    8, 9, 15
    9, 0, 16
    '''
    g = dgl.graph([(0,1), (1,9), (0,2), (2,9), (0,3), (3,9), (0,4), (4,9),
                   (0,5), (5,9), (0,6), (6,9), (0,7), (7,9), (0,8), (8,9), (9,0)])
    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['w'] = ecol
    g.set_n_initializer(dgl.init.zero_initializer)
    g.set_e_initializer(dgl.init.zero_initializer)
    return g

def test_pull_0deg():
    g = dgl.graph([(0,1)])
    def _message(edges):
        return {'m' : edges.src['h']}
    def _reduce(nodes):
        return {'h' : nodes.data['h'] + F.sum(nodes.mailbox['m'], 1)}
    def _apply(nodes):
        return {'h' : nodes.data['h'] * 2}
    def _init2(shape, dtype, ctx, ids):
        return 2 + F.zeros(shape, dtype, ctx)
    g.set_n_initializer(_init2, 'h')
    # test#1: pull both 0deg and non-0deg nodes
    old = F.randn((2, 5))
    g.ndata['h'] = old
    g.pull([0, 1], _message, _reduce, _apply)
    new = g.ndata.pop('h')
    # 0deg check: initialized with the func and got applied
    assert F.allclose(new[0], F.full_1d(5, 4, dtype=F.float32))

fg = g['developer', :, 'game']
    assert fg.ntypes == ['developer', 'game']
    assert fg.etypes == ['develops']
    u1, v1 = g.edges(etype='develops', order='eid')
    u2, v2 = fg.edges(etype='develops', order='eid')
    assert F.array_equal(u1, u2)
    assert F.array_equal(v1, v2)

    fg = g[:, :, :]
    assert fg.ntypes == ['developer+user', 'game+user']
    assert fg.etypes == ['develops+follows+plays+wishes']
    check_mapping(g, fg)

    # Test another heterograph
    g_x = dgl.graph(([0, 1, 2], [1, 2, 3]), 'user', 'follows')
    g_y = dgl.graph(([0, 2], [2, 3]), 'user', 'knows')
    g_x.nodes['user'].data['h'] = F.randn((4, 3))
    g_x.edges['follows'].data['w'] = F.randn((3, 2))
    g_y.nodes['user'].data['hh'] = F.randn((4, 5))
    g_y.edges['knows'].data['ww'] = F.randn((2, 10))
    g = dgl.hetero_from_relations([g_x, g_y])

    assert F.array_equal(g.ndata['h'], g_x.ndata['h'])
    assert F.array_equal(g.ndata['hh'], g_y.ndata['hh'])
    assert F.array_equal(g.edges['follows'].data['w'], g_x.edata['w'])
    assert F.array_equal(g.edges['knows'].data['ww'], g_y.edata['ww'])

    fg = g['user', :, 'user']
    assert fg.ntypes == ['user']
    assert fg.etypes == ['follows+knows']
    check_mapping(g, fg)

def test_local_scope():
    g = dgl.graph([(0,1), (1,2), (2,3), (3,4)])
    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):
        with g.local_scope():
            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):
        with g.local_scope():
            g.nodes[[2, 3]].data['h'] = F.ones((2, 3))
            g.edges[[2, 3]].data['w'] = F.ones((2, 4))
    foo(g)

def test_local_var():
    g = dgl.graph([(0,1), (1,2), (2,3), (3,4)])
    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_recv_0deg_newfld():
    # test recv with 0deg nodes; the reducer also creates a new field
    g = dgl.graph([(0,1)])
    def _message(edges):
        return {'m' : edges.src['h']}
    def _reduce(nodes):
        return {'h1' : nodes.data['h'] + F.sum(nodes.mailbox['m'], 1)}
    def _apply(nodes):
        return {'h1' : nodes.data['h1'] * 2}
    def _init2(shape, dtype, ctx, ids):
        return 2 + F.zeros(shape, dtype=dtype, ctx=ctx)
    # test#1: recv both 0deg and non-0deg nodes
    old = F.randn((2, 5))
    g.set_n_initializer(_init2, 'h1')
    g.ndata['h'] = old
    g.send((0, 1), _message)
    g.recv([0, 1], _reduce, _apply)
    new = g.ndata.pop('h1')
    # 0deg check: initialized with the func and got applied

print(pa_g.number_of_edges(('paper', 'written-by', 'author')))
print(pa_g.number_of_edges('written-by'))
print(pa_g.successors(1, etype='written-by'))  # get the authors that write paper #1

# Type name argument could be omitted whenever the behavior is unambiguous.
print(pa_g.number_of_edges())  # Only one edge type, the edge type argument could be omitted

###############################################################################
# A homogeneous graph is just a special case of a heterograph with only one type
# of node and edge. In this case, all the APIs are exactly the same as in
# :class:`DGLGraph`.

# Paper-citing-paper graph is a homogeneous graph
pp_g = dgl.heterograph({('paper', 'citing', 'paper') : data['PvsP']})
# equivalent (shorter) API for creating homogeneous graph
pp_g = dgl.graph(data['PvsP'], 'paper', 'cite')

# All the ntype and etype arguments could be omitted because the behavior is unambiguous.
print(pp_g.number_of_nodes())
print(pp_g.number_of_edges())
print(pp_g.successors(3))

###############################################################################
# Create a subset of the ACM graph using the paper-author, paper-paper, 
# and paper-subject relationships.  Meanwhile, also add the reverse
# relationship to prepare for the later sections.

G = dgl.heterograph({
        ('paper', 'written-by', 'author') : data['PvsA'],
        ('author', 'writing', 'paper') : data['PvsA'].transpose(),
        ('paper', 'citing', 'paper') : data['PvsP'],
        ('paper', 'cited', 'paper') : data['PvsP'].transpose(),