How to use the dgl.frame.Frame function in dgl

def test_append1():
    # test append API on Frame
    data = create_test_data()
    f1 = Frame()
    f2 = Frame(data)
    f1.append(data)
    assert f1.num_rows == N
    f1.append(f2)
    assert f1.num_rows == 2 * N
    c1 = f1['a1']
    assert tuple(F.shape(c1.data)) == (2 * N, D)
    truth = F.cat([data['a1'], data['a1']], 0)
    assert F.allclose(truth, c1.data)
    # append dict of different length columns should fail
    f3 = {'a1' : F.zeros((3, D)), 'a2' : F.zeros((3, D)), 'a3' : F.zeros((2, D))}
    def failed_append():
        f1.append(f3)
    assert check_fail(failed_append)

def test_column1():
    # Test frame column getter/setter
    data = create_test_data()
    f = Frame(data)
    assert f.num_rows == N
    assert len(f) == 3
    assert F.allclose(f['a1'].data, data['a1'])
    f['a1'] = data['a2']
    assert F.allclose(f['a2'].data, data['a2'])
    # add a different length column should fail
    def failed_add_col():
        f['a4'] = F.zeros([N+1, D])
    assert check_fail(failed_add_col)
    # delete all the columns
    del f['a1']
    del f['a2']
    assert len(f) == 1
    del f['a3']
    assert len(f) == 0

def test_row1():
    # test row getter/setter
    data = create_test_data()
    f = FrameRef(Frame(data))

    # getter
    # test non-duplicate keys
    rowid = Index(F.tensor([0, 2]))
    rows = f[rowid]
    for k, v in rows.items():
        assert tuple(F.shape(v)) == (len(rowid), D)
        assert F.allclose(v, F.gather_row(data[k], F.tensor(rowid.tousertensor())))
    # test duplicate keys
    rowid = Index(F.tensor([8, 2, 2, 1]))
    rows = f[rowid]
    for k, v in rows.items():
        assert tuple(F.shape(v)) == (len(rowid), D)
        assert F.allclose(v, F.gather_row(data[k], F.tensor(rowid.tousertensor())))

    # setter

def test_create():
    data = create_test_data()
    f1 = Frame(num_rows=N)
    for k, v in data.items():
        f1.update_column(k, v)
    print(f1.schemes)
    assert f1.keys() == set(data.keys())
    assert f1.num_columns == 3
    assert f1.num_rows == N
    f2 = Frame(data)
    assert f2.keys() == set(data.keys())
    assert f2.num_columns == 3
    assert f2.num_rows == N
    f1.clear()
    assert len(f1.schemes) == 0
    assert f1.num_rows == 0

def test_create():
    data = create_test_data()
    f1 = Frame(num_rows=N)
    for k, v in data.items():
        f1.update_column(k, v)
    print(f1.schemes)
    assert f1.keys() == set(data.keys())
    assert f1.num_columns == 3
    assert f1.num_rows == N
    f2 = Frame(data)
    assert f2.keys() == set(data.keys())
    assert f2.num_columns == 3
    assert f2.num_rows == N
    f1.clear()
    assert len(f1.schemes) == 0
    assert f1.num_rows == 0

def test_slicing():
    data = Frame(create_test_data(grad=True))
    f1 = FrameRef(data, index=toindex(slice(1, 5)))
    f2 = FrameRef(data, index=toindex(slice(3, 8)))
    # test read
    for k, v in f1.items():
        assert F.allclose(F.narrow_row(data[k].data, 1, 5), v)
    f2_a1 = f2['a1']    # is a tensor
    # test write
    f1[Index(F.tensor([0, 1]))] = {
            'a1': F.zeros([2, D]),
            'a2': F.zeros([2, D]),
            'a3': F.zeros([2, D]),
            }
    assert F.allclose(f2['a1'], f2_a1)

    f1[Index(F.tensor([2, 3]))] = {
            'a1': F.ones([2, D]),

def test_pickling_frame():
    x = F.randn((3, 7))
    y = F.randn((3, 5))

    c = Column(x)

    c2 = _reconstruct_pickle(c)
    assert F.allclose(c.data, c2.data)

    fr = Frame({'x': x, 'y': y})

    fr2 = _reconstruct_pickle(fr)
    assert F.allclose(fr2['x'].data, x)
    assert F.allclose(fr2['y'].data, y)

    fr = Frame()

def test_add_rows():
    data = Frame()
    f1 = FrameRef(data)
    f1.add_rows(4)
    x = F.randn((1, 4))
    f1[Index(F.tensor([0]))] = {'x': x}
    ans = F.cat([x, F.zeros((3, 4))], 0)
    assert F.allclose(f1['x'], ans)
    f1.add_rows(4)
    f1[toindex(slice(4, 8))] = {'x': F.ones((4, 4)), 'y': F.ones((4, 5))}
    ans = F.cat([ans, F.ones((4, 4))], 0)
    assert F.allclose(f1['x'], ans)
    ans = F.cat([F.zeros((4, 5)), F.ones((4, 5))], 0)
    assert F.allclose(f1['y'], ans)

def test_row3():
    # test row delete
    data = Frame(create_test_data())
    f = FrameRef(data)
    assert f.is_contiguous()
    assert f.is_span_whole_column()
    assert f.num_rows == N
    del f[toindex(F.tensor([2, 3]))]
    assert not f.is_contiguous()
    assert not f.is_span_whole_column()
    # delete is lazy: only reflect on the ref while the
    # underlying storage should not be touched
    assert f.num_rows == N - 2
    assert data.num_rows == N
    newidx = list(range(N))
    newidx.pop(2)
    newidx.pop(2)
    newidx = toindex(newidx)
    for k, v in f.items():