def METHOD_NAME(self):
    dtype = torch.float32
    device = get_random_cuda_device()
    verts = torch.tensor(
        [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=dtype, device=device
    )
    edges = torch.tensor([[0, 1], [0, 2]], device=device)
    w0 = torch.tensor([[1, 1, 1]], dtype=dtype, device=device)
    w1 = torch.tensor([[-1, -1, -1]], dtype=dtype, device=device)
    expected_y = torch.tensor(
        [
            [1 + 2 + 3 - 4 - 5 - 6 - 7 - 8 - 9],
            [4 + 5 + 6 - 1 - 2 - 3],
            [7 + 8 + 9 - 1 - 2 - 3],
        ],
        dtype=dtype,
        device=device,
    )
    conv = GraphConv(3, 1, directed=False).to(device)
    conv.w0.weight.data.copy_(w0)
    conv.w0.bias.data.zero_()
    conv.w1.weight.data.copy_(w1)
    conv.w1.bias.data.zero_()
    y = conv(verts, edges)
    self.assertClose(y, expected_y)