def METHOD_NAME(iter_num=3):
    linear = flow.nn.Linear(3, 8)
    linear = linear.to(device)
    flow.nn.init.constant_(linear.weight, 2.068758)
    flow.nn.init.constant_(linear.bias, 0.23)
    of_sgd = flow.optim.SGD(linear.parameters(), lr=0.001, momentum=0.9)
    x = flow.tensor(
        [
            [-0.94630778, -0.83378579, -0.87060891],
            [2.0289922, -0.28708987, -2.18369248],
            [0.35217619, -0.67095644, -1.58943879],
            [0.08086036, -1.81075924, 1.20752494],
            [0.8901075, -0.49976737, -1.07153746],
            [-0.44872912, -1.07275683, 0.06256855],
            [-0.22556897, 0.74798368, 0.90416439],
            [0.48339456, -2.32742195, -0.59321527],
        ],
        dtype=flow.float32,
        device=device,
        requires_grad=False,
    )
    def one_iter():
        of_out = linear(x)
        of_out = of_out.sum()
        of_out.backward()
        of_sgd.step()
        of_sgd.zero_grad()
        return of_out.numpy(), linear.weight.numpy()
    check_list = []
    for i in range(iter_num):
        check_list.append(one_iter())
    return check_list