def METHOD_NAME(self) -> None:
    model = nn.Sequential(nn.Linear(10, 10), nn.Linear(10, 10))
    opt_0_kwargs = {"lr": 0.03}
    opt_1_kwargs = {"lr": 0.01}
    opt_0 = torch.optim.SGD(model.parameters(), **opt_0_kwargs)
    opt_1 = torch.optim.SGD(model.parameters(), **opt_1_kwargs)
    model_with_opt_in_bwd = deepcopy(model)
    _apply_optimizer_in_backward(
        torch.optim.SGD,
        model_with_opt_in_bwd.parameters(),
        optimizer_kwargs=opt_0_kwargs,
    )
    _apply_optimizer_in_backward(
        torch.optim.SGD,
        model_with_opt_in_bwd.parameters(),
        optimizer_kwargs=opt_1_kwargs,
    )
    self._run_training_loop_and_validate(
        torch.randn(4, 10),
        [model, model_with_opt_in_bwd],
        [opt_0, opt_1],
    )