def METHOD_NAME(self, input_param, input_shape, _):
    for eps in [1e-4, 1e-5]:
        for momentum in [0.1, 0.01]:
            for affine in [True, False]:
                norm_param = {"eps": eps, "momentum": momentum, "affine": affine}
                input_param["norm_name"] = ("instance", norm_param)
                input_param_fuser = input_param.copy()
                input_param_fuser["norm_name"] = ("instance_nvfuser", norm_param)
                for memory_format in [torch.contiguous_format, torch.channels_last_3d]:
                    net = DynUNet(**input_param).to("cuda:0", memory_format=memory_format)
                    net_fuser = DynUNet(**input_param_fuser).to("cuda:0", memory_format=memory_format)
                    net_fuser.load_state_dict(net.state_dict())
                    input_tensor = torch.randn(input_shape).to("cuda:0", memory_format=memory_format)
                    with eval_mode(net):
                        result = net(input_tensor)
                    with eval_mode(net_fuser):
                        result_fuser = net_fuser(input_tensor)
                    assert_allclose(result, result_fuser, rtol=1e-4, atol=1e-4)