def METHOD_NAME(target, dev):
    backbone = _get_backbone()
    pred_sinfo = relax.TensorStructInfo((1, 5), "float32")
    setup_trainer = SetupTrainer(
        MSELoss(reduction="sum"),
        SGD(0.01),
        [pred_sinfo, pred_sinfo],
    )
    train_mod = setup_trainer(backbone)
    ex = relax.build(train_mod, target)
    vm = relax.VirtualMachine(ex, dev)
    trainer = Trainer(train_mod, vm, dev, False)
    trainer.xaiver_uniform_init_params()
    dataset = _make_dataset()
    for input, label in dataset:
        trainer.update(input, label)
    param_dict = trainer.export_params()
    assert "w0" in param_dict
    assert "b0" in param_dict
    trainer1 = Trainer(train_mod, vm, dev, False)
    trainer1.load_params(param_dict)
    x_sample = dataset[np.random.randint(len(dataset))][0]
    tvm.testing.assert_allclose(
        trainer.predict(x_sample).numpy(), trainer1.predict(x_sample).numpy()
    )