def METHOD_NAME(self):
    torch.random.manual_seed(0)
    x = torch.randn(2, 50)
    module = DefaultModuleAdapter()
    module.add_adapter(name='temp', cfg=get_adapter_cfg())
    module.train()
    adapter = module.adapter_layer[module.get_enabled_adapters()[0]]
    # update the strategy
    adapter_strategy = adapter_mixin_strategies.ResidualAddAdapterStrategy(l2_lambda=0.01)
    adapter.adapter_strategy = adapter_strategy
    with torch.no_grad():
        access_mixins.AccessMixin.reset_registry(module)
        assert access_mixins.AccessMixin.is_access_enabled() is False
        assert adapter_strategy.stochastic_depth == 0.0
        assert adapter_strategy.l2_lambda > 0.0
        out = adapter_strategy.forward(x, adapter, module=module)
        assert (out - x).abs().mean() < 1e-5
        # extract losses
        assert access_mixins.AccessMixin.is_access_enabled() is True
        auxiliary_losses = access_mixins.AccessMixin.get_module_registry(module)
        loss = list(auxiliary_losses.values())[0]
        assert 'adapter_loss' in loss
        assert loss['adapter_loss'][0] == torch.tensor(0.0)  # initially adapter is 0 init, no loss required.