def METHOD_NAME(self, stochastic_depth):
    torch.random.manual_seed(0)
    x = torch.randn(2, 50)
    module = DefaultModuleAdapter()
    module.add_adapter(name='temp', cfg=get_adapter_cfg())
    # extract adapter
    adapter = module.adapter_layer[module.get_enabled_adapters()[0]]
    # reinitialize the final layer of the adapter module (so that it is not zero init)
    adapter.module[-1].weight.data += 1
    # get just module output
    module.set_enabled_adapters('temp', enabled=False)
    module_out = module(x)
    # get module + adapter output
    module.set_enabled_adapters('temp', enabled=True)
    module_adapter_out = module(x)
    assert (
        module_out - module_adapter_out
    ).abs().sum() > 0  # results should not be the same after adapter forward now
    adapter_strategy = adapter_mixin_strategies.ResidualAddAdapterStrategy(stochastic_depth=stochastic_depth)
    adapter.adapter_strategy = adapter_strategy
    module.eval()
    with torch.inference_mode():  # stochastic depth disabled, no grad tracking
        assert adapter.adapter_strategy.stochastic_depth == stochastic_depth
        out = adapter_strategy.forward(module_out, adapter, module=module)
        assert (out - module_adapter_out).abs().mean() < 1e-5
    module.train()
    with torch.inference_mode():  # stochastic depth enabled, but no grad tracking during training mode
        out = adapter_strategy.forward(module_out, adapter, module=module)
        if stochastic_depth == 0.0:
            check = module_adapter_out
        else:
            check = module_out
        assert (out - check).abs().mean() < 1e-5