def METHOD_NAME(self, non_tensor_output):
    module = LinearNonTensorOutput(non_tensor_output)
    inputs = torch.rand(HIDDEN_DIM)
    # First return is a tensor
    ordering = [True]
    if type(non_tensor_output) in [list, tuple]:
        ordering += [torch.is_tensor(t) for t in non_tensor_output]
    else:
        ordering += [torch.is_tensor(non_tensor_output)]
    _test_activation_checkpoint_ordering(module, ordering, inputs)