Now I got your confusion. A torch::nn::Sequential already implements this for you. Go ahead and check out the implementation 9 of it. There you will find the line
/// A `ModuleHolder` subclass for `SequentialImpl`.
/// See the documentation for `SequentialImpl` class to learn what methods it
/// provides, or the documentation for `ModuleHolder` to learn about PyTorch's
/// module storage semantics.
TORCH_MODULE(Sequential);

In other words, to save or load a Sequential, just do
// Just a simple example Sequential
auto MySequential = torch::nn::Sequential(torch::nn::Conv2d(1 /*input channels*/, 1 /*output channels*/, 2 /*kernel size*/),
					  torch::nn::Conv2d(1, 1, 2));

// Save the model
torch::save(MySequential, "model.pt");

// Load the model
torch::load(MySequential, "model.pt");