hey @afshin67,
glad you ask .  Since torch::nn::Sequential 1  is a
std::vector of torch::nn::AnyModule 1, we can access all function of the torch::nn::Module 2 class.
For example, to access the names and parameters, do
#include <torch/torch.h>

using namespace torch;
using namespace torch::nn;

int main()
{
	auto net = Sequential(Conv2d(1 /*input channels*/, 1 /*output channels*/, 2 /*kernel size*/),
                              Conv2d(1, 1, 2));

	for (auto& p : net->named_parameters()) {
	
		NoGradGuard no_grad;

		// Access name.
		std::cout << p.key() << std::endl;

		// Access weigth and bias.
		p.value().zero_(); // set all zero
		std::cout << p.value() << std::endl;
	}

	return 0;
}

The layers of a sequential, have the following naming convention: <number layer>.<weight/bias>, e.g. see the console output
0.weight # name of the layer
(1,1,.,.) = 
  0  0
  0  0
[ Variable[CPUFloatType]{1,1,2,2} ]
0.bias
 0
[ Variable[CPUFloatType]{1} ]
1.weight
(1,1,.,.) = 
  0  0
  0  0
[ Variable[CPUFloatType]{1,1,2,2} ]
1.bias
 0
[ Variable[CPUFloatType]{1} ]