auto layers = some<CNeuralLayers>();
//Generate layers 1 by 1
//...
//Done generating layers
auto all_layers = layers->done();


//Now we generate the Neural network
auto network = some<CNeuralNetwork>(all_layers);
network->quick_connect()
network->initialize_neural_network()
network->set_labels(labels_train)
//... L1/L2/Learning Rate


//Predict the value of network for features_train
auto labels_predict = network->apply_regression(features_train);

//Define loss founction similar to the way it is done in the unit test of FirstOrderSAGCostFunctionInterface
auto loss = some<FirstOrderSAGCostFunctionInterface>();
loss->set_training_data(features_train, labels_test);
loss->set_ith_cost_function(ith_cost_function); //ith cost function is any function that defines the loss like in the unit test of the new pr
loss->set_total_cost_function(total_cost_function); //check new pr for examples of ith_cost_function and total_cost_function

//Now we generate an Adam updater and SGD minimizer to minimize loss
auto adam = AdamUpdater();
auto optimizer = SGDMinimizer(loss); //Minimize the loss defined up
optimizer.set_gradient_updater(adam);
optimizer.set_learning_rate(0.001);

//Now minimize the loss by utilizing stan auto diff
optimizer.minimize();