continued_optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum)

The continued_optimizer variable is an instance of the SGD (Stochastic Gradient Descent) optimizer, which is used to update the parameters of a neural network during training.

The optimizer is initialized with the parameters of the network (network.parameters()), a learning rate (lr), and a momentum value (momentum). The learning rate determines the step size of the optimizer during parameter updates, while the momentum value controls the contribution of the previous updates to the current update.

By creating a new instance of the optimizer with the same network parameters, it is possible to continue training a previously trained network without starting from scratch. This can be useful for fine-tuning a network on a new dataset or for resuming training after a break.