optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)

This line of code initializes an optimizer object called "optimizer" using the stochastic gradient descent (SGD) algorithm with the following parameters:

- g0: the initial values of the model parameters (i.e., the weights and biases)
- lr: the learning rate, which determines the step size taken during each iteration of the optimization algorithm
- momentum: a parameter that controls how much the optimizer relies on the previous updates to the model parameters when computing the current update
- nesterov: a Boolean flag that determines whether to use Nesterov momentum, which is a modification of the standard momentum algorithm that can improve convergence speed and accuracy in some cases.

Overall, this line of code sets up an optimizer that will be used to update the model parameters during training in order to minimize a given loss function.