I asked here 10 but got no answer. Maybe it’s a topic which is many years ago, and my question is a little bit inconsistent with the original one. So I’m sorry but ask again here.
My question is,
it is said that when regularization L2, it should  only  for  weight parameters , but not  bias parameters .(if regularization L2 is for all parameters, it’s very easy for the model to become overfitting, is it right?)
But the L2 regularization included in most optimizers in PyTorch, is for all of the parameters in the model (weight and bias).

I mean the parameters in the red box should be weight parameters only. (If what I heard of is right.)
And the way to deal with it is code here:
weight_p, bias_p = [],[]
for name, p in model.named_parameters():
  if 'bias' in name:
    bias_p += [p]
  else:
    weight_p += [p]

optim.SGD(
  [
    {'params': weight_p, 'weight_decay':1e -5},
    {'params': bias_p, 'weight_decay':0}
  ],
  lr=1e-2, momentum=0.9
)

is it right?