Different learning rates for pretrained densenet

What is the proper way to set different learning rates for different dense blocks of densenet? I am confused because these are nested under


section inside densenet:

The hierarchy is as following:

(features): Sequential(
    (conv0): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (relu0): ReLU(inplace)
    (pool0): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
    (denseblock1): _DenseBlock(),
    (transition1): _Transition(),

Should I send this to the optimizer?

{'params': densenet.features.conv0},
{other layers},
{'params': densenet.features.denseblock1},
{'params': densenet.features.transition1},
{'params': densenet.classifier}]