Since features is a nn.Sequential class, you can access its modules and get the name:
for name, layer in model.features.named_modules():
    print('{}, type: {}'.format(name, type(layer)))

I’m not familiar with the model architecture, but it seems that _Denseblocks have some internal _Denselayers. So this loop will return them all:
...
denseblock4.denselayer32, type: <class 'torchvision.models.densenet._DenseLayer'>
denseblock4.denselayer32.norm.1, type: <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
...

Depending on your use case you could iterate the model using its children:
for name, child in model.features.named_children():
    print('{}, type: {}'.format(name, type(child)))
    if name == 'denseblock4':
        for n, c in child.named_children():
            print(n, c)