Oh, yeah I can imagine that. Sometimes I spend quite some time on trivial issues until I realize how complicated my approach was. 
Yes, you have to call each layer before that selected one, since the activations are changing from layer to layer. In your case densenet.features.denseblock4.denselayer32 expects to have 1888 input channels.
So if you just want to call this layer, you should change the input to something like:
activation = Variable(torch.randn(1, 1888, 10, 10))
output = model.features.denseblock4.denselayer32(activation)

However, I don’t know the width and height of the activation. You could calculate it using all preceding layers or just use the for loop to get to your denselayer32 with the original input dimensions.