Inference with DCG

After running this example dcg code

which randomly chooses the number of hidden layers [0, 3] each iteration, I printed the parameters in the network with print [x for x in model.parameters()] and it only shows the original nn.modules.

[Parameter containing:
 0.0682 -0.0155 -0.3447
 0.1916 -0.1639  0.1732
[torch.FloatTensor of size 2x3]
, Parameter containing:
 0.0412
-0.3786
[torch.FloatTensor of size 2]
, Parameter containing:
 0.3711  0.2467
-0.4274  0.6104
[torch.FloatTensor of size 2x2]
, Parameter containing:
 0.5531
 0.0944
[torch.FloatTensor of size 2]
, Parameter containing:
 0.0678 -0.4616
[torch.FloatTensor of size 1x2]
, Parameter containing:
 0.2338
[torch.FloatTensor of size 1]
]

It doesn’t show the dynamic layers used during training. Question: how does pytorch store the dynamic parameters? Are the dynamic parameters used during inference?