I don’t think there is a parser, as the forward method is undefined.
I.e. while the submodules are created, it’s unclear how they are used (e.g. in a sequential manner or are different paths used inside the forward).
Assuming these layers are used in a sequential manner, you could recreate the modules by wrapping them in nn.Sequential containers, e.g.
self.layer1 = nn.Sequential(
    nn.Conv1d(12, 128, 8, 1, 3, bias=False),
    nn.BatchNorm1d(128),
    nn.ReLU(inplace=True)

One unknown would be the AdaptiveConcatPool1d, which doesn’t seem to be a core PyTorch layer, but seems to be defined in FastAI.