nn.ParallelTable applies the

i -th member module to the  i -th input and outputs a  table

so it seems these layers are not used sequentially but in parallel (which also the name suggests  ).
Given that, I assume you have 12 different inputs, so that you could feed each one to a layer?
Your input shape could therefore be [batch_size, 12, nb_features].
If that’s the case, you could use a loop via:
output = []
for idx in range(12):
    data = x1[:, idx]
    module = self.linears[idx]
    out = module(data)
    outputs.append(out)
outputs = torch.stack(outputs)

I’m not sure, in which dimension the JoinTable concatenates the output tensors, so you might want to use torch.stack(outputs, dim=?) (or torch.cat) with a specific dimension argument.
Could you post all shapes of all tensors from the Torch model, so that we could try to create the equivalent PyTorch model?