You could implement the posted model architecture using nn.ModuleLists as seen here:
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoders = nn.ModuleList()
        for _ in range(9):
            self.encoders.append(nn.Linear(4, 3))
        
        self.encoder = nn.Sequential(
            nn.Linear(9*3, 4),
            nn.ReLU(),
            nn.Linear(4, 3)
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, 4),
            nn.ReLU(),
            nn.Linear(4, 9*3)
        )
        self.decoders = nn.ModuleList()
        for _ in range(9):
            self.decoders.append(nn.Linear(3, 4))
        
    def forward(self, inputs):
        out = []
        for idx, enc in enumerate(self.encoders):
            out.append(enc(inputs[idx]))
        out = torch.cat(out, dim=1)
        z = self.encoder(out)
        out = self.decoder(z)
        out = torch.split(out, 3, dim=1)
        outs = []
        for idx, dec in enumerate(self.decoders):
            outs.append(dec(out[idx]))
        return outs
            
model = MyModel()
inputs = [torch.randn(1, 4) for _ in range(9)]
outs = model(inputs)

I don’t know which activation functions should be used etc. so you could use this code snippet as a base implementation and adapt it to your use case.