Hi all,

I am trying to train the autoencoder model that consists of two sub-encoders and one generic decoder (not concatenation!)

please see the attached figure.

Would the code below work? or please advise me how to train this!.

```
# Define individual modules
encoder1 = torch.nn.Linear(10, 4).to(device)
encoder2 = torch.nn.Linear(10, 4).to(device)
decoder = torch.nn.Linear(4, 10).to(device)
# Define each model
model1 = nn.Sequential(encoder1, decoder)
model2 = nn.Sequential(encoder2, decoder)
# Optimizer
optim_1 = torch.optim.Adam(model1 .parameters(), lr=learning_rate)
optim_2 = torch.optim.Adam(model2.parameters(), lr=learning_rate)
# Train
for epoch in range(epochs):
model1.train()
model2.train()
for i, data in enumerate(train_loader):
input1, output1 = data[:5]
input2, output2 = data[5:]
model1.zero_grad()
pred = model1(input1)
loss_1 = criterion(pred , output1)
loss_1 .backward()
model2.zero_grad()
pred = model2(input2)
loss_2 = criterion(pred , output2)
loss_2.backward()
```