can you help me to achieve this goal for this code???
#Define the Convolutional Autoencoder
class ConvAutoencoder(nn.Module):
def init(self):
super(ConvAutoencoder, self).init()
    #Encoder
    self.conv1 = nn.Conv2d(3, 16, 3, stride=2, padding=1)
    self.conv2 = nn.Conv2d(16, 8, 3, stride=2, padding=1)
    self.conv3 = nn.Conv2d(8,8,3)

    #Decoder
    self.conv4 = nn.ConvTranspose2d(8, 8, 3)
    self.conv5 = nn.ConvTranspose2d(8, 16, 3, stride=2, padding=1, output_padding=1)
    self.conv6 = nn.ConvTranspose2d(16, 3, 3, stride=2, padding=1, output_padding=1)

def forward(self, x):
    x = F.relu(self.conv1(x))      
    x = F.relu(self.conv2(x))
    x = F.relu(self.conv3(x))  ***# in this output layer i need to zeroing end of row and column and repeat above row and column in this row and column....***
   
    x = F.relu(self.conv4(x))
    x = F.relu(self.conv5(x))
    x = F.relu(self.conv6(x))

    return x

model = ConvAutoencoder()
print(model)
def train(model, num_epochs=5, batch_size=64, learning_rate=1e-3):
torch.manual_seed(42)

criterion = nn.MSELoss() # mean square error loss

optimizer = torch.optim.Adam(model.parameters(),

                             lr=learning_rate, 

                             weight_decay=1e-5) # <--


train_loader =train_loader;
outputs = []

for epoch in range(num_epochs):

    for data in train_loader:
        img, _ = data
        recon = model(img)
        loss = criterion(recon, img)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

    print('Epoch:{}, Loss:{:.4f}'.format(epoch+1, float(loss)))
    outputs.append((epoch, img, recon),)
return outputs

model =  ConvAutoencoder()
max_epochs = 5
outputs = train(model, num_epochs=max_epochs)