I guess the user tried to suggest to add a new layer in your decoder, which would be used to output the prediction for the classification, which could then be used to train together with the reconstruction.
I.e. I think she meant something like this:
# original
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(10, 10),
            nn.ReLU(),
            nn.Linear(10, 2))
        self.decoder = nn.Sequential(
            nn.ReLU(),
            nn.Linear(2, 10),
            nn.ReLU(),
            nn.Linear(10, 10))
        
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

model = MyModel()
x = torch.randn(1, 10)
reconstruction = model(x)

# probably suggested
# original
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(10, 10),
            nn.ReLU(),
            nn.Linear(10, 2))
        self.decoder_features = nn.Sequential(
            nn.ReLU(),
            nn.Linear(2, 10),
            nn.ReLU())
        self.decoder_recon = nn.Linear(10, 10)
        self.decoder_classifier = nn.Linear(10, 10)
        
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder_features(x)
        reconstruction = self.decoder_recon(x)
        logits = self.decoder_classifier(x)
        return reconstruction, logits

model = MyModel()
x = torch.randn(1, 10)
reconstruction, logits = model(x)
# calculate losses for both using corresponding loss functions
loss = loss_recon + loss_classifier
loss.backward()

Of course your model would look different and more sophisticated than this example.