You could add the classifier into the same model or use it as a separate one and I think it depends more on your coding style or which approach would be more convenient to use in the long run.
Anyway, assuming you want to use these two features and a single classifier on top, you could use something like:
class CNN1D(torch.nn.Module):
     def __init__(self, num_classes=7):
        super(CNN1D, self).__init__()
        ...
        self.classifier = nn.Linear(in_features, nb_classes)
        
    def forward(self, x):
        ...
        encoded = out
        
        ...
        out=F.relu(self.cnt6(out))
        # flatten encoded
        encoded = encoded.view(encoded.size(0), -1)
        # concatenate both features in the feature dimension
        out = torch.cat((encoded, out), dim=1)
        out = self.classifier(out)
        return out

Note that I’ve added the flattening operation to encoded, as I assume it’s a 3-dimensional tensor.
Also, you would have to check the in_features e.g. by adding a print statement in the forward and use this number in the self.classifier in_features.
You could of course use a “better” classifier by e.g. using nn.Sequential with more layers.