Yes here is the model code you could use
class classification(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        self.AE = AE(kwargs)
        self.AE.load_state_dict(FILENAME)
        self.classifier = nn.Sequential(nn.Linear(INPUT,OUPUT))
        ##You can use as many linear layers and other activations as you want
    def forward(self, input):
        x = self.AE(input)
        output = self.classifier(output)
        ##You can use a sigmoid or softmax here if neecd
        return output

and then the optimizer
optimizer = torch.optim.Adam(model.classifier.parameters(), lr=LR)