You could just use the pre-trained model like any other nn.Module in your new model:
class MyPreTrainedModel(nn.Module):
    def __init__(self):
        super(MyPreTrainedModel, self).__init__()
        self.fc = nn.Linear(1, 1)
        
    def forward(self, x):
        return self.fc(x)

class MyModel(nn.Module):
    def __init__(self, model):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(1, 1)
        self.pre_trained = model
        self.fc2 = nn.Linear(1, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.pre_trained(x))
        x = self.fc2(x)
        return x

pre_trained = MyPreTrainedModel()
model = MyModel(pre_trained)
x = torch.randn(1, 1)
output = model(x)