Wrapping child modules into an nn.Sequential container will only work in simple use cases, where each module is called sequentially and no functional calls are used in the forward method.
As you can see here 4, the Inception model uses some functional pooling layers, conditions, dropout, and a flattening inside the forward. All these functional API calls will be missing in your feature_extractor.
You could thus derive a custom model and manipulate the forward method as you wish or alternatively replace the unwanted layers with an nn.Identity to get the desired output.