Yes, it depends how the hand-crafted features are computed. If you use some functions and arrays outside PyTorch, like what I did in NumPy, and then convert them to PyTorch tensors, they will not be in the « computation-graph ».  So, in this setting, the actual features will not be back-propped, but only their final weights in the vggx.classifier will be back-proped.
However, if you want the hand-crafted features to also be included in the back-propagation, then you should define them in tensors and all the operations applied to them should use pytorch modules and learnable parameters. Then, those will also be automatically backpropped.