will choose the last node with that prefix.
feature_extractor = create_feature_extractor(
    model, return_nodes=['blocks.0', 'blocks.1', 'blocks.2', 'blocks.3'])
out will be a dict of Tensors, each representing a feature map
out = feature_extractor(torch.zeros(1, 3, 32, 32))
```
It’s as simple as that. When it comes down to it, FX feature extraction is just a way of making it possible to do what some of us would have naively hoped for when we first started programming: “just give me the output of this code (points finger at screen)”*.

[ ] … does not require us to fiddle with source code.
[ ] … provides full flexibility in terms of accessing any intermediate transformation of our inputs, whether they are the results of a module or a functional operation
[ ] … does drop unnecessary computations steps once features have been extracted
[ ] … and I didn’t mention this before, but it’s also TorchScript friendly!

Here’s that table again with another row added for FX feature extraction