create_feature_extractor, which is where all the magic happens, goes a few steps further than get_graph_node_names. It takes desired node names as one of the input arguments, and then uses more FX core functionality to:

Assign the desired nodes as outputs.
Prune unnecessary downstream nodes and their associated parameters.
Translate the resulting graph back into Python code.
Return another PyTorch Module to the user. This has the python code from step 3 as the forward method.

As a demonstration, here’s how we would apply create_feature_extractor to get the 4 feature maps from our toy CNN model
```python
from torchvision.models.feature_extraction import create_feature_extractor
Confused about the node specification here?
We are allowed to provide truncated node names, and create_feature_extractor
will choose the last node with that prefix.