diff --git a/examples/monkeypatch.py b/examples/monkeypatch.py
deleted file mode 100644
index 052e3a94..00000000
--- a/examples/monkeypatch.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Example of patching a model after training to analyze it.
-
-This file shows how to load a pickled Python model after training
-and modify it to save and output the features after the first
-convolution for later analysis.
-"""
-
-from nequip.utils import Config, find_first_of_type
-from nequip.data import AtomicDataDict, AtomicData, dataset_from_config
-from nequip.nn import SequentialGraphNetwork, SaveForOutput
-from nequip.train import Trainer
-
-# The path to the original training session
-path = "../results/aspirin/minimal"
-
-# Load the model
-# there are other ways to do this, such as model_from_config etc.
-model = Trainer.load_model_from_training_session(traindir=path)
-
-# Find the SequentialGraphNetwork, which contains the
-# sequential bulk of the NequIP GNN model. To see the
-# structure of the GNN models, see
-# nequip/models/_eng.py
-sgn = find_first_of_type(model, SequentialGraphNetwork)
-
-# Now insert a SaveForOutput
-insert_after = "layer1_convnet"  # change this, again see nequip/models/_eng.py
-# `insert_from_parameters` builds the module for us from `shared_parameters`
-# You could also build it manually and use `insert`, but `insert_from_parameters`
-# has the advantage of constructing it with the correct input irreps
-# based on whatever comes before:
-sgn.insert_from_parameters(
-    after=insert_after,
-    name="feature_extractor",
-    shared_params=dict(
-        field=AtomicDataDict.NODE_FEATURES_KEY,
-        out_field="saved",
-    ),
-    builder=SaveForOutput,
-)
-
-# Now, we can test our patched model:
-# Load the original config file --- this could be a new one too:
-config = Config.from_file(path + "/config.yaml")
-# Load the dataset:
-# (Note that this loads the training dataset if there are separate training and validation datasets defined.)
-dataset = dataset_from_config(config)
-
-# Evaluate the model on a configuration:
-data = dataset[0]
-out = sgn(AtomicData.to_AtomicDataDict(data))
-
-# Check that our extracted data is there:
-assert "saved" in out
-print(out["saved"].shape)