diff --git a/scripts/nips_workshop/typing_classification.py b/scripts/nips_workshop/typing_classification.py
new file mode 100644
index 00000000..982ecf51
--- /dev/null
+++ b/scripts/nips_workshop/typing_classification.py
@@ -0,0 +1,74 @@
+import torch
+
+import espaloma as esp
+
+
+def run(layer_name):
+    # grab dataset
+    # esol = esp.data.esol(first=20)
+    ds = esp.data.zinc(first=1000).shuffle()
+
+    # do some typing
+    typing = esp.graphs.legacy_force_field.LegacyForceField('gaff-1.81')
+    ds.apply(typing, in_place=True) # this modify the original data
+
+    # split
+    # NOTE:
+    # I don't like torch-generic splitting function as it requires
+    # specifically the volume of each partition and it is inconsistent
+    # with the specification of __getitem__ method
+    ds_tr, ds_te = ds.split([4, 1])
+
+    ds_tr.save('ds_tr.th')
+    ds_te.save('ds_te.th')
+
+    # get a loader object that views this dataset in some way
+    # using this specific flag the dataset turns into an iterator
+    # that outputs loss function, per John's suggestion
+    ds_tr = ds_tr.view('graph', batch_size=20)
+    ds_te = ds_te.view('graph', batch_size=len(ds_te))
+
+    # define a layer
+    layer = esp.nn.layers.dgl_legacy.gn(layer_name)
+
+    # define a representation
+    representation = esp.nn.Sequential(
+            layer,
+            [32, 'leaky_relu', 32, 'leaky_relu', 32, 'leaky_relu']
+    )
+
+    # define a readout
+    readout = esp.nn.readout.node_typing.NodeTyping(
+            in_features=32,
+            n_classes=100
+    ) # not too many elements here I think?
+
+    net = torch.nn.Sequential(
+        representation,
+        readout
+    )
+
+
+    exp = esp.TrainAndTest(
+        ds_tr=ds_tr,
+        ds_te=ds_te,
+        net=net,
+        metrics_te=[esp.metrics.TypingAccuracy()],
+        n_epochs=500,
+    )
+
+    results = exp.run()
+
+    print(esp.app.report.markdown(results))
+
+    import pickle
+    with open(layer_name + "_ref_g_test.th", "wb") as f_handle:
+        pickle.dump(exp.ref_g_test, f_handle)
+
+    with open(layer_name + "_ref_g_training.th", "wb") as f_handle:
+        pickle.dump(exp.ref_g_training, f_handle)
+
+
+if __name__ == '__main__':
+    import sys
+    run(sys.argv[1])