--- a
+++ b/medicalbert/classifiers/util.py
@@ -0,0 +1,19 @@
+##These functions allow us to delete layers from a model,
+## Or if not possible, replace with an identity function,
+## So that it has no trainable parameters/effect.
+from torch import nn
+import copy
+
+def deleteEncodingLayers(model, num_layers_to_keep):  # must pass in the full bert model
+    oldModuleList = model.bert.encoder.layer
+    newModuleList = nn.ModuleList()
+
+    # Now iterate over all layers, only keepign only the relevant layers.
+    for i in range(0, num_layers_to_keep):
+        newModuleList.append(oldModuleList[i])
+
+    # create a copy of the model, modify it with the new list, and return
+    copyOfModel = copy.deepcopy(model)
+    copyOfModel.bert.encoder.layer = newModuleList
+
+    return copyOfModel
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