diff --git a/tests/test_library_transforms.py b/tests/test_library_transforms.py
index 66ffc128..f26f3a31 100644
--- a/tests/test_library_transforms.py
+++ b/tests/test_library_transforms.py
@@ -46,6 +46,7 @@ def test_config():
     cfg3 = ArrowConfig(ab_only=True, scale=False)
     assert cfg3.save_name == cfg.save_name
 
+
 def test_knot_merge(tmp_path, create_dummy_expert):
     config = ExpertConfig(
         **{
@@ -64,16 +65,19 @@ def test_knot_merge(tmp_path, create_dummy_expert):
         }
     )
     model = ExpertModel(ExpertModelConfig(base_model="EleutherAI/gpt-neo-125m"))
-    
+
     config.finetune_task_name = "cot_creak"
     expert1 = create_dummy_expert(config, "cot_creak")
 
     config.finetune_task_name = "cot_creak_ii"
     expert2 = create_dummy_expert(config, "cot_creak_ii")
     # only leave 1 layer to speed up things.
-    expert1.expert_weights = {k:v for k,v in expert1.expert_weights.items() if "8.mlp" in k}
-    expert2.expert_weights = {k:v for k,v in expert2.expert_weights.items() if "8.mlp" in k}   
-    
+    expert1.expert_weights = {
+        k: v for k, v in expert1.expert_weights.items() if "8.mlp" in k
+    }
+    expert2.expert_weights = {
+        k: v for k, v in expert2.expert_weights.items() if "8.mlp" in k
+    }
 
     library = LocalExpertLibrary(tmp_path)
     library.add_expert(expert1)
@@ -82,13 +86,13 @@ def test_knot_merge(tmp_path, create_dummy_expert):
     transform = KnotMerge(KnotMergeConfig(path=f"{tmp_path}/knot_ingredients.pt"))
     exp = transform.transform(library)
     state_dict = model.model.state_dict()
-    
-    # TODO: this can be implemented as a seperate modifier maybe or utils func.    
+
+    # TODO: this can be implemented as a seperate modifier maybe or utils func.
     merged_layers = []
     for p_name, value in exp.expert_weights.items():
         if p_name in state_dict:
             merged_layers.append(p_name)
-            state_dict[p_name]+=value
+            state_dict[p_name] += value
     assert len(merged_layers) == len(exp.expert_weights.keys()) == 1