diff --git a/configs/model/cell/can.yaml b/configs/model/cell/can.yaml
index 85ca8d84..7ce1eeae 100755
--- a/configs/model/cell/can.yaml
+++ b/configs/model/cell/can.yaml
@@ -17,7 +17,7 @@ backbone:
   heads: 4 # For now we stuck to out_channels//heads
   concat: True
   skip_connection: True
-  n_layers: 1
+  n_layers: 4
   att_lift: False
 
 loss:
diff --git a/configs/train.yaml b/configs/train.yaml
index 01144a49..cb72c21b 100755
--- a/configs/train.yaml
+++ b/configs/train.yaml
@@ -5,7 +5,7 @@
 defaults:
   - _self_
   - dataset: ZINC
-  - model: cell/cwn #hypergraph/unignn2 #
+  - model: cell/can #hypergraph/unignn2 #
   - evaluator: default
   - callbacks: default
   - logger: wandb # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
diff --git a/topobenchmarkx/models/wrappers/default_wrapper.py b/topobenchmarkx/models/wrappers/default_wrapper.py
index c05c75eb..eb6db0aa 100755
--- a/topobenchmarkx/models/wrappers/default_wrapper.py
+++ b/topobenchmarkx/models/wrappers/default_wrapper.py
@@ -274,11 +274,11 @@ def __call__(self, batch):
         """Define logic for forward pass"""
         model_out = {"labels": batch.y, "batch": batch.batch}
         x_1 = self.backbone(
-            batch.x,
-            batch.x_1,
-            batch.adjacency_0.coalesce(),
-            batch.down_laplacian_1.coalesce(),
-            batch.up_laplacian_1.coalesce(),
+            x_0=batch.x_0,
+            x_1=batch.x_1,
+            adjacency_0=batch.adjacency_0.coalesce(),
+            down_laplacian_1=batch.down_laplacian_1.coalesce(),
+            up_laplacian_1=batch.up_laplacian_1.coalesce(),
         )
         x_0 = torch.sparse.mm(batch.incidence_1, x_1)
 
@@ -321,9 +321,9 @@ def __call__(self, batch):
             x_0=batch.x_0,
             x_1=batch.x_1,
             x_2=batch.x_2,
-            neighborhood_0_to_1=batch.incidence_1.T,
-            neighborhood_1_to_1=batch.adjacency_1,
-            neighborhood_2_to_1=batch.incidence_2,
+            incidence_1_t=batch.incidence_1.T,
+            adjacency_0=batch.adjacency_1,
+            incidence_2=batch.incidence_2,
         )
 
         model_out["x_0"] = torch.mm(
@@ -346,8 +346,8 @@ def __call__(self, batch):
         x_0, x_1, x_2 = self.backbone(
             x_0=batch.x_0,
             x_1=batch.x_1,
-            neighborhood_0_to_0=batch.adjacency_0,
-            neighborhood_1_to_2=batch.incidence_2.T,
+            adjacency_0=batch.adjacency_0,
+            incidence_2_t=batch.incidence_2.T,
         )
         model_out["x_0"] = x_0
         model_out["x_1"] = x_1