diff --git a/test/nn/test_to_hetero_transformer.py b/test/nn/test_to_hetero_transformer.py
index 31f111821a68..51831addc441 100644
--- a/test/nn/test_to_hetero_transformer.py
+++ b/test/nn/test_to_hetero_transformer.py
@@ -5,7 +5,7 @@
 from torch.nn import Linear, ReLU, Sequential
 from torch_sparse import SparseTensor
 
-from torch_geometric.nn import BatchNorm, GINEConv
+from torch_geometric.nn import BatchNorm, GCNConv, GINEConv
 from torch_geometric.nn import Linear as LazyLinear
 from torch_geometric.nn import MessagePassing, RGCNConv, SAGEConv, to_hetero
 
@@ -212,6 +212,34 @@ def test_to_hetero():
     assert out['author'].size() == (8, 16)
 
 
+class GCN(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = GCNConv(16, 32)
+        self.conv2 = GCNConv(32, 64)
+
+    def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
+        x = self.conv1(x, edge_index).relu()
+        x = self.conv2(x, edge_index).relu()
+        return x
+
+
+def test_to_hetero_with_gcn():
+    metadata = (['paper'], [('paper', '0', 'paper'), ('paper', '1', 'paper')])
+    x_dict = {'paper': torch.randn(100, 16)}
+    edge_index_dict = {
+        ('paper', '0', 'paper'): torch.randint(100, (2, 200)),
+        ('paper', '1', 'paper'): torch.randint(100, (2, 200)),
+    }
+
+    model = GCN()
+    model = to_hetero(model, metadata, debug=False)
+    print(model)
+    out = model(x_dict, edge_index_dict)
+    assert isinstance(out, dict) and len(out) == 1
+    assert out['paper'].size() == (100, 64)
+
+
 class GraphConv(MessagePassing):
     def __init__(self, in_channels, out_channels):
         super().__init__(aggr='mean')
@@ -221,6 +249,8 @@ def reset_parameters(self):
         self.lin.reset_parameters()
 
     def forward(self, x, edge_index):
+        if isinstance(x, Tensor):
+            x = (x, x)
         return self.propagate(edge_index, x=(self.lin(x[0]), x[1]))
 
 
diff --git a/torch_geometric/nn/to_hetero_transformer.py b/torch_geometric/nn/to_hetero_transformer.py
index 742f30cb40f3..cedd26f4d8e2 100644
--- a/torch_geometric/nn/to_hetero_transformer.py
+++ b/torch_geometric/nn/to_hetero_transformer.py
@@ -305,12 +305,18 @@ def map_args_kwargs(self, node: Node,
         def _recurse(value: Any) -> Any:
             if isinstance(value, Node):
                 out = self.find_by_name(f'{value.name}__{key2str(key)}')
-                if out is None and isinstance(key, tuple):
-                    out = (
+                if out is not None:
+                    return out
+                elif isinstance(key, tuple) and key[0] == key[-1]:
+                    name = f'{value.name}__{key2str(key[0])}'
+                    return self.find_by_name(name)
+                elif isinstance(key, tuple) and key[0] != key[-1]:
+                    return (
                         self.find_by_name(f'{value.name}__{key2str(key[0])}'),
                         self.find_by_name(f'{value.name}__{key2str(key[-1])}'),
                     )
-                return out
+                else:
+                    raise NotImplementedError
             elif isinstance(value, dict):
                 return {k: _recurse(v) for k, v in value.items()}
             elif isinstance(value, list):