diff --git a/coremltools/converters/mil/frontend/torch/test/test_passes.py b/coremltools/converters/mil/frontend/torch/test/test_passes.py
index 4401ebbfb..f2c83fe9e 100644
--- a/coremltools/converters/mil/frontend/torch/test/test_passes.py
+++ b/coremltools/converters/mil/frontend/torch/test/test_passes.py
@@ -405,3 +405,25 @@ def forward(self, x):
         y_cm = ct_model.predict({'x': x})['y']
 
         assert((y_cm == np.zeros(shape)).all())
+
+    @staticmethod
+    def test_inpace_op_from_add():
+        class Net(torch.nn.Module):
+            def forward(self, x):
+                y = torch.empty(x.shape) + 1
+                y.fill_(0)
+                return y
+
+        shape = (2, 3)
+        x = torch.rand(*shape)
+        traced_fn = torch.jit.trace(Net(), x).eval()
+
+        ct_model = ct.convert(
+            traced_fn,
+            inputs=[ct.TensorType(shape=shape)],
+            outputs=[ct.TensorType(name="y", dtype=np.int32)],
+            source="pytorch",
+        )
+        y_cm = ct_model.predict({'x': x})['y']
+
+        assert((y_cm == np.zeros(shape)).all())
diff --git a/coremltools/converters/mil/frontend/torch/torchir_passes.py b/coremltools/converters/mil/frontend/torch/torchir_passes.py
index d066d9a9d..100fe66a1 100644
--- a/coremltools/converters/mil/frontend/torch/torchir_passes.py
+++ b/coremltools/converters/mil/frontend/torch/torchir_passes.py
@@ -135,17 +135,7 @@ def _construct_nodes_to_fuse_inputs(nodes_to_fuse):
                 tensor_to_node_sequence_mapping.pop(node_input)
             node_sequence.append(node)
             tensor_to_node_sequence_mapping[node_output] = node_sequence
-
-        if node.kind == "to":
-            node_input = node.inputs[0]
-            if node_input in tensor_to_node_sequence_mapping:
-                # update the mapping
-                node_output = node.outputs[0]
-                val = tensor_to_node_sequence_mapping[node_input]
-                del tensor_to_node_sequence_mapping[node_input]
-                tensor_to_node_sequence_mapping[node_output] = val
-
-        if node.kind in ("copy_", "fill_"):
+        elif node.kind in ("copy_", "fill_"):
             node_input = node.inputs[0]
             if node_input not in tensor_to_node_sequence_mapping:
                 raise ValueError("No matching select or slice.")
@@ -176,6 +166,13 @@ def _construct_nodes_to_fuse_inputs(nodes_to_fuse):
                 blocks=[],
             )
             graph.nodes[i] = tensor_assign_node
+        elif node.inputs:
+            node_input = node.inputs[0]
+            if node_input in tensor_to_node_sequence_mapping:
+                # update the mapping
+                node_output = node.outputs[0]
+                val = tensor_to_node_sequence_mapping[node_input]
+                tensor_to_node_sequence_mapping[node_output] = val
 
     # modify the graph outputs if it is effected by this graph pass
     for idx in range(len(graph.outputs)):