diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 5f9da3faa18e..28fe36e6e550 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -962,6 +962,32 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             /*memory_format=*/noneVal);
         return success();
       });
+  patterns.onOp("ReduceLogSum", 1,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value data;
+                  int64_t keepDims, noop_with_empty_axes;
+                  if (binder.tensorOperandAtIndex(data, 0) ||
+                      binder.tensorResultType(resultType) ||
+                      binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
+                      binder.s64IntegerAttr(noop_with_empty_axes,
+                                            "noop_with_empty_axes", 0))
+                    return failure();
+
+                  auto reducedSumBool =
+                      reducedSumImpl(binder, rewriter, data, resultType,
+                                     /*storeValue=*/data, keepDims,
+                                     noop_with_empty_axes, true);
+
+                  if (failed(reducedSumBool))
+                    return rewriter.notifyMatchFailure(
+                        binder.op,
+                        "Failed to perform sum operation on square of operand");
+
+                  rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(
+                      binder.op, resultType, data);
+                  return success();
+                });
   patterns.onOp("ReduceSum", 1,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
@@ -978,7 +1004,7 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                                         /*storeValue=*/data, keepDims,
                                         noop_with_empty_axes, false);
                 });
-  patterns.onOp("ReduceLogSum", 1,
+  patterns.onOp("ReduceSumSquare", 1,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;
                   Value data;
@@ -990,19 +1016,13 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                                             "noop_with_empty_axes", 0))
                     return failure();
 
-                  auto reducedSumBool =
-                      reducedSumImpl(binder, rewriter, data, resultType,
-                                     /*storeValue=*/data, keepDims,
-                                     noop_with_empty_axes, true);
-
-                  if (failed(reducedSumBool))
-                    return rewriter.notifyMatchFailure(
-                        binder.op,
-                        "Failed to perform sum operation on square of operand");
+                  Value dataSquare = rewriter.create<Torch::AtenMulTensorOp>(
+                      binder.getLoc(), data.getType(), data, data);
 
-                  rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(
-                      binder.op, resultType, data);
-                  return success();
+                  return reducedSumImpl(binder, rewriter, dataSquare,
+                                        resultType,
+                                        /*storeValue=*/data, keepDims,
+                                        noop_with_empty_axes, false);
                 });
   patterns.onOp(
       "ReduceMean", 1,
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index 2748a640a6b3..cb4314ea0a07 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -868,6 +868,57 @@ func.func @test_reduce_l2_keep_dims_int_input_example(%arg0: !torch.vtensor<[3,2
 
 // -----
 
+// CHECK-LABEL: func.func @test_reduce_log_sum_default_axes_keepdims_example
+func.func @test_reduce_log_sum_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
+  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[1,1,1],f32> -> !torch.vtensor<[1,1,1],f32>
+  // CHECK: return %[[LOG]] : !torch.vtensor<[1,1,1],f32>
+  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32>
+  return %0 : !torch.vtensor<[1,1,1],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_reduce_log_sum_keep_dims_example
+func.func @test_reduce_log_sum_keep_dims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2,1],f32>
+  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2,1],f32> -> !torch.vtensor<[3,2,1],f32>
+  // CHECK: return %[[LOG]] : !torch.vtensor<[3,2,1],f32>
+  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],f32>
+  return %0 : !torch.vtensor<[3,2,1],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_reduce_log_sum_do_not_keepdims_example
+func.func @test_reduce_log_sum_do_not_keepdims_example(%arg0:!torch.vtensor<[3,2,2],f32>, %arg1:!torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
+  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2],f32> -> !torch.vtensor<[3,2],f32>
+  // CHECK: return %[[LOG]] : !torch.vtensor<[3,2],f32>
+  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32>
+  return %0 : !torch.vtensor<[3,2],f32>
+}
+
+// -----
+
 // CHECK-LABEL: func.func @test_reduce_sum_default_axes_keepdims_example
 func.func @test_reduce_sum_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
@@ -950,53 +1001,86 @@ func.func @test_reduce_sum_negative_axes_keepdims_example(%arg0: !torch.vtensor<
 
 // -----
 
-// CHECK-LABEL: func.func @test_reduce_log_sum_default_axes_keepdims_example
-func.func @test_reduce_log_sum_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+// CHECK-LABEL: func.func @test_reduce_sum_square_default_axes_keepdims_example
+func.func @test_reduce_sum_square_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
   // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE:.+]] = torch.constant.none
-  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
-  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[1,1,1],f32> -> !torch.vtensor<[1,1,1],f32>
-  // CHECK: return %[[LOG]] : !torch.vtensor<[1,1,1],f32>
-  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32>
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
+  // CHECK: return %[[SUM]] : !torch.vtensor<[1,1,1],f32>
+  %0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32>
   return %0 : !torch.vtensor<[1,1,1],f32>
 }
 
 // -----
 
-// CHECK-LABEL: func.func @test_reduce_log_sum_keep_dims_example
-func.func @test_reduce_log_sum_keep_dims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+// CHECK-LABEL: func.func @test_reduce_sum_square_do_not_keepdims_example
+func.func @test_reduce_sum_square_do_not_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
   // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
   // CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
   // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
   // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
+  // CHECK: return %[[SUM]] : !torch.vtensor<[3,2],f32>
+  %0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32>
+  return %0 : !torch.vtensor<[3,2],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_reduce_sum_square_empty_set_non_reduced_axis_zero
+func.func @test_reduce_sum_square_empty_set_non_reduced_axis_zero(%arg0: !torch.vtensor<[2,0,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,0,1],f32> attributes {torch.onnx_meta.ir_version = 8: si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[2,0,4],f32>, !torch.vtensor<[2,0,4],f32> -> !torch.vtensor<[2,0,4],f32>
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT2]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE:.+]] = torch.constant.none
-  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2,1],f32>
-  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2,1],f32> -> !torch.vtensor<[3,2,1],f32>
-  // CHECK: return %[[LOG]] : !torch.vtensor<[3,2,1],f32>
-  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],f32>
-  return %0 : !torch.vtensor<[3,2,1],f32>
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[2,0,4],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[2,0,1],f32>
+  // CHECK: return %[[SUM]] : !torch.vtensor<[2,0,1],f32>
+  %0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[2,0,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,0,1],f32>
+  return %0 : !torch.vtensor<[2,0,1],f32>
 }
 
 // -----
 
-// CHECK-LABEL: func.func @test_reduce_log_sum_do_not_keepdims_example
-func.func @test_reduce_log_sum_do_not_keepdims_example(%arg0:!torch.vtensor<[3,2,2],f32>, %arg1:!torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+// CHECK-LABEL: func.func @test_reduce_sum_square_keepdims_example
+func.func @test_reduce_sum_square_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
   // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
   // CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
   // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
   // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
-  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE:.+]] = torch.constant.none
-  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %arg0, %[[DIMS]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
-  // CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2],f32> -> !torch.vtensor<[3,2],f32>
-  // CHECK: return %[[LOG]] : !torch.vtensor<[3,2],f32>
-  %0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32>
-  return %0 : !torch.vtensor<[3,2],f32>
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  // CHECK: return %[[SUM]] : !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32>
+  return %0 : !torch.vtensor<[3,1,2],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_reduce_sum_square_keepdims_int_example
+func.func @test_reduce_sum_square_keepdims_int_example(%arg0: !torch.vtensor<[3,2,2],si64>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],si64>, !torch.vtensor<[3,2,2],si64> -> !torch.vtensor<[3,2,2],si64>
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],si64>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  // CHECK: return %[[SUM]] : !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32>
+  return %0 : !torch.vtensor<[3,1,2],f32>
 }
 
 // -----