diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 6d2fb8153..3637f7f35 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -459,6 +459,316 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             binder.op, resultType, operand, vAlpha, vScale, vInputScale);
         return success();
       });
+  patterns.onOp(
+      "ReduceSum", 13,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        Value data;
+        Value axes;
+        int64_t keepDims;
+        int64_t noop_with_empty_axes;
+        if (binder.tensorOperands(data, axes) ||
+            binder.tensorResultType(resultType) ||
+            binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
+            binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
+                                  0))
+          return failure();
+        Torch::BaseTensorType axesType =
+            axes.getType().cast<Torch::BaseTensorType>();
+        SmallVector<Value> dimList;
+        SmallVector<int64_t> selectSizes;
+        selectSizes.push_back(1);
+        Type selectResultType = axesType.getWithSizesAndDtype(
+            llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
+        auto sizes =
+            dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
+        Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+        // Deal with case when axes is empty
+        if (sizes.size() == 1 && sizes[0] == 0) {
+          if (noop_with_empty_axes == 0) {
+            Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
+            Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
+                binder.getLoc(), keepDimsConstInt);
+            rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
+                binder.op, resultType, data, /*dim=*/noneVal,
+                /*keepdim=*/keepDimsBool, /*dtype=*/noneVal);
+          } else {
+            rewriter.replaceOp(binder.op, data);
+          }
+          return success();
+        }
+        Value zero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
+        int64_t adjustmentInt =
+            cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
+        Value adjustment = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64),
+                                    adjustmentInt));
+        // convert axes (tensor) into torch int list while dealing with neg axis
+        for (int i = 0; i < sizes[0]; i++) {
+          // Go through the axes list and get each dim in the list
+          Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+          Value extract = rewriter.create<Torch::AtenSelectIntOp>(
+              binder.getLoc(), selectResultType, axes, zero, selectIndex);
+          Value dim = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
+          // deal with neg axis: if (axis < 0) axis += rank
+          Value isNegative =
+              rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
+          isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
+                                                             isNegative);
+          Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
+              binder.getLoc(), isNegative, adjustment);
+          Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
+              binder.getLoc(), dim, finalOffset);
+          dimList.push_back(finalDim);
+        }
+        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            dimList);
+        Value keepDimBool;
+        if (keepDims == 1) {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
+        } else {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
+        }
+        rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
+            binder.op, resultType, data, dimValueList, keepDimBool,
+            /*dtype=*/noneVal);
+        return success();
+      });
+  patterns.onOp(
+      "ReduceMean", 13,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        Value data;
+        Value axes;
+        int64_t keepDims;
+        int64_t noop_with_empty_axes;
+        if (binder.tensorOperands(data, axes) ||
+            binder.tensorResultType(resultType) ||
+            binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
+            binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
+                                  0))
+          return failure();
+        Torch::BaseTensorType axesType =
+            axes.getType().cast<Torch::BaseTensorType>();
+        SmallVector<Value> dimList;
+        SmallVector<int64_t> selectSizes;
+        selectSizes.push_back(1);
+        Type selectResultType = axesType.getWithSizesAndDtype(
+            llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
+        auto sizes =
+            dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
+        Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+        // deal with case when axes is empty
+        if (sizes.size() == 1 && sizes[0] == 0) {
+          if (noop_with_empty_axes == 0) {
+            Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
+            Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
+                binder.getLoc(), keepDimsConstInt);
+            rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
+                binder.op, resultType, data, /*dim=*/noneVal, keepDimsBool,
+                /*dtype=*/noneVal);
+          } else {
+            rewriter.replaceOp(binder.op, data);
+          }
+          return success();
+        }
+        Value zero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
+        int64_t adjustmentInt =
+            cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
+        Value adjustment = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64),
+                                    adjustmentInt));
+        // convert axes (tensor) into torch int list while dealing with neg axis
+        for (int i = 0; i < sizes[0]; i++) {
+          // Go through the axes list and get each dim in the list
+          Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+          Value extract = rewriter.create<Torch::AtenSelectIntOp>(
+              binder.getLoc(), selectResultType, axes, zero, selectIndex);
+          Value dim = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
+          // deal with neg axis: if (axis < 0) axis += rank
+          Value isNegative =
+              rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
+          isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
+                                                             isNegative);
+          Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
+              binder.getLoc(), isNegative, adjustment);
+          Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
+              binder.getLoc(), dim, finalOffset);
+          dimList.push_back(finalDim);
+        }
+        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            dimList);
+        Value keepDimBool;
+        if (keepDims == 1) {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
+        } else {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
+        }
+        rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
+            binder.op, resultType, data, dimValueList, keepDimBool,
+            /*dtype=*/noneVal);
+        return success();
+      });
+  patterns.onOp(
+      "ReduceMin", 13,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        // AtenAminOp allows us to pass a list of dims
+        Torch::ValueTensorType resultType;
+        Value data;
+        Value axes;
+        int64_t keepDims;
+        int64_t noop_with_empty_axes;
+        // Deal with case when no axes arg is passed
+        if (binder.op->getNumOperands() == 1) {
+          if (binder.tensorOperand(data) ||
+              binder.tensorResultType(resultType) ||
+              binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
+              binder.s64IntegerAttr(noop_with_empty_axes,
+                                    "noop_with_empty_axes", 0))
+            return failure();
+          if (noop_with_empty_axes == 0) {
+            Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
+            Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
+                binder.getLoc(), keepDimsConstInt);
+            int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
+                                  .getSizes()
+                                  .size();
+            SmallVector<Value> axesList;
+            for (int i = 0; i < numDims; i++) {
+              Value curr = rewriter.create<Torch::ConstantIntOp>(
+                  binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                  rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+              axesList.push_back(curr);
+            }
+            Value axesValueList = rewriter.create<Torch::PrimListConstructOp>(
+                binder.getLoc(),
+                Torch::ListType::get(
+                    Torch::IntType::get(binder.op->getContext())),
+                axesList);
+            rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
+                binder.op, resultType, data, axesValueList, keepDimsBool);
+          } else {
+            rewriter.replaceOp(binder.op, data);
+          }
+          return success();
+        }
+        if (binder.tensorOperands(data, axes) ||
+            binder.tensorResultType(resultType) ||
+            binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
+            binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
+                                  0))
+          return failure();
+        Torch::BaseTensorType axesType =
+            axes.getType().cast<Torch::BaseTensorType>();
+        SmallVector<Value> dimList;
+        SmallVector<int64_t> selectSizes;
+        selectSizes.push_back(1);
+        Type selectResultType = axesType.getWithSizesAndDtype(
+            llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
+        auto sizes =
+            dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
+        // deal with case when axes is empty
+        if (sizes.size() == 1 && sizes[0] == 0) {
+          if (noop_with_empty_axes == 0) {
+            // create dims list with all dims [0, data.getSizes().size())
+            Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
+            Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
+                binder.getLoc(), keepDimsConstInt);
+            int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
+                                  .getSizes()
+                                  .size();
+            for (int i = 0; i < numDims; i++) {
+              Value curr = rewriter.create<Torch::ConstantIntOp>(
+                  binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                  rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+              dimList.push_back(curr);
+            }
+            Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
+                binder.getLoc(),
+                Torch::ListType::get(
+                    Torch::IntType::get(binder.op->getContext())),
+                dimList);
+            rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
+                binder.op, resultType, data, dimValueList, keepDimsBool);
+          } else {
+            rewriter.replaceOp(binder.op, data);
+          }
+          return success();
+        }
+        Value zero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
+        int64_t adjustmentInt =
+            cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
+        Value adjustment = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getIntegerAttr(rewriter.getIntegerType(64),
+                                    adjustmentInt));
+        // convert axes (tensor) into torch int list while dealing with neg axis
+        for (int i = 0; i < sizes[0]; i++) {
+          // Go through the axes list and get each dim in the list
+          Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
+          Value extract = rewriter.create<Torch::AtenSelectIntOp>(
+              binder.getLoc(), selectResultType, axes, zero, selectIndex);
+          Value dim = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
+          // deal with neg axis: if (axis < 0) axis += rank
+          Value isNegative =
+              rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
+          isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
+                                                             isNegative);
+          Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
+              binder.getLoc(), isNegative, adjustment);
+          Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
+              binder.getLoc(), dim, finalOffset);
+          dimList.push_back(finalDim);
+        }
+        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
+            dimList);
+        Value keepDimBool;
+        if (keepDims == 1) {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
+        } else {
+          keepDimBool =
+              rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
+        }
+        rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
+            binder.op, resultType, data, dimValueList, keepDimBool);
+        return success();
+      });
 
   patterns.onOp("Shape", 9,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
@@ -550,7 +860,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         }
 
         rewriter.replaceOp(binder.op, operand);
-
         return success();
       });
 }
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index e4c95fe2b..da2a5c44a 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -11,6 +11,8 @@ func.func @test_reciprocal(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_relu
 func.func @test_relu(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 14 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.relu %arg0 : !torch.vtensor<[3,4,5],f32> -> !torch.vtensor<[3,4,5],f32>
@@ -18,6 +20,8 @@ func.func @test_relu(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_round
 func.func @test_round(%arg0: !torch.vtensor<[15],f32>) -> !torch.vtensor<[15],f32> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   //CHECK: torch.aten.round %arg0 : !torch.vtensor<[15],f32> -> !torch.vtensor<[15],f32>
@@ -25,6 +29,8 @@ func.func @test_round(%arg0: !torch.vtensor<[15],f32>) -> !torch.vtensor<[15],f3
   return %0 : !torch.vtensor<[15],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_scatter_elements_with_axis
 func.func @test_scatter_elements_with_axis(%arg0: !torch.vtensor<[1,5],f32>, %arg1: !torch.vtensor<[1,2],si64>, %arg2: !torch.vtensor<[1,2],f32>) -> !torch.vtensor<[1,5],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: %[[INT1:.*]] = torch.constant.int 1
@@ -59,6 +65,8 @@ func.func @test_scatter_elements_with_reduction_mul(%arg0: !torch.vtensor<[1,5],
   return %0 : !torch.vtensor<[1,5],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_sigmoid_example
 func.func @test_sigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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: torch.aten.sigmoid %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
@@ -66,6 +74,8 @@ func.func @test_sigmoid_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtenso
   return %0 : !torch.vtensor<[3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_sin_example
 func.func @test_sin_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 7 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.sin %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
@@ -73,6 +83,8 @@ func.func @test_sin_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3
   return %0 : !torch.vtensor<[3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_tanh_example
 func.func @test_tanh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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: torch.aten.tanh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
@@ -80,6 +92,8 @@ func.func @test_tanh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[
   return %0 : !torch.vtensor<[3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_sqrt_example
 func.func @test_sqrt_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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: torch.aten.sqrt %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>
@@ -87,6 +101,8 @@ func.func @test_sqrt_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[
   return %0 : !torch.vtensor<[3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_sub_bcast
 func.func @test_sub_bcast(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 14 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: %[[INT1:.*]] = torch.constant.int 1
@@ -119,6 +135,8 @@ func.func @test_sub_uint8(%arg0: !torch.vtensor<[3,4,5],ui8>, %arg1: !torch.vten
   return %0 : !torch.vtensor<[3,4,5],ui8>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_sum_example
 func.func @test_sum_example(%arg0: !torch.vtensor<[3],f32>, %arg1: !torch.vtensor<[3],f32>, %arg2: !torch.vtensor<[3],f32>, %arg3: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],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: %[[INT1:.*]] = torch.constant.int 1
@@ -143,6 +161,8 @@ func.func @test_sum_two_inputs(%arg0: !torch.vtensor<[3],f32>, %arg1: !torch.vte
   return %0 : !torch.vtensor<[3],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_where_example
 func.func @test_where_example(%arg0: !torch.vtensor<[2,2],i1>, %arg1: !torch.vtensor<[2,2],f32>, %arg2: !torch.vtensor<[2,2],f32>) -> !torch.vtensor<[2,2],f32> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 16 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.where.self %arg0, %arg1, %arg2 : !torch.vtensor<[2,2],i1>, !torch.vtensor<[2,2],f32>, !torch.vtensor<[2,2],f32> -> !torch.vtensor<[2,2],f32>
@@ -157,6 +177,8 @@ func.func @test_where_long_example(%arg0: !torch.vtensor<[2,2],i1>, %arg1: !torc
   return %0 : !torch.vtensor<[2,2],si64>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_xor2d
 func.func @test_xor2d(%arg0: !torch.vtensor<[3,4],i1>, %arg1: !torch.vtensor<[3,4],i1>) -> !torch.vtensor<[3,4],i1> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 7 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
   // CHECK: torch.aten.logical_xor %arg0, %arg1 : !torch.vtensor<[3,4],i1>, !torch.vtensor<[3,4],i1> -> !torch.vtensor<[3,4],i1>
@@ -192,6 +214,8 @@ func.func @test_xor_bcast4v4d(%arg0: !torch.vtensor<[1,4,1,6],i1>, %arg1: !torch
   return %0 : !torch.vtensor<[3,4,5,6],i1>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_squeeze
 func.func @test_squeeze(%arg0: !torch.vtensor<[1,3,4,5],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,4,5],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
@@ -233,6 +257,8 @@ func.func @test_squeeze_two_axes(%arg0: !torch.vtensor<[3,1,4,5,1],f32>, %arg1:
   return %0 : !torch.vtensor<[3,4,5],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_unsqueeze_axis_0
 func.func @test_unsqueeze_axis_0(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,3,4,5],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
@@ -421,6 +447,8 @@ func.func @test_unsqueeze_unsorted_axes(%arg0: !torch.vtensor<[3,4,5],f32>, %arg
   return %0 : !torch.vtensor<[3,4,1,5,1,1],f32>
 }
 
+// -----
+
 // CHECK-LABEL: func.func @test_softmax_axis_0
 func.func @test_softmax_axis_0(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],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
@@ -489,6 +517,275 @@ func.func @test_selu(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,
 
 // -----
 
+// 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: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: torch.aten.Bool.int %int1 : !torch.int -> !torch.bool
+  // CHECK: torch.aten.sum.dim_IntList %arg0, %none, %0, %none : !torch.vtensor<[3,2,2],f32>, !torch.none, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
+  %0 = torch.operator "onnx.ReduceSum"(%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_sum_do_not_keepdims_example
+func.func @test_reduce_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 = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: torch.aten.sum.dim_IntList %arg0, %6, %false, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
+  %0 = torch.operator "onnx.ReduceSum"(%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_empty_axes_input_noop_example
+func.func @test_reduce_sum_empty_axes_input_noop_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[3,2,2],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: %[[NONE:.+]] = torch.constant.none
+  %0 = torch.operator "onnx.ReduceSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64, torch.onnx.noop_with_empty_axes = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[3,2,2],f32>
+  return %0 : !torch.vtensor<[3,2,2],f32>
+}
+
+// CHECK-LABEL: func.func @test_reduce_sum_empty_set_non_reduced_axis_zero
+func.func @test_reduce_sum_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 = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.sum.dim_IntList %arg0, %6, %true, %none : !torch.vtensor<[2,0,4],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[2,0,1],f32>
+  %0 = torch.operator "onnx.ReduceSum"(%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_sum_keepdims_example
+func.func @test_reduce_sum_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 = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.sum.dim_IntList %arg0, %6, %true, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceSum"(%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_negative_axes_keepdims_example
+func.func @test_reduce_sum_negative_axes_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 = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.sum.dim_IntList %arg0, %6, %true, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceSum"(%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_mean_default_axes_keepdims_example
+func.func @test_reduce_mean_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: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: torch.aten.Bool.int %int1 : !torch.int -> !torch.bool
+  // CHECK: torch.aten.mean.dim %arg0, %none, %0, %none : !torch.vtensor<[3,2,2],f32>, !torch.none, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
+  %0 = torch.operator "onnx.ReduceMean"(%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_mean_do_not_keepdims_example
+func.func @test_reduce_mean_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: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: torch.aten.mean.dim %arg0, %6, %false, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
+  %0 = torch.operator "onnx.ReduceMean"(%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_mean_keepdims_example
+func.func @test_reduce_mean_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: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.mean.dim %arg0, %6, %true, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceMean"(%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_mean_negative_axes_keepdims_example
+func.func @test_reduce_mean_negative_axes_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: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.mean.dim %arg0, %6, %true, %none : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceMean"(%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_min_bool_inputs
+func.func @test_reduce_min_bool_inputs(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int2 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4,1],i1>
+  %0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[4,2],i1>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1>
+  return %0 : !torch.vtensor<[4,1],i1>
+}
+
+// CHECK-LABEL: func.func @test_reduce_min_default_axes_keepdims_example
+func.func @test_reduce_min_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>) -> !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: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: torch.aten.Bool.int %int1 : !torch.int -> !torch.bool
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1_0:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: torch.prim.ListConstruct %int0, %int1_0, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: torch.aten.amin %arg0, %1, %0 : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[1,1,1],f32>
+  %0 = torch.operator "onnx.ReduceMin"(%arg0) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>) -> !torch.vtensor<[1,1,1],f32>
+  return %0 : !torch.vtensor<[1,1,1],f32>
+}
+
+// CHECK-LABEL: func.func @test_reduce_min_do_not_keepdims_example
+func.func @test_reduce_min_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: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: torch.aten.amin %arg0, %6, %false : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,2],f32>
+  %0 = torch.operator "onnx.ReduceMin"(%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_min_empty_set
+func.func @test_reduce_min_empty_set(%arg0: !torch.vtensor<[2,0,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[2,0,4],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[2,1,4],f32>
+  %0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[2,0,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],f32>
+  return %0 : !torch.vtensor<[2,1,4],f32>
+}
+
+// CHECK-LABEL: func.func @test_reduce_min_keepdims_example
+func.func @test_reduce_min_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: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceMin"(%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_min_negative_axes_keepdims_example
+func.func @test_reduce_min_negative_axes_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: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT3:.+]] = torch.constant.int 3
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
+  // CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
+  // CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
+  // CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
+  // CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[TRUE:.+]] = torch.constant.bool true
+  // CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,1,2],f32>
+  %0 = torch.operator "onnx.ReduceMin"(%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_sinh
 func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[3],f32> attributes {torch.onnx_meta.ir_version = 4 : si64, torch.onnx_meta.opset_version = 9 : si64} {
   // CHECK: torch.aten.sinh %arg0 : !torch.vtensor<[3],f32> -> !torch.vtensor<[3],f32>