diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 1793af9590ef..bee7b4aa2f2b 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -36,21 +36,24 @@ namespace {
 // we provide the original operand through storeResult, which will be modified
 // if the result will be passed onto another operation, and will be used for
 // noop_with_empty_axes handling before that.
-LogicalResult reducedSumImpl(OpBinder binder,
-                             ConversionPatternRewriter &rewriter, Value data,
-                             Torch::ValueTensorType resultType,
-                             Value &storeResult, int64_t keepDims,
-                             int64_t noop_with_empty_axes,
-                             bool isIntermediateOp) {
-
+template <typename AtenReductionTypeOp>
+LogicalResult reduceOpImpl(OpBinder binder, ConversionPatternRewriter &rewriter,
+                           Value data, Torch::ValueTensorType resultType,
+                           Value &storeResult, int64_t keepDims,
+                           int64_t noop_with_empty_axes,
+                           bool isIntermediateOp) {
+
+  auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
+  if (!inputType)
+    return failure();
   SmallVector<Value> axesList;
   Value axesVal;
   if (!binder.tensorOperandAtIndex(axesVal, 1)) {
-    auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
-    if (!inputType.hasSizes() || !resultType.hasSizes()) {
-      return rewriter.notifyMatchFailure(
-          binder.op, "unimplemented: expected input and result to have shapes");
-    }
+    auto axesTy = dyn_cast<Torch::ValueTensorType>(axesVal.getType());
+    if (!axesTy || !axesTy.areAllSizesKnown() || axesTy.getSizes().size() > 1)
+      return failure();
+    auto axesShape = axesTy.getSizes();
+    uint64_t numAxes = (axesShape.empty()) ? 1 : axesShape.front();
 
     if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
       SmallVector<int64_t> inputShape{inputType.getSizes()};
@@ -77,22 +80,25 @@ LogicalResult reducedSumImpl(OpBinder binder,
           } else {
             reduceDims.push_back(i);
             if (resultShapeCounter < resultShape.size() &&
-                resultShape[resultShapeCounter] == 1)
+                resultShape[resultShapeCounter] == 1 && keepDims == 1)
               resultShapeCounter++;
           }
         }
-        for (auto i : reduceDims) {
-          axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
-              binder.getLoc(), rewriter.getI64IntegerAttr(i)));
-        }
+        if (reduceDims.size() == numAxes) {
+          for (auto i : reduceDims) {
+            axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
+                binder.getLoc(), rewriter.getI64IntegerAttr(i)));
+          }
+        } else
+          binder.op->emitWarning(
+              "Number of inferred reduce dims, " +
+              std::to_string(reduceDims.size()) +
+              ", does not match the provided number of axes, " +
+              std::to_string(numAxes) + ".");
       }
     }
     if (axesList.empty()) {
-      Torch::BaseTensorType axesType =
-          cast<Torch::BaseTensorType>(axesVal.getType());
-      auto axesTy = dyn_cast<Torch::ValueTensorType>(axesVal.getType());
-      auto axesShape = axesTy.getSizes();
-      if (axesShape.size() != 1 || axesShape[0] == Torch::kUnknownSize)
+      if (axesTy.getSizes()[0] == Torch::kUnknownSize)
         return failure();
 
       Value zero = rewriter.create<Torch::ConstantIntOp>(
@@ -100,9 +106,8 @@ LogicalResult reducedSumImpl(OpBinder binder,
           rewriter.getI64IntegerAttr(0));
       SmallVector<int64_t> selectSizes{1};
       auto selType = rewriter.getType<Torch::ValueTensorType>(
-          selectSizes, axesType.getOptionalDtype());
-      int64_t numAxes = axesShape[0];
-      for (int64_t i = 0; i < numAxes; ++i) {
+          selectSizes, axesTy.getOptionalDtype());
+      for (uint64_t i = 0; i < numAxes; ++i) {
         Value iv = rewriter.create<Torch::ConstantIntOp>(
             binder.getLoc(), rewriter.getType<Torch::IntType>(),
             rewriter.getI64IntegerAttr(i));
@@ -117,38 +122,60 @@ LogicalResult reducedSumImpl(OpBinder binder,
 
   SmallVector<int64_t> axesInts;
   if (!binder.s64IntegerArrayAttr(axesInts, "axes", {})) {
-    for (int64_t i = 0, s = axesInts.size(); i < s; ++i) {
-      Value iv = rewriter.create<Torch::ConstantIntOp>(
-          binder.getLoc(), rewriter.getType<Torch::IntType>(),
-          rewriter.getI64IntegerAttr(axesInts[i]));
-      axesList.push_back(iv);
+    for (int64_t i : axesInts) {
+      axesList.push_back(
+          rewriter.create<Torch::ConstantIntOp>(binder.getLoc(), i));
     }
   }
 
   // Do not include absolute value in the noop
-  if (axesList.empty() && noop_with_empty_axes) {
-    rewriter.replaceOp(binder.op, storeResult);
+  if (axesList.empty() && noop_with_empty_axes == 1) {
+    if (!isIntermediateOp)
+      rewriter.replaceOp(binder.op, data);
+    else
+      storeResult = data;
     return success();
   }
 
+  // if the axes list is still empty, reduce everything.
+  if (axesList.empty()) {
+    if (keepDims == 0 && !resultType.getSizes().empty())
+      return rewriter.notifyMatchFailure(
+          binder.op,
+          "no axes provided & no keepdim: expected result to be rank zero.");
+    if (keepDims == 1 &&
+        (resultType.getSizes().size() != inputType.getSizes().size() ||
+         llvm::any_of(resultType.getSizes(),
+                      [](int64_t size) { return size != 1; })))
+      return rewriter.notifyMatchFailure(
+          binder.op,
+          "no axes provided & keepdim: expected result to have 1 element.");
+    for (uint64_t i = 0; i < inputType.getSizes().size(); i++) {
+      axesList.push_back(
+          rewriter.create<Torch::ConstantIntOp>(binder.getLoc(), i));
+    }
+  }
+
   Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
       binder.getLoc(),
       Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
       axesList);
   Value keepDimBool =
       rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
-  Value dType = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
   // If we are using the ReducedSum as an intermediate op to be passed into
   // another operation, we might not want to replace the Op. So we create a new
   // Op and store the result in a variable.
+  SmallVector<Value> operands = {data, dimValueList, keepDimBool};
+  if (llvm::is_one_of<AtenReductionTypeOp, Torch::AtenSumDimIntListOp,
+                      Torch::AtenMeanDimOp>())
+    operands.push_back(
+        /*dtype=*/rewriter.create<Torch::ConstantNoneOp>(binder.getLoc()));
   if (!isIntermediateOp) {
-    rewriter.replaceOpWithNewOp<Torch::AtenSumDimIntListOp>(
-        binder.op, resultType, data, dimValueList, keepDimBool,
-        /*dtype=*/dType);
+    rewriter.replaceOpWithNewOp<AtenReductionTypeOp>(binder.op, resultType,
+                                                     operands);
   } else {
-    storeResult = rewriter.create<Torch::AtenSumDimIntListOp>(
-        binder.getLoc(), resultType, data, dimValueList, keepDimBool,
-        /*dtype=*/dType);
+    storeResult = rewriter.create<AtenReductionTypeOp>(binder.getLoc(),
+                                                       resultType, operands);
   }
   return success();
 }
@@ -1054,9 +1081,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                   Value data = rewriter.create<Torch::AtenAbsOp>(
                       binder.getLoc(), operand.getType(), operand);
 
-                  return reducedSumImpl(binder, rewriter, data, resultType,
-                                        /*storeValue=*/operand, keepDims,
-                                        noop_with_empty_axes, false);
+                  return reduceOpImpl<Torch::AtenSumDimIntListOp>(
+                      binder, rewriter, data, resultType,
+                      /*storeValue=*/operand, keepDims, noop_with_empty_axes,
+                      false);
                 });
   patterns.onOp(
       "ReduceL2", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
@@ -1075,9 +1103,9 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         Value squareOfOperand = rewriter.create<Torch::AtenMulTensorOp>(
             binder.getLoc(), operand.getType(), operand, operand);
 
-        auto reducedSum =
-            reducedSumImpl(binder, rewriter, squareOfOperand, resultType,
-                           operand, keepDims, noop_with_empty_axes, true);
+        auto reducedSum = reduceOpImpl<Torch::AtenSumDimIntListOp>(
+            binder, rewriter, squareOfOperand, resultType, operand, keepDims,
+            noop_with_empty_axes, true);
         if (failed(reducedSum))
           return rewriter.notifyMatchFailure(
               binder.op,
@@ -1125,9 +1153,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                     return failure();
 
                   auto reducedSumBool =
-                      reducedSumImpl(binder, rewriter, data, resultType,
-                                     /*storeValue=*/data, keepDims,
-                                     noop_with_empty_axes, true);
+                      reduceOpImpl<Torch::AtenSumDimIntListOp>(
+                          binder, rewriter, data, resultType,
+                          /*storeValue=*/data, keepDims, noop_with_empty_axes,
+                          true);
 
                   if (failed(reducedSumBool))
                     return rewriter.notifyMatchFailure(
@@ -1169,7 +1198,7 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             binder.getLoc(), f64ResultType, dataCast);
         auto f64ReduceType = rewriter.getType<Torch::ValueTensorType>(
             resultType.getOptionalSizes(), rewriter.getF64Type());
-        auto reducedSumBool = reducedSumImpl(
+        auto reducedSumBool = reduceOpImpl<Torch::AtenSumDimIntListOp>(
             binder, rewriter, dataExp, f64ReduceType,
             /*storeValue=*/data, keepDims, noop_with_empty_axes, true);
         if (failed(reducedSumBool))
@@ -1198,9 +1227,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                                             "noop_with_empty_axes", 0))
                     return failure();
 
-                  return reducedSumImpl(binder, rewriter, data, resultType,
-                                        /*storeValue=*/data, keepDims,
-                                        noop_with_empty_axes, false);
+                  return reduceOpImpl<Torch::AtenSumDimIntListOp>(
+                      binder, rewriter, data, resultType,
+                      /*storeValue=*/data, keepDims, noop_with_empty_axes,
+                      false);
                 });
   patterns.onOp("ReduceSumSquare", 1,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
@@ -1217,10 +1247,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                   Value dataSquare = rewriter.create<Torch::AtenMulTensorOp>(
                       binder.getLoc(), data.getType(), data, data);
 
-                  return reducedSumImpl(binder, rewriter, dataSquare,
-                                        resultType,
-                                        /*storeValue=*/data, keepDims,
-                                        noop_with_empty_axes, false);
+                  return reduceOpImpl<Torch::AtenSumDimIntListOp>(
+                      binder, rewriter, dataSquare, resultType,
+                      /*storeValue=*/data, keepDims, noop_with_empty_axes,
+                      false);
                 });
   patterns.onOp(
       "ReduceMean", 1,
@@ -1235,111 +1265,10 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
                                   0))
           return failure();
 
-        SmallVector<Value> axesList;
-
-        Value axesVal;
-        if (!binder.tensorOperandAtIndex(axesVal, 1)) {
-          auto inputType = dyn_cast<Torch::ValueTensorType>(data.getType());
-          if (!inputType.hasSizes() || !resultType.hasSizes()) {
-            return rewriter.notifyMatchFailure(
-                binder.op,
-                "unimplemented: expected input and result to have shapes");
-          }
-
-          // If the input shape and result shape is statically known then the
-          // list of dims to be squeezed can be derived from those shapes. As a
-          // result, we don't have to wait for the dim values to be known at
-          // runtime which is also expected by the downstream pipeline.
-          if (inputType.areAllSizesKnown() && resultType.areAllSizesKnown()) {
-            SmallVector<int64_t> inputShape{inputType.getSizes()};
-            SmallVector<int64_t> resultShape{resultType.getSizes()};
-            if (llvm::equal(inputShape, resultShape)) {
-              // Case: none of the dimension is reduced.
-              rewriter.replaceOp(binder.op, data);
-              return success();
-            }
-            if (areAllElementsDistinct(inputShape)) {
-              // The check for the input shape elements to be distinct is added
-              // for the cases like:
-              // Input: [3, 2, 2] -> Output: [3, 2]
-              // For the above case, from the input and output shape it can't be
-              // inferred whether the dim:1 is reduced or dim:2. To avoid these
-              // type of cases, the check has been placed.
-              SmallVector<int64_t> reduceDims;
-              unsigned resultShapeCounter = 0;
-              for (unsigned i = 0; i < inputShape.size(); i++) {
-                if (resultShapeCounter < resultShape.size() &&
-                    inputShape[i] == resultShape[resultShapeCounter]) {
-                  resultShapeCounter++;
-                } else {
-                  reduceDims.push_back(i);
-                  if (resultShapeCounter < resultShape.size() &&
-                      resultShape[resultShapeCounter] == 1)
-                    resultShapeCounter++;
-                }
-              }
-              for (auto i : reduceDims) {
-                axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
-                    binder.getLoc(), rewriter.getI64IntegerAttr(i)));
-              }
-            }
-          }
-
-          if (axesList.empty()) {
-            Torch::BaseTensorType axesType =
-                cast<Torch::BaseTensorType>(axesVal.getType());
-            auto axesTy = dyn_cast<Torch::ValueTensorType>(axesVal.getType());
-            auto axesShape = axesTy.getSizes();
-            if (axesShape.size() != 1 || axesShape[0] == Torch::kUnknownSize)
-              return failure();
-
-            Value zero = rewriter.create<Torch::ConstantIntOp>(
-                binder.getLoc(), rewriter.getType<Torch::IntType>(),
-                rewriter.getI64IntegerAttr(0));
-            SmallVector<int64_t> selectSizes{1};
-            auto selType = rewriter.getType<Torch::ValueTensorType>(
-                selectSizes, axesType.getOptionalDtype());
-            int64_t numAxes = axesShape[0];
-            for (int64_t i = 0; i < numAxes; ++i) {
-              Value iv = rewriter.create<Torch::ConstantIntOp>(
-                  binder.getLoc(), rewriter.getType<Torch::IntType>(),
-                  rewriter.getI64IntegerAttr(i));
-              Value extract = rewriter.create<Torch::AtenSelectIntOp>(
-                  binder.getLoc(), selType, axesVal, zero, iv);
-              Value dim = rewriter.create<Torch::AtenItemOp>(
-                  binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
-              axesList.push_back(dim);
-            }
-          }
-        }
-
-        SmallVector<int64_t> axesInts;
-        if (!binder.s64IntegerArrayAttr(axesInts, "axes", {})) {
-          for (int64_t i = 0, s = axesInts.size(); i < s; ++i) {
-            Value iv = rewriter.create<Torch::ConstantIntOp>(
-                binder.getLoc(), rewriter.getType<Torch::IntType>(),
-                rewriter.getI64IntegerAttr(axesInts[i]));
-            axesList.push_back(iv);
-          }
-        }
-
-        // deal with case when axes is empty
-        if (axesList.empty() && noop_with_empty_axes) {
-          rewriter.replaceOp(binder.op, data);
-          return success();
-        }
-
-        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
-            binder.getLoc(),
-            Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
-            axesList);
-        Value keepDimBool =
-            rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
-        Value noneVal = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
-        rewriter.replaceOpWithNewOp<Torch::AtenMeanDimOp>(
-            binder.op, resultType, data, dimValueList, keepDimBool,
-            /*dtype=*/noneVal);
-        return success();
+        Value reduceSum = data;
+        return reduceOpImpl<Torch::AtenMeanDimOp>(
+            binder, rewriter, data, resultType,
+            /*storeValue=*/reduceSum, keepDims, noop_with_empty_axes, false);
       });
   patterns.onOp(
       "ReduceMax", 13,
@@ -1347,7 +1276,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         // AtenAmaxOp allows us to pass a list of dims
         Torch::ValueTensorType resultType;
         Value data;
-        Value axes;
         int64_t keepDims;
         int64_t noop_with_empty_axes;
         if (binder.tensorOperandAtIndex(data, 0) ||
@@ -1412,87 +1340,9 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
           return success();
         }
 
-        // Previous version of the operation had the axes as an attribute:
-        SmallVector<Value> axesList;
-        llvm::SmallVector<int64_t> axesAttr;
-        if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
-          for (int i = 0, s = axesAttr.size(); i < s; ++i) {
-            axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
-                binder.getLoc(), torchIntTy,
-                rewriter.getI64IntegerAttr(axesAttr[i])));
-          }
-        }
-
-        // Extract the axes values from the axes operand:
-        if (!binder.tensorOperandAtIndex(axes, 1)) {
-          Torch::BaseTensorType axesType =
-              cast<Torch::BaseTensorType>(axes.getType());
-          SmallVector<int64_t> selectSizes{1};
-          Type selectResultType = axesType.getWithSizesAndDtype(
-              selectSizes, axesType.getOptionalDtype());
-          auto sizes = axesType.getSizes();
-
-          Value zero = rewriter.create<Torch::ConstantIntOp>(
-              binder.getLoc(), rewriter.getType<Torch::IntType>(),
-              rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
-
-          // Extract the value of each axes:
-          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);
-            axesList.push_back(dim);
-          }
-        }
-
-        // Handle the noop case:
-        if (axesList.empty() && noop_with_empty_axes) {
-          rewriter.replaceOp(binder.op, data);
-          return success();
-        }
-
-        // Deal with case when no axes arg is passed but not a noop:
-        if (axesList.empty()) {
-          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));
-            axesList.push_back(curr);
-          }
-        }
-
-        // Handle negative axis:
-        Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
-                                                          torchIntTy, data);
-        Value zero = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getType<Torch::IntType>(),
-            rewriter.getI64IntegerAttr(0));
-        for (Value &axes : axesList) {
-          Value isNegative =
-              rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
-          isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
-                                                             isNegative);
-          Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
-              binder.getLoc(), isNegative, rankVal);
-          axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
-                                                      finalOffset);
-        }
-
-        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
-            binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
-        Value keepDimBool =
-            rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
-        rewriter.replaceOpWithNewOp<Torch::AtenAmaxOp>(
-            binder.op, resultType, data, dimValueList, keepDimBool);
-        return success();
+        return reduceOpImpl<Torch::AtenAmaxOp>(
+            binder, rewriter, data, resultType,
+            /*storeValue=*/data, keepDims, noop_with_empty_axes, false);
       });
 
   patterns.onOp(
@@ -1501,7 +1351,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         // 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;
         if (binder.tensorOperandAtIndex(data, 0) ||
@@ -1565,87 +1414,9 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
           return success();
         }
 
-        // Previous version of the operation had the axes as an attribute:
-        SmallVector<Value> axesList;
-        llvm::SmallVector<int64_t> axesAttr;
-        if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
-          for (int i = 0, s = axesAttr.size(); i < s; ++i) {
-            axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
-                binder.getLoc(), torchIntTy,
-                rewriter.getI64IntegerAttr(axesAttr[i])));
-          }
-        }
-
-        // Extract the axes values from the axes operand:
-        if (!binder.tensorOperandAtIndex(axes, 1)) {
-          Torch::BaseTensorType axesType =
-              cast<Torch::BaseTensorType>(axes.getType());
-          SmallVector<int64_t> selectSizes{1};
-          Type selectResultType = axesType.getWithSizesAndDtype(
-              selectSizes, axesType.getOptionalDtype());
-          auto sizes = axesType.getSizes();
-
-          Value zero = rewriter.create<Torch::ConstantIntOp>(
-              binder.getLoc(), rewriter.getType<Torch::IntType>(),
-              rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
-
-          // Extract the value of each axes:
-          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);
-            axesList.push_back(dim);
-          }
-        }
-
-        // Handle the noop case:
-        if (axesList.empty() && noop_with_empty_axes) {
-          rewriter.replaceOp(binder.op, data);
-          return success();
-        }
-
-        // Deal with case when no axes arg is passed but not a noop:
-        if (axesList.empty()) {
-          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));
-            axesList.push_back(curr);
-          }
-        }
-
-        // Handle negative axis:
-        Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
-                                                          torchIntTy, data);
-        Value zero = rewriter.create<Torch::ConstantIntOp>(
-            binder.getLoc(), rewriter.getType<Torch::IntType>(),
-            rewriter.getI64IntegerAttr(0));
-        for (Value &axes : axesList) {
-          Value isNegative =
-              rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
-          isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
-                                                             isNegative);
-          Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
-              binder.getLoc(), isNegative, rankVal);
-          axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
-                                                      finalOffset);
-        }
-
-        Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
-            binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
-        Value keepDimBool =
-            rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
-        rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
-            binder.op, resultType, data, dimValueList, keepDimBool);
-        return success();
+        return reduceOpImpl<Torch::AtenAminOp>(
+            binder, rewriter, data, resultType,
+            /*storeValue=*/data, keepDims, noop_with_empty_axes, false);
       });
 
   patterns.onOp(
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index 30fd60dbde3a..16c86218dbc8 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -707,17 +707,8 @@ func.func @test_reduce_max_empty_set_int(%arg0: !torch.vtensor<[2,0,4],si32>, %a
 
 // CHECK-LABEL: func.func @test_reduce_max_bool_inputs
 func.func @test_reduce_max_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: %[[IDX:.+]] = torch.constant.int 0
-  // CHECK: %[[SZ:.+]] = torch.constant.int 0
-  // CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
-  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[ADD]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[I1:.*]] = torch.constant.int 1
+  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[AMAX:.+]] = torch.aten.amax %arg0, %[[LST]], %[[TRUE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4,1],i1>
   // CHECK: return %[[AMAX]] : !torch.vtensor<[4,1],i1>
@@ -729,17 +720,8 @@ func.func @test_reduce_max_bool_inputs(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !
 
 // CHECK-LABEL: func.func @test_reduce_max_bool_inputs_nokeepdims
 func.func @test_reduce_max_bool_inputs_nokeepdims(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],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: %[[IDX:.+]] = torch.constant.int 0
-  // CHECK: %[[SZ:.+]] = torch.constant.int 0
-  // CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
-  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[ADD]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[I1:.*]] = torch.constant.int 1
+  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[AMAX:.+]] = torch.aten.amax %arg0, %[[LST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
   // CHECK: return %[[AMAX]] : !torch.vtensor<[4],i1>
@@ -751,19 +733,9 @@ func.func @test_reduce_max_bool_inputs_nokeepdims(%arg0: !torch.vtensor<[4,2],i1
 
 // CHECK-LABEL: func.func @test_reduce_max_all_dims_default
 func.func @test_reduce_max_all_dims_default(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[],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: %[[I0:.+]] = torch.constant.int 0
-  // CHECK: %[[I1:.+]] = torch.constant.int 1
-  // CHECK: %[[RANK:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I0]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[A0:.+]] = torch.aten.add.int %[[I0]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I1]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[A1:.+]] = torch.aten.add.int %[[I1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[A0]], %[[A1]]
+  // CHECK: %[[I0:.*]] = torch.constant.int 0
+  // CHECK: %[[I1:.*]] = torch.constant.int 1
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[I0]], %[[I1]]
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[MAX:.+]] = torch.aten.amax %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[],i1>
   // CHECK: return %[[MAX]] : !torch.vtensor<[],i1>
@@ -775,13 +747,7 @@ func.func @test_reduce_max_all_dims_default(%arg0: !torch.vtensor<[4,2],i1>) ->
 
 func.func @test_reduce_max_attr(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[4],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: %[[INT1:.+]] = torch.constant.int 1
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[INT0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[INT1]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[INT1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ADD]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[AMAX:.+]] = torch.aten.amax %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
   // CHECK: return %[[AMAX]]
@@ -793,9 +759,12 @@ func.func @test_reduce_max_attr(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtens
 
 // CHECK-LABEL: func.func @test_reduce_l1_default_axes_keepdims_example
 func.func @test_reduce_l1_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: %[[ABS:.+]] = torch.aten.abs %arg0 : !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
+  // CHECK: %[[ABS:.*]] = torch.aten.abs %arg0
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
-  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK-DAG: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK-DAG: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK-DAG: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE:.+]] = torch.constant.none
   // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[ABS]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
@@ -845,8 +814,11 @@ func.func @test_reduce_l1_do_not_keepdims_example(%arg0:!torch.vtensor<[3,2,2],f
 // CHECK-LABEL: func.func @test_reduce_l2_default_axes_keepdims_example
 func.func @test_reduce_l2_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: %[[INT0_0:.+]] = torch.constant.int 0
-  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE_0:.+]] = torch.constant.bool true
   // CHECK: %[[NONE_0:.+]] = torch.constant.none
   // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE_0]], %[[NONE_0]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
@@ -944,7 +916,10 @@ 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: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !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<[1,1,1],f32>
@@ -1000,7 +975,10 @@ func.func @test_reduce_log_sum_exp_default_axes_keepdims_example(%arg0: !torch.v
   // CHECK: %[[CAST:.+]] = torch.aten.to.dtype %arg0, %[[INT7]], %[[FALSE]], %[[FALSE]], %[[NONE_0]] : !torch.vtensor<[3,2,2],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[3,2,2],f64>
   // CHECK: %[[EXP:.+]] = torch.aten.exp %[[CAST]] : !torch.vtensor<[3,2,2],f64> -> !torch.vtensor<[3,2,2],f64>
   // CHECK: %[[INT0:.+]] = torch.constant.int 0
-  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE_1:.+]] = torch.constant.none
   // CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[EXP]], %[[DIMS]], %[[TRUE]], %[[NONE_1]] : !torch.vtensor<[3,2,2],f64>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f64>
@@ -1092,7 +1070,10 @@ func.func @test_reduce_log_sum_exp_keep_dims_int_input_example(%arg0: !torch.vte
 // 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
-  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[NONE:.+]] = torch.constant.none
   // CHECK: 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>
@@ -1177,7 +1158,10 @@ func.func @test_reduce_sum_negative_axes_keepdims_example(%arg0: !torch.vtensor<
 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: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT0_0]], %[[INT1]], %[[INT2]] : (!torch.int, !torch.int, !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],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
@@ -1385,17 +1369,8 @@ func.func @test_reduce_min_empty_set_int(%arg0: !torch.vtensor<[2,0,4],si32>, %a
 
 // 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: %[[IDX:.+]] = torch.constant.int 0
-  // CHECK: %[[SZ:.+]] = torch.constant.int 0
-  // CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
-  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %6 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[I1:.+]] = torch.constant.int 1
+  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[TRUE:.+]] = torch.constant.bool true
   // CHECK: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LST]], %[[TRUE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4,1],i1>
   // CHECK: return %[[AMIN]] : !torch.vtensor<[4,1],i1>
@@ -1407,17 +1382,8 @@ func.func @test_reduce_min_bool_inputs(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !
 
 // CHECK-LABEL: func.func @test_reduce_min_bool_inputs_nokeepdims
 func.func @test_reduce_min_bool_inputs_nokeepdims(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],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: %[[IDX:.+]] = torch.constant.int 0
-  // CHECK: %[[SZ:.+]] = torch.constant.int 0
-  // CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
-  // CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %6 : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[I1:.+]] = torch.constant.int 1
+  // CHECK: %[[LST:.+]] = torch.prim.ListConstruct %[[I1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
   // CHECK: return %[[AMIN]] : !torch.vtensor<[4],i1>
@@ -1431,17 +1397,7 @@ func.func @test_reduce_min_bool_inputs_nokeepdims(%arg0: !torch.vtensor<[4,2],i1
 func.func @test_reduce_min_all_dims_default(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[],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: %[[I0:.+]] = torch.constant.int 0
   // CHECK: %[[I1:.+]] = torch.constant.int 1
-  // CHECK: %[[RANK:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[C0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I0]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[A0:.+]] = torch.aten.add.int %[[I0]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I1]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[A1:.+]] = torch.aten.add.int %[[I1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[A0]], %[[A1]]
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[I0]], %[[I1]]
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[MIN:.+]] = torch.aten.amin %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[],i1>
   // CHECK: return %[[MIN]] : !torch.vtensor<[],i1>
@@ -1453,13 +1409,7 @@ func.func @test_reduce_min_all_dims_default(%arg0: !torch.vtensor<[4,2],i1>) ->
 
 func.func @test_reduce_min_attr(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[4],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: %[[INT1:.+]] = torch.constant.int 1
-  // CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
-  // CHECK: %[[INT0:.+]] = torch.constant.int 0
-  // CHECK: %[[LT:.+]] = torch.aten.lt.int %[[INT1]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
-  // CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
-  // CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[ADD:.+]] = torch.aten.add.int %[[INT1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
-  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ADD]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
   // CHECK: %[[FALSE:.+]] = torch.constant.bool false
   // CHECK: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
   // CHECK: return %[[AMIN]]