diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 5fb17c79a65b..944c258a8d12 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -180,53 +180,67 @@ LogicalResult reduceOpImpl(OpBinder binder, ConversionPatternRewriter &rewriter,
   return success();
 }
 
-Value getValueList(OpBinder binder, ConversionPatternRewriter &rewriter,
-                   Value operand) {
-  SmallVector<Value> itemList;
-  auto sizes = dyn_cast<Torch::ValueTensorType>(operand.getType()).getSizes();
-  Torch::BaseTensorType operandType =
-      cast<Torch::BaseTensorType>(operand.getType());
-
-  SmallVector<int64_t> selectSizes;
-  selectSizes.push_back(1);
-  Type selectResultType = operandType.getWithSizesAndDtype(
-      llvm::ArrayRef(selectSizes), operandType.getOptionalDtype());
-
-  auto extract = [&rewriter, &binder](Value x, Value v) {
-    auto xTy = cast<Torch::ValueTensorType>(x.getType());
-    Type extractTy = rewriter.getType<Torch::FloatType>();
-    if (isa<IntegerType>(xTy.getDtype()))
-      extractTy = rewriter.getType<Torch::IntType>();
-
-    return rewriter.create<Torch::AtenItemOp>(binder.getLoc(), extractTy, v);
-  };
-
-  Value zero = rewriter.create<Torch::ConstantIntOp>(
-      binder.getLoc(), rewriter.getType<Torch::IntType>(),
-      rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
-
-  MLIRContext *context = binder.op->getContext();
-  for (int i = 2; i < sizes[0]; i++) {
-    Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
-        binder.getLoc(), rewriter.getType<Torch::IntType>(),
-        rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
-    Value ext = rewriter.create<Torch::AtenSelectIntOp>(
-        binder.getLoc(), selectResultType, operand, zero, selectIndex);
-    Value item = extract(operand, ext);
-    itemList.push_back(item);
-  }
-  auto xTy = cast<Torch::ValueTensorType>(operand.getType());
-  Value ValueList;
-  if (isa<IntegerType>(xTy.getDtype())) {
-    ValueList = rewriter.create<Torch::PrimListConstructOp>(
-        binder.getLoc(), Torch::ListType::get(Torch::IntType::get(context)),
-        itemList);
-  } else {
-    ValueList = rewriter.create<Torch::PrimListConstructOp>(
-        binder.getLoc(), Torch::ListType::get(Torch::FloatType::get(context)),
-        itemList);
+Type getTorchScalarType(
+    /* forElementIn */ Torch::BaseTensorType givenTensorType,
+    /*        using */ ConversionPatternRewriter &rewriter) {
+  auto elementTypeForGivenTensor = givenTensorType.getDtype();
+
+  if (isa<IntegerType>(elementTypeForGivenTensor))
+    return rewriter.getType<Torch::IntType>();
+  if (isa<FloatType>(elementTypeForGivenTensor))
+    return rewriter.getType<Torch::FloatType>();
+
+  assert(false && "dtype for given tensor expected to be either int or float");
+}
+
+Value extractTorchScalar(
+    /*    at */ Location givenLoc,
+    /*  from */ int64_t givenIndex,
+    /*    in */ Value given1DTensor,
+    /* using */ ConversionPatternRewriter &rewriter) {
+  auto some1DTensorType = cast<Torch::BaseTensorType>(given1DTensor.getType());
+
+  Type selectionTypeForSome1DTensor = some1DTensorType.getWithSizesAndDtype(
+      ArrayRef<int64_t>{1}, some1DTensorType.getOptionalDtype());
+
+  Value frontDim = rewriter.create<Torch::ConstantIntOp>(givenLoc, 0);
+
+  Value selectionIndex =
+      rewriter.create<Torch::ConstantIntOp>(givenLoc, givenIndex);
+
+  auto someTorchScalarType = getTorchScalarType(some1DTensorType, rewriter);
+
+  Value selectionFromGiven1DTensor = rewriter.create<Torch::AtenSelectIntOp>(
+      givenLoc, selectionTypeForSome1DTensor, given1DTensor, frontDim,
+      selectionIndex);
+
+  return rewriter.create<Torch::AtenItemOp>(givenLoc, someTorchScalarType,
+                                            selectionFromGiven1DTensor);
+}
+
+Value createScalarSublist(
+    /*                    at */ Location givenLoc,
+    /* movingForwardsThrough */ Value given1DTensor,
+    /*            startingAt */ int64_t givenIndex,
+    /*                 using */ ConversionPatternRewriter &rewriter) {
+  auto some1DTensorType = cast<Torch::BaseTensorType>(given1DTensor.getType());
+  auto sizesOfSome1DTensor = some1DTensorType.getSizes();
+  auto lengthOfFullList = sizesOfSome1DTensor[0];
+
+  SmallVector<Value> runningScalarSublist;
+
+  for (int indexOfEachScalar = givenIndex; indexOfEachScalar < lengthOfFullList;
+       indexOfEachScalar++) {
+    Value eachScalar = extractTorchScalar(givenLoc, indexOfEachScalar,
+                                          given1DTensor, rewriter);
+    runningScalarSublist.push_back(eachScalar);
   }
-  return ValueList;
+
+  auto someTorchScalarType = runningScalarSublist.front().getType();
+  Type someTorchScalarListType = Torch::ListType::get(someTorchScalarType);
+
+  return rewriter.create<Torch::PrimListConstructOp>(
+      givenLoc, someTorchScalarListType, runningScalarSublist);
 }
 } // namespace
 
@@ -2809,14 +2823,21 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
           modeStrValue =
               rewriter.create<Torch::ConstantStrOp>(binder.getLoc(), modeStr);
         }
+
+        int64_t assumedForemostSpatialDim = 2;
+
         if (operands.size() < 4) {
           Value scaleOperand = operands[2];
-          scalesValueList = getValueList(binder, rewriter, scaleOperand);
+          scalesValueList =
+              createScalarSublist(binder.getLoc(), scaleOperand,
+                                  assumedForemostSpatialDim, rewriter);
           sizesValueList = noneVal;
         } else {
           Value sizeOperand = operands[3];
           scalesValueList = noneVal;
-          sizesValueList = getValueList(binder, rewriter, sizeOperand);
+          sizesValueList =
+              createScalarSublist(binder.getLoc(), sizeOperand,
+                                  assumedForemostSpatialDim, rewriter);
         }
         if (isa<Torch::NoneType>(scalesValueList.getType()) &&
             isa<Torch::NoneType>(sizesValueList.getType())) {
@@ -3339,7 +3360,9 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
           return rewriter.notifyMatchFailure(
               binder.op, "supports upto 3d upsampling only");
 
-        Value scalesValueList = getValueList(binder, rewriter, scales);
+        int64_t assumedForemostSpatialDim = 2;
+        Value scalesValueList = createScalarSublist(
+            binder.getLoc(), scales, assumedForemostSpatialDim, rewriter);
         if (mode == "linear") {
           if (resultRank == 4)
             mode = "bilinear";
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index 16c86218dbc8..5dd6ee037b75 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -2803,8 +2803,9 @@ func.func @test_upsample_nearest(%arg0: !torch.vtensor<[1,1,2,2],f32>, %arg1: !t
   // CHECK: %[[INT2:.*]] = torch.constant.int 2
   // CHECK: %[[SELECT:.*]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT2]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
   // CHECK: %[[SCALE:.*]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],f32> -> !torch.float
+  // CHECK: %[[INT0_0:.*]] = torch.constant.int 0
   // CHECK: %[[INT3:.*]] = torch.constant.int 3
-  // CHECK: %[[SELECT_0:.*]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT3]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
+  // CHECK: %[[SELECT_0:.*]] = torch.aten.select.int %arg1, %[[INT0_0]], %[[INT3]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
   // CHECK: %[[SCALE_0:.*]] = torch.aten.item %[[SELECT_0]] : !torch.vtensor<[1],f32> -> !torch.float
   // CHECK: %[[SCALE_LIST:.*]] = torch.prim.ListConstruct %[[SCALE]], %[[SCALE_0]] : (!torch.float, !torch.float) -> !torch.list<float>
   // CHECK: %[[MODE:.*]] = torch.constant.str "nearest"
@@ -2824,8 +2825,9 @@ func.func @test_upsample_bilinear(%arg0: !torch.vtensor<[1,1,2,2],f32>, %arg1: !
   // CHECK: %[[INT2:.*]] = torch.constant.int 2
   // CHECK: %[[SELECT:.*]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT2]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
   // CHECK: %[[SCALE:.*]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],f32> -> !torch.float
+  // CHECK: %[[INT0_0:.*]] = torch.constant.int 0
   // CHECK: %[[INT3:.*]] = torch.constant.int 3
-  // CHECK: %[[SELECT_0:.*]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT3]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
+  // CHECK: %[[SELECT_0:.*]] = torch.aten.select.int %arg1, %[[INT0_0]], %[[INT3]] : !torch.vtensor<[4],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
   // CHECK: %[[SCALE_0:.*]] = torch.aten.item %[[SELECT_0]] : !torch.vtensor<[1],f32> -> !torch.float
   // CHECK: %[[SCALE_LIST:.*]] = torch.prim.ListConstruct %[[SCALE]], %[[SCALE_0]] : (!torch.float, !torch.float) -> !torch.list<float>
   // CHECK: %[[MODE:.*]] = torch.constant.str "bilinear"